Treamnt of Endodontically Treated Tooth

Home , Dental restoration

DECISION SUPPORT AND TRAINING SYSTEM FOR MANAGEMENT OF ENDODONTICALLY TREATED TEETH

Anjali B Shah

Dissertation Submitted to

Rutgers University

School of Health Related Professions

In Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

In Biomedical Informatics

Department of Health Informatics

October 2014

ABSTRACT

Endodontically treated teeth are often structurally compromised and require proper prosthodontic restoration following endodontic therapy to ensure long-term success. As a result and as the number of endodontic procedures in contemporary dentistry have steadily increased in the past decade, tooth restoration is becoming an integral part of restorative practice in everyday dentistry. Although the subject has been widely researched and published in dental literature, the topic of best way to restore root canal treated teeth still remains an area prone to high error rates in decision-making. Complexity of the domain is largely due to multi-factorial considerations and need for evaluation of a wide range of restorative techniques of varying intricacies. Determining the best restorative treatment plan following endodontic therapy requires sound knowledge of principles that span across multiple dental disciplines of endodontics, orthodontics, periodontics, and prosthodontics. Memorization of a large and ever- increasing number of decision rules to be meaningfully used at the point-of-care can be arduous, especially for dental students and less experienced clinicians. Clinical experts with their knowledge and years of experience treating patients can help in the process, but may not always be around to provide assistance. Misdiagnosis and mistreatment of such teeth can lead to adverse clinical consequences and significant inconvenience to the patient as well as financial implications both for the patient and provider. To address this problem, we have developed a clinical decision support and training system based on expert knowledge and evidence-based guidelines. Using Corvid expert system development framework and careful consideration of factors necessary for success of clinical decision support systems, we have developed a working prototype of a web-based, interactive system that can be launched at the operatory and can be easily integrated into providers’ workflow. One of the important goals of the system is to train users to think holistically like an expert while problem-solving and planning restorative

iii treatment. Based on information entered by the user, system comes up with recommendations, alerts and treatment prognosis. Treatment options provided with justifying explanations can help clinicians provide decision-making rationale to the patients, better inform and educate them, and thus better manage their expectations and ensure their satisfaction and compliance.

Since the knowledge base of our system is developed using expert guidelines that are known to be effective, we expect to see improved restorative treatment outcomes for endodontically treated teeth.

ACKNOWLEDGEMENTS

First and foremost, I would like to express my gratitude to Dr. Louis DiPede from the Rutgers

School of Dental Medicine for taking the time to serve as my thesis advisor, for original concept and ideas, helpful advice and for helping me gain practical insight into the field of restorative dentistry. I am indebted to Dr. Shankar Srinivasan for his kind and ever encouraging words, encyclopedic knowledge, fresh perspectives and countless useful conversations that have made a very positive and profound impact on my work. I can never thank enough my committee chair,

Dr. Dinesh Mital, for his valuable guidance and insight throughout my graduate career. His influence has tremendously contributed to my development as a researcher.

My sincere appreciation to Dr. Syed Haque, and Dr. Masayuki Shibata who have always been very supportive and encouraging and have helped me enlarge my sphere of understanding of

Biomedical Informatics.

Needless to say, my family truly deserves all the credit for what I am and where I am today. I thank my mother Dr. Bharati Shah, my father Dr. Bharat Shah, my father-in-law Mr. Balwant

Nirmal, and my child’s baby-sitter Mrs. Halima Ali for all their love and support.

I offer my greatest gratitude to my husband Suresh and my daughter Ariana, whose love, faith, and understanding carry me through life.

TABLE OF CONTENTS

ABSTRACT ...... iii ACKNOWLEDGEMENTS ...... v Chapter 1 Introduction ...... 1 1.1 Introduction ...... 1 1.2 Background and Statement of the Problem ...... 3 1.3 Significance of the Study ...... 10 1.4 Goals & Objectives of the Study ...... 13 1.5 Hypotheses ...... 15 Chapter 2 Literature Review ...... 17 2.1 Factors and Challenges in Restoration of Endodontically Treated Teeth ...... 18 2.1.1 Fracture Resistance of Endodontically Treated Teeth ...... 18 2.1.2 Significance of Microleakage & Coronal Barrier ...... 19 2.1.3 Significance of Full Cuspal Coverage ...... 20 2.1.4 Caries management ...... 21 2.1.5 Significance of Remaining Coronal Tooth Structure ...... 22 2.1.6 Significance of Biologic Width ...... 24 2.1.7 Significance of Ferrule Effect ...... 26 2.1.8 Significance of Occlusal Forces ...... 28 2.1.9 Principles in the Use of Post and Core System ...... 30 2.1.9.1 Need for Posts in Endodontically Treated Teeth ...... 30 2.1.9.2 Type of Posts and Post Design ...... 32 2.1.9.3 Post Space Preparation Considerations ...... 34 2.1.9.4 Core Materials ...... 36 2.1.9.5 Final Restoration ...... 38 2.2 Review of Clinical Decision Support Systems...... 39 2.2.1 Definition and Concept of Computer-based Clinical Decision Support Systems ...... 39 2.2.2 Historical Perspective ...... 40 2.2.2.1 Clinical Decision Support Systems in Medical Field ...... 40 2.2.2.2 Clinical Decision Support Systems in Dentistry ...... 46 2.2.3 Characteristics and Types of Clinical Decision Support Systems ...... 51

2.2.4 Clinical Decision Support Systems in Restorative Dentistry ...... 62 2.3 Importance and Effectiveness of Technology in Dental Education ...... 65 Chapter 3 System Design and Implementation ...... 68 3.1 System Architecture ...... 69 3.2 Rationale for Choosing Exsys Corvid ...... 80 3.3 User Interaction Process Flow ...... 81 3.4 System Design and Implementation ...... 84 3.4.1 Logical System Flow ...... 84 3.4.2 Rule-based Knowledge Representation ...... 94 Chapter 4 Results and Case Studies ...... 104 4.1 Preliminary System Testing ...... 104 4.2 System Validation Using Patient Radiographs ...... 133 Chapter 5 Discussion & Conclusions ...... 136 5.1 Discussion...... 151 5.2 Conclusions ...... 154 5.3 Future Direction ...... 155 References ...... 157

vii

LIST OF TABLES

Table 2-1. Four-phased architectural model of CDSS evolution with notable examples140...... 46

Table 2-2 Listing of CDS applications142,148 ordered chronologically and classified by knowledge representation scheme...... 48

Table 2-3. Seven sub-groups of dentistry for classification of reviewed clinical decision support systems as suggested by White160...... 49

Table 3-1. Key considerations for determining optimal treatment recommendation...... 95

Table 3-2. Table indicating how rules affect restoration prognosis score (initialized to 100). ... 102

Table 4-1. Summary of case description and screenshots – case example 1...... 104

Table 4-2. Summary of case description and screenshots – case example 2...... 108

Table 4-3. Summary of case description and screenshots – case example 3...... 109

Table 4-4. Summary of case description and screenshots – case example 4...... 112

Table 4-5. Summary of case description and screenshots – case example 5...... 115

Table 4-6. Summary of case description and screenshots – case example 6...... 116

Table 4-7. Summary of case description and screenshots – case example 7...... 121

Table 4-8. Summary of case description and screenshots – case example 8...... 124

Table 4-9. Summary of case description and screenshots – case example 9...... 127

Table 4-10. Summary of case description and screenshots – case example 10...... 130

Table 4-11. Comparison of system generated treatment plan with expert opinion – case scenario 1...... 134

Table 4-12. Comparison of system generated treatment plan with expert opinion – case scenario 2...... 135

Table 4-13. Comparison of system generated treatment plan with expert opinion – case scenario 3...... 136

Table 4-14. Comparison of system generated treatment plan with expert opinion – case scenario 4...... 137

Table 4-15. Comparison of system generated treatment plan with expert opinion – case scenario 5...... 138

viii

Table 4-16. Comparison of system generated treatment plan with expert opinion – case scenario 6...... 139

Table 4-17. Comparison of system generated treatment plan with expert opinion – case scenario 7...... 140

Table 4-18. Comparison of system generated treatment plan with expert opinion – case scenario 8...... 141

Table 4-19. Comparison of system generated treatment plan with expert opinion – case scenario 9...... 142

Table 4-20. Comparison of system generated treatment plan with expert opinion – case scenario 10...... 144

Table 4-21. Comparison of system generated treatment plan with expert opinion – case scenario 11...... 145

Table 4-22. Comparison of system generated treatment plan with expert opinion – case scenario 12...... 146

Table 4-23. Comparison of system generated treatment plan with expert opinion – case scenario 13...... 147

Table 4-24. Comparison of system generated treatment plan with expert opinion – case scenario 14...... 148

Table 4-25. Comparison of system generated treatment plan with expert opinion – case scenario 15...... 149

Table 4-26. Summary of agreement between system-generated treatment plan and expert opinion on restoration for the endodontically treated tooth in the 15 case scenarios used for system validation ...... 149

LIST OF FIGURES

Figure 1-1. Treatment plan variations based on tooth type. Intra-coronal restoration may be sufficient in case of anterior tooth with endodontic access opening. However, intra- and extra- coronal restorations are necessary to restore posterior tooth for good long-term prognosis. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 5

Figure 1-2. Treatment plan variations based on ferrule effect. Tooth that is second from left has significant loss and inadequate ferrule effect. Tooth that is second from right has some loss, but approximately 2 mm ferrule on both sides. Restoration prognosis is better with greater ferrule effect. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 6

Figure 1-3. Treatment plan variations based on caries risk level. Caries excavation on tooth on far left is necessary for accurate assessment of caries risk level and formulating a meaningful restorative treatment plan. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 7

Figure 1-4. Core and crown finish line are at the same level indicating no ferrule effect. Treatment plan must take the finding into account. Crown Lengthening or Orthodontic Eruption should be incorporated into the plan to achieve desirable ferrule effect. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 7

Figure 1-5. Treatment plan variations based on dentin wall thickness. All, three or two walls in opposing configuration of at least 1 mm thickness are considered good for restoration prognosis. Insufficient dentin wall thickness, two remaining walls in ‘L’ configuration or one remaining dentin wall are not considered good for long-term prognosis and must be carefully evaluated as part of the planning process. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 8

Figure 1-6. Post is often necessary in restorative dentistry to retain core material where remaining coronal tooth structure is not adequate. Images on left and right depict teeth with crown-to-post ratio of 1:1.5 and 1:2 respectively. Crown-to-post ratio less than 1:1 is not considered good for long-term prognosis and when ratio is 1:1, several other factors need to be carefully evaluated. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 9

Figure 2-1.Teeth and bone are dense, absorb x-rays and appear dark on radiographic images. Similarly, restorations and fillings are denser than bone and appear as solid, white sections. Dental decay and caries appear as dark spots on the image. Photo courtesy of Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey...... 22

Figure 2-2. Figure on the left shows correct restoration placement margin leaving biologic width undamaged. Figure on the right shows incorrectly placed restoration with the margin violating the biologic width of the tooth...... 25

Figure 2-3. Illustration of the concept of ferrule effect. Figure on the left depicts restored tooth with adequate ferrule and one on the right depicts restored tooth without any ferrule...... 27

Figure 3-1. Block diagram of CDSS components for restoration of endodontically treated teeth. High level system architecture that depicts logical tiers and main working components of the system...... 70

Figure 3-2. Snapshot of Logic block to determine if tooth selected by user is anterior or posterior type. Snapshot also highlights tooth_type and tooth_site variables used to define the if/then rule in the Logic block...... 76

Figure 3-3. Snapshot of Command block to derive information regarding tooth’s existing restoration status. Inference engine will run in backward chaining mode to derive this information from rules in the system if possible or pose it as a question to the user...... 77

Figure 3-4. Illustrative entity-relationship diagram describing information aspects of clinical decision support system...... 79

Figure 3-5. Swim lane flowchart of user interaction with clinical decision support system. Dental professional enters patient data into the system, answers a series of questions to determine restorability. System suggests appropriate options if tooth is deemed restorable...... 83

Figure 3-6. Logical process flow diagram representing distinct phases the CDSS steps through while processing rules to build most optimal treatment plan...... 85

Figure 3-7. Screenshots of Command and Logic blocks containing rules pertaining to the data collection phase...... 86

Figure 3-8. Screenshot of Logic block containing rules pertaining to primary evaluation sub- process within treatment planning phase...... 88

Figure 3-9. Screenshot of Logic block containing rules pertaining to secondary evaluation sub- process within treatment planning phase...... 89

Figure 3-10. Screenshot of Logic block containing rules pertaining to secondary evaluation sub- process within treatment planning phase...... 90

Figure 3-11. Screenshot of Logic block containing rules pertaining to gray area evaluation sub- process within treatment planning phase...... 91

Figure 3-12. Screenshot of Logic block containing rules pertaining to shared decision-making phase...... 93

Figure 3-13. Screenshot of Command block containing rules to save system generated treatment plan to the database for future references...... 94

Figure 3-14-A. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated anterior teeth...... 97

Figure 3-14-B. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated anterior teeth...... 98

Figure 3-15-A. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated posterior teeth...... 99

Figure 3-15-B. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated posterior teeth...... 100

Figure 3-16. Decision tree depicting gray area logic for management of endodontically treated teeth when crown-to-root ratio or crown-to-post ratio is 1:1...... 101

Figure 3-17. Decision tree depicting shared decision-making logic for management of endodontically treated teeth when restoration prognosis after primary, secondary and gray area evaluation is guarded...... 103

xii

Chapter 1 Introduction

1.1 Introduction

Studies involving restoration of endodontically treated teeth have been widely published in dental literature. Number of endodontic procedures in contemporary dentistry has increased steadily in the past decade. Long-term success of endodontic treatment depends significantly on proper prosthodontic reconstruction following endodontic therapy. Therefore, tooth restoration is becoming an integral part of restorative practice in everyday dental practice to successfully treat damaged tooth with pulpal disease. Despite improved knowledge through in-vitro and in- vivo investigations over last few decades, the topic of best way to restore teeth after root canal treatment remains complex, subject to opinions and controversial to this date.1 Treatment process involves multi-factorial considerations and evaluation of wide range of restorative techniques of varying complexity. Determining best restorative plan following endodontic therapy requires good understanding of tooth anatomy, physical and biomechanical properties, and a sound knowledge of endodontic, periodontal, restorative, and occlusal principles.2

Different factors that influence treatment prognosis include: tooth position in dental arch, tooth type, number of adjacent teeth, apical status, occlusal contacts, parafunctional habits, amount of hard tissue loss, remaining dentin wall thickness, biologic width, type of coronal restoration, abutment for removable partial dentures (RPDs) or fixed partial dentures (FPDs), type of post or core if necessary, and presence of adequate ferrule effect.3

With so many interdependent factors at play, decision-making for restorative dentists is particularly challenging. It may seem logical to think of root canal failures to have endodontic origin. However, studies have shown otherwise with majority of failures of endodontically

1 treated teeth to have restorative origin as the primary reason of failure.4 5,6 Misdiagnosis and mistreatment by restorative dentists can lead to adverse clinical as well as non-clinical consequences. Pain, discomfort, tooth fracture, loss of natural dentition accompanied with loss of function, damage to periodontal ligament and loss of proprioception, contamination of the canal and colonization of bacterial species at the walls of apical portion of root canal, plaque accumulation in ditches contributing to bad taste and halitosis, inflammation of gums, tooth decay with loss of entire bridge in case of abutment tooth, and dental trauma are some of the examples of clinical consequences of failing or failed tooth restorations.4 Loss of time and money are amongst non-clinical consequences causing considerable inconvenience to patients.

Unpleasant patient-dentist differences on treatments and their costs lead to erosion of patient trust and in many cases formal patient complaints to dental practice boards and legal proceedings.7

We propose an expert system that will help train inexperienced restorative dentists and dental students about the art of decision-making based on scientific principles. Such a system will go a long way in addressing problems described above. Determining treatment plan for restoring endodontically treated teeth can be a complex decision-making process as stated that must leverage the expertise and guidance of an expert clinician. Since expert clinicians cannot always be on floor to assist students and inexperienced restorative dentists, we propose developing a decision-support and training system that captures expert knowledge and disseminates pertinent information through an interactive, user-friendly graphical user interface on demand.

Since diagnostic errors and poor decision-making lead to poor prognosis and treatment outcomes, training system based on expert clinical guidelines will significantly improve patient care. Students and novice clinicians will learn about treatment planning a patient case before starting any treatment procedure on the patient. Treatment planning entire patient case

2 requires taking into account holistic approach towards problem-solving that cannot be gained by focusing on individual restorative procedures. Holistic approach to problem-solving in turn leads to better decision-making geared towards good long-term prognosis and treatment outcomes.

Training decision support system that we propose will pose relevant questions to end user to collect patient data and findings in light of factors described necessary to treatment plan restoration of endodontically treated tooth. It will train users to think like an expert would think through the decision-making process. Based on information entered by the user, system will come up with recommendations, alerts and treatment prognosis. System will also query users to take patient preferences into account and where recommended treatment options do not match patient preferences, it will suggest other viable treatment alternatives. This will help users manage patient expectations while keeping patients informed about treatment options and associated prognoses.

1.2 Background and Statement of the Problem

American Dental Association (ADA)’s Code on Dental Procedures and Nomenclature (CDT)8 defines treatment planning as “The sequential guide for patient’s care as determined by dentist’s diagnosis and is used by the dentist for the restoration to and/or maintenance of optimal oral health.” A good manageable treatment plan requires diligent execution of following steps: relevant data collection by means of case history and examination, identification of problems list, establishing diagnosis that may need special tests and/or consultations, evaluating possible treatment options that meet any emergency/immediate as well as comprehensive/long-term treatment needs, and development of formal treatment plan in shared decision-making with patient.

Hook, Comer, Trombly, Guinn, and Shrout9 discuss rationale and significance of treatment planning process as a critical aspect of clinical education in dental school curriculum. It is important to educate students that while treating at the individual tooth level, they must formulate comprehensive treatment plan involving decisions about treating teeth in the context of the rest of the dentition, and about managing the rest of the dentition in the context of the masticatory function and the individual. Such holistic view of the patient as a person with specific needs and preferences ensures patient compliance, satisfaction and expected treatment outcomes in the near and long-term future.7 It lays the foundation for successful, patient- oriented planning and diagnosis rather than considering patient as just “another case to be treated” in a set manner.10 It is also important to educate students about prevention-oriented diagnosis and planning where focus is not just on fixing the problem, but understanding the underlying causes of the problem manifestation and addressing those. For example, it makes little sense to remove and restore carious lesions without addressing the patient's oral hygiene and diet.

Determining treatment plan for restoration of endodontically treated teeth requires careful evaluation of several factors individually as well as in correlation with other factors. Tooth should be assessed for prognosis after restoration keeping in mind factors such as occlusal function, periodontal health, and aspects such as biological width, ferrule effect, and crown-to- root ratio. If satisfactory, tooth must be evaluated in context of rest of natural dentition to come up with most meaningful and comprehensive oral rehabilitation treatment plan.11 Figure 1-1 shows difference in treatment plan based on tooth type. Image on the left is that of an anterior tooth with endodontic access opening and the one on the right is that of a posterior tooth with similar access opening. Whereas intra-coronal restoration is sufficient in case of anterior tooth,

4 intra- and extra-coronal restorations are necessary to restore posterior tooth for good long-term prognosis.

Figure 1-1. Treatment plan variations based on tooth type. Intra-coronal restoration may be sufficient in case of anterior tooth with endodontic access opening. However, intra- and extra-coronal restorations are necessary to restore posterior tooth for good long-term prognosis. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey.

Figure 1-2 shows difference in treatment plan based on presence of adequate ferrule effect. A minimum of 2 mm ferrule has been widely acknowledged as necessary for good restoration prognosis.12-15 Figure depicts endodontically treated teeth with varying degrees of ferrule. Tooth that is second from left has significant loss, whereas tooth that is second from right has suffered some loss, but has approximately 2 mm ferrule effect on both sides. More the ferrule better is the restorative treatment prognosis. In cases where there isn’t enough ferrule effect, crown lengthening or orthodontic eruption could be planned to improve ferrule width and restore the tooth if other presenting factors project positive outcome.

Patients can be classified into low-, medium-, and high-risk categories based on their risk of developing caries.16 Treatment plan must be tailored for each of the three risk levels, as management greatly differs at each level. Radiograph in figure 1-3 shows tooth on the far left with caries. Real damage is typically 30 - 60% more than that depicted in the radiograph.

Excavation of caries must be done before proceeding with any treatment planning. This helps understand true extent of tooth damage and amount of restoration necessary. Benn17 discusses importance of accurate assessment of caries risk level, patient’s age, date of last examination, and the importance and difficulty of producing treatment plans according to different caries risk level.

Figure 1-4 illustrates scenario where tooth has no ferrule. Core finish line and crown finish line are at the same level. In order to hold the root stump, it is necessary to have some overlap between core and crown finish line. Where such an overlap is missing as shown in figure1- 4, restoration prognosis is not considered good. Crown lengthening or orthodontic eruption is highly recommended to achieve the minimum necessary ferrule effect before considering tooth for any restorative treatment.

Remaining dentin wall thickness is an important parameter that influences restoration prognosis of endodontically treated tooth.3 Image on the left in figure 1-5 has tooth with three remaining dentin walls approximately 1 mm in thickness. This presenting configuration is considered to have good restoration prognosis. Image on the right has tooth with all intact dentin walls of 1 mm thickness and excellent restoration prognosis. In both these scenarios, post is not necessary to retain intra-coronal restoration. When there are two remaining dentin walls in opposing configuration, it’s generally considered acceptable tooth condition for restoration. However, scenarios with two remaining dentin walls in ‘L’ configuration or just one remaining dentin wall, restoration prognosis is considered to be bad.

Figure 1-5. Treatment plan variations based on dentin wall thickness. All, three or two walls in opposing configuration of at least 1 mm thickness are considered good for restoration prognosis. Insufficient dentin wall thickness, two remaining walls in ‘L’ configuration or one remaining dentin wall are not considered good for long- term prognosis and must be carefully evaluated as part of the planning process. Photo courtesy of Dr. Louis DiPede, Rutgers School of Dental Medicine, New Jersey.

Posts should be used in restoration of endodontically treated tooth for retention of core material in cases where remaining coronal tooth structure is not adequate, i.e., one or no cavity walls. Posts should also be used when tooth is used as abutment for removable partial denture.18 When post is necessary, careful consideration of expected crown-to-post ratio is necessary. Figure 1-6 shows images with acceptable crown-to-post ratio. Similar to crown-to- root ratio, crown-to-post ratio of 1:2 or 1:1.5 is desirable for good long-term prognosis. If this ratio is less than 1:1, tooth is generally not considered good candidate for crown restoration.

When the ratio is 1:1, other factors such as occlusal load, abutment for fixed or removable partial denture, patient’s bruxing or clenching tendency, and caries risk level must be considered to determine restorability.

With many different factors presenting themselves in varying combinations, decision tree with all the rules involved in restorative decision-making process gets intricate. It can be arduous for clinicians and students to recollect and apply all the required rules at point of care. This is where true value of clinical decision support systems (CDSS) is realized. Any number of rules relevant to the problem domain can be obtained from the domain expert and through evidence-based guidelines. This can be transcribed into system executable format by the knowledge engineer to build knowledge base of CDSS. Inference engine of the CDSS reasons with patient data entered by clinical users using rules in the knowledge base to formulate options for treatment recommendations. There have been other studies leveraging benefits of CDSS in the field of restorative dentistry. 19,20 However, these have not focused on training and treatment planning in the complex area of restoration of endodontically treated teeth. In this study, we propose

9 developing a CDSS for restoration of endodontically treated teeth using Corvid21 expert system development tool. Clinical users will be provided with data and knowledge through treatment recommendations and alerts. Although recommendations will be made, final decision will always rest with the clinician who can work with the patient in formulating an acceptable treatment plan. Proposed system will assist them in evaluating possible treatment options and in providing rationale to the patient.

A nationwide study of electronic curriculum implementation at North American dental schools cited lack of appropriate educational software as one of the prominent barriers in the adoption of E-curriculum capacities in day-to-day learning. 22 Rule-based CDSS we propose will serve as an example of educational software that can be incorporated into curriculum at dental schools. Our proposed training system focuses on building students’ expertise in treatment planning restoration of endodontically treated teeth. Because of the complexity involved in successfully treatment planning restoration of endodontically treated teeth and catastrophic outcomes of poor decision-making, our system prototype focuses on this aspect of dentistry. With relevant data and rules, system knowledge base could be extended to process decision-making for other areas of dentistry as future direction of research and development.

1.3 Significance of the Study

CDSSs have shown promise in reducing medical errors and improving patient care. However, studies have shown that not all CDSSs have been successful in this endeavor. In a systematic review of computer based systems, 66% significantly improved clinical practice, but 34% did not.23 Kawamoto, Houlihan, Balas, and Lobach24 conducted a systematic review of trials to identify factors that most contributed to success of CDSSs. They identified following features common to CDSSs that improved clinical practice: (1) decision support delivery integrated with

10 clinical workflow, (2) decision support delivered at point-of-care, (3) practical, executable treatment recommendations, and (4) computerized systems.

Our proposed CDSS is web-based and can be launched at operatory for soliciting system-based recommendations. Clinicians do not have to go out of their way to get recommendations. Tool can be easily incorporated as part of the providers’ workflow as they examine patient and can be used for shared decision-making between patient and provider. Treatment plan allows provider to present rationale to the patient before performing the procedure and obtain patient buy-in. It helps in educating patient about the preferred treatment option and its expected outcome. If there are differences between provider’s proposed treatment plan and patient preferences, provider can make timely changes to the treatment plan so long as patient is made aware of the treatment prognosis of altered plan. This helps ensure patient satisfaction, compliance, and improves treatment outcomes.

In a classroom scenario, proposed CDSS can be incorporated as part of class curriculum. Design of the system allows it to be launched from any web-interface, for example, web browsers like

MS Internet Explorer, smartphones or tablets. Ubiquity of these channels allows the system to be used anywhere, especially in classroom teaching where digital modes of teaching are becoming very common. It can be used in complement with traditional teaching methods to help students understand intricacies of the decision-making process. Access to the tool can also be provided outside of classroom through student login accounts enabling them to learn at their own pace. There can be very drastic consequences of making incorrect decisions in restorative dentistry. Through this CDSS, knowledge and experience of an expert in restorative dentistry can be captured and delivered to the students and inexperienced clinicians at the right time at point-of-care to minimize decision-making errors and improve treatment outcomes.

Recommendations generated by our proposed CDSS are based on many years of experience of expert dentists and evidence-based guidelines. These recommendations have already been followed by experts and have proven effective and have now been codified and entered into the system. They are actionable because the delivery mechanism (output of the system) is very clear and easy to understand. However, recommendations rendered by the CDSS are not meant to supersede or supplant clinician’s diagnosis, but only meant to act as an adjunct in an advisory capacity. It is ultimately up to the clinician to accept, reject, or modify the information as deemed necessary considering case-specific variables. Passive mode of delivering treatment plan aids, but does not interfere with clinician’s decision-making. Such tool, that captures the knowledge and experience of an expert can be of indispensable help in educating students and inexperienced faculty to think like an expert. System’s role as a consultant can prove useful not only in the evaluation of difficult patient cases, but also as an aid to geographically isolated dentists who do not have easy access to knowledgeable experts in the field for case-specific discussions.

Our proposed CDSS is a computer-based system that can evaluate several considerations that are needed to make the right decision. It ensures that all the necessary criteria are evaluated before a treatment plan is recommended. Students and inexperienced clinicians might have mastered the skill of performing individual procedure, but do not always have the insight of an experienced clinician to think of the problem holistically. Taking into consideration all relevant patient findings and coming up with a treatment plan that addresses immediate needs and promises good long-term treatment prognosis only comes with experience. Focus of our proposed CDSS design is to leverage this experience that helps reduce the rate of decision- making errors in an inherently complex restorative treatment planning process.

Computer generated treatment plan can be stored in the patient record for audit as well as legal purposes. Compiled plan signed by the patient also serves as legal evidence of professional competence and patient understanding and acceptance. This helps explain and justify clinician’s decision in solving patient case and helps records patient’s understanding and acceptance of the decision and its implications. This allows for a more transparent patient care process.

1.4 Goals & Objectives of the Study

This study is based on achieving 5 goals that are described in detail below:

1. Develop CDSS to satisfy an unmet need: As discussed earlier in this chapter, currently

there exists gap in the domain of decision support system for restoration of

endodontically treated teeth. CDSSs in the area of restorative dentistry have been

developed, but these do not specifically focus on restoring root canal treated teeth.

Complexity of this problem domain and multifactorial considerations make decision-

making particularly challenging for clinicians. In addition, identifying a sub-optimal

treatment plan can have serious implications for the patient, both from a financial

perspective and in terms of inconsiderable inconvenience caused to the patient. Our

primary objective is to therefore address this unmet need and build a system based on

expert and evidence-based guidelines to support decision-making in the area of

restorative treatment planning.

2. Training system for students and inexperienced faculty members: Our goal is to

develop a system that serves as a training platform for students and novice clinicians to

treatment plan restoration of root canal treated teeth. Our clinical users will be trained

to think like an expert would think through the decision-making process. CDSS will take

into account patient findings and preferences entered by clinical users and come up

with relevant recommendations, alerts and treatment prognosis. This will help them

provide rationale to the patient about treatment options and manage their expectations

better.

3. Based on expert knowledge as well as evidence-based guidelines: Our objective is to

leverage Corvid Expert System development framework to develop a rule-based clinical

decision support system. Knowledge base of the system will be developed using rules

obtained from practicing experts in the field of restorative dentistry as well as evidence-

based guidelines. A combination of forward and backward chaining algorithms will be

implemented to reason with patient data and preferences to drive inferences based on

rules in the knowledge-base. Inferences will be delivered in the form of actionable

treatment recommendations, alerts and prognosis information. This will help reduce the

rate of decision-making errors in an inherently complex restorative treatment planning

process.

4. Satisfy key principles of a successful clinical decision support system: In the design of

our expert system, we have followed the principles of design identified as critical for

success.24 These include: (1) decision support delivery integrated with clinical workflow,

(2) decision support delivered at point-of-care, (3) practical, executable treatment

recommendations, and (4) computerized systems. Satisfying these principles will ensure

the usability of the system and ensure adoption.

5. Scalability: Our goal is to design the system such that it can be scaled by adding new

rules to the knowledge base. Scalability is critical for two reasons. As new findings

emerge with further research and development in the field, this new found knowledge

must be incorporated into the rule base. Leveraging Corvid framework helps in this

regard wherein new variables, rules and logic blocks can be added to the system at any

point. These will be inferenced by the engine in conjunction with older rules as the

context arises. Scalability also helps ensure possibility of expanding applications of the

system to other complex areas of dentistry.

1.5 Hypotheses

To address challenges and gaps described in the previous sections, we propose the development of a clinical decision support and training system for optimal management of endodontically treated teeth. Our hypotheses in the design and development of this system are as follows:

1. It is possible to design a system which will serve as a decision-support aid in the complex

area of restoration of structurally-compromised, root-canal treated teeth

2. It is possible to design a CDSS for training and educating dental students as well as

inexperienced clinicians in the field of restorative dentistry

3. It is possible to derive rules of the CDSS knowledge base from expert clinicians and

evidence-based guidelines

4. With the use of the proposed CDSS, it is possible to reduce the rate of decision-making

errors in an inherently complex restorative treatment planning process

5. It is possible to leverage Corvid Expert System development framework to build a

scalable knowledge base for the restorative CDSS that is capable of incorporating new

found knowledge as it emerges with further research and development in the field of

restorative dentistry

System will be designed to include treatment recommendations that may include use of post and core based on patient findings. It is not within the scope of this study to make recommendations for use of any specific post system or core material. However, knowledge

15 base can be expanded in the future to include rules for specific post systems or core materials and recommendations can be made based on these rules in presenting case scenarios.

Chapter 2 Literature Review

Endodontic therapy is a common dental procedure in contemporary dentistry, but requires an optimal restorative plan after root canal has been treated to ensure long-term success.

Restoration of endodontically treated teeth is a subject that has been widely studied in dental literature. Numerous in-vitro and in-vivo investigations have greatly improved our understanding of this complex subject in the last few decades. However, diversity of published opinions has added to the complexity and confusion surrounding this topic and many practical questions concerning choice of optimal treatment plan still persist. Our study aims to review literature and seek proven best practices regarding treatment plan for successful restoration. By reviewing pertinent literature, our goal is to develop a clinical decision support and training system that relies on evidence-based expert opinions and published guidelines that are sound from restorative as well as endodontic perspective. We compartmentalized the literature review process into three distinct areas: (1) review of factors and challenges in restoring endodontically treated teeth, (2) history and evolution of CDSSs in medicine and dentistry, and (3) importance and effectiveness of technology in dental education. Clinical performance of restored endodontically treated tooth is affected by several factors including fracture resistance of endodontically treated tooth, coronal leakage and coronal barrier, caries management, full cuspal coverage, remaining coronal tooth structure, biologic width, dentin thickness, ferrule effect, location of tooth in the dental arch, number of adjacent teeth, occlusal contacts, functional occlusal load, type post system (if necessary) and core material. We discuss these factors in further detail in the following sections.

2.1 Factors and Challenges in Restoration of Endodontically Treated Teeth

2.1.1 Fracture Resistance of Endodontically Treated Teeth

Endodontically treated teeth are unique subset of teeth requiring careful consideration during treatment planning due to several mitigating factors to ensure successful restoration. One factor widely believed to cause brittleness in endodontically treated teeth is dentin of such teeth as compared to vital dentin. Some studies in the past have indicated that endodontic treatment causes degradation of physical and mechanical properties of dentin.25,26 However, Sedgley and

Messer27 disputed this finding. They compared biomechanical properties (punch shear strength, toughness, hardness, and load to fracture) of 23 endodontically treated teeth with mean time since treatment of 10 years to their contralateral vital pairs and did not report any significant differences. Similarly, Huang, Schilder, and Nathanson28 reported no significant difference in compressive and tensile strengths of dentin in root canal treated teeth as compared to normal dentin. Schwartz and Robbins29 supported the conclusion that dentin after endodontic treatment is not substantially different from vital dentin. Reeh, Messer and Douglas30 indicated that the factor leading to higher susceptibility of fractures in root canal treated teeth compared with vital teeth is the loss of structural integrity due to endodontic access preparation rather than changes in dentin. Pantvisai and Messer31 conducted an in vitro study and reported that mandibular molars with endodontic access preparations exhibited increased cuspal flexure during function than simple two-cavity (MO) and three-cavity (MOD) preparations. This increased possibility of cusp fracture and microleakage in such teeth at the margins of restoration.

2.1.2 Significance of Microleakage & Coronal Barrier

“Coronal leakage” or “coronal microleakage” refers to the recontamination of the root canal system after treatment due to bacterial contaminates from saliva leaking in through the endodontic access cavity. Bacterial by-products and toxins can penetrate all the way up to the root apex causing inflammation of periapical tissues, thereby requiring retreatment or periradicular surgery.32 Obturated root canals may be recontaminated by micro-organisms due to any of the following reasons33: (1) Delay in placing coronal restoration following root canal treatment. Temporary restorative material cannot provide adequate seal against leakage as compared to appropriate and prompt permanent restoration34,35 , (2) Fracture of coronal restoration and/or tooth, and (3) Preparation of post space for core retention in absence of adequate remaining coronal tooth structure. Mechanical preparation of post space can disrupt apical seal increasing chances of root canal treatment failure and possibly root fracture, especially when an oversized post channel is prepared.36 Coronal leakage has been considered one of the major causes of failure in root canal treatment.32,37 Aseptic treatment techniques, including the use of a rubber dam should be used during and after endodontic treatment to prevent contamination of root-canal system with bacteria. Leak-proof restorations should be placed as soon as possible following endodontic therapy. Retreatment must be considered for teeth with coronal seal compromised for over three months.5 Sometimes procedures for a long- term restoration are delayed, because of the time needed for the assessment of the endodontic treatment success. When immediate tooth restoration is not possible, use of intracoronal or orifice barriers can provide a second line of defense against coronal leakage by sealing canals and floor of pulp chamber during the period of temporization and while restorative dentistry is being performed.29,38 However, no obturation material or technique has been proven to prevent microleakage for an indefinite period of time. The need for an immediate and

19 appropriate coronal restoration after root canal treatment is therefore critical for long-term success.32

2.1.3 Significance of Full Cuspal Coverage

Loss of tooth structure due to endodontic procedures, caries, trauma or existing restorations in endodontically treated teeth is yet another factor that may contribute to decreased protection during mastication and eventual tooth fracture. Teeth have neurosensory feedback mechanism that is impaired with the removal of pulpal tissue. 39 Remaining coronal tooth structure before the final restoration is directly proportional to the ability to resist occlusal forces and is therefore an important factor in determining appropriate restoration plan, which must aim to provide full cuspal coverage as soon as possible following endodontic treatment.40 A retrospective study of 1273 endodontically treated teeth by Sorensen and Martinoff41 concluded that the presence of full cuspal coverage was the only significant restorative variable predicting long-term success. Endodontic treatment significantly increased failure in posterior teeth without coverage. The need for full crown coverage in posterior teeth to prevent root fracture has been supported by in vitro42,43 and retrospective41,44 clinical studies. Study of effect of restorative procedures on the strength of endodontically treated molars by Linn and Messer45 replicated the conclusion that endodontically treated (mandibular) molar teeth are strengthened more by providing full cuspal coverage than by preserving intact marginal ridges.

When tooth type and radiographic evidence of caries at access were controlled, Aquilino and

Caplan46 reported that endodontically treated teeth receiving full coverage restorations after obturation had 6 times higher survival rate than those that did not. Scurria, Shugars, Hayden, and Felton47 summarized literature’s recommendations that for anterior teeth with conservative endodontic access and intact marginal ridges, coronal coverage is not indicated. Direct restoration as opposed to crown restoration may be more favorable in anterior teeth given the

20 risk associated with crown preparation procedures. However, endodontically treated posterior teeth should receive full cuspal coverage restorations to provide resistance to fracture. Despite strongly recommended approaches in literature, their study of 1,199 endodontically treated teeth found that up to 40% of posterior teeth did not receive extracoronal or cuspal coverage restorations by general dentists treating such teeth.

2.1.4 Caries management

Dental caries, otherwise known as tooth decay, is the localized destruction of susceptible dental hard tissues by acidic by-products from bacterial fermentation of dietary sugars and carbohydrates. 48,49 It is one of the most prevalent and preventable diseases of people worldwide; affecting individuals throughout their lifetime.50,51 The disease develops in both crowns and roots of teeth and is the primary cause of oral pain and tooth loss.48 Risk for developing caries includes several factors such as high numbers of cariogenic bacteria, insufficient salivary flow, insufficient exposure to fluoride, poor oral hygiene habits, inappropriate methods of feeding infants, and poverty.49 Patients should be classified into low-, medium- , and high-risk categories and managed based on their risk of developing caries.16

Treatment plan must be tailored for each of the three risk levels, as management differs at each level. Most advisable and actionable clinical approach is to completely remove previous restorations and all existing caries before initiating treatment planning process. This helps understand true extent of tooth damage and amount of restoration necessary. In clinical practice around the world, caries management by tooth restoration and retention is the most favored approach.49 However, treatment plan that focuses on restoration without a preventive approach, results in poor durability of restoration coupled with propensity of new caries to develop at the margins of restorations if the causes of disease are not removed.52 Restorative

21 treatment plan involving preventive approach to caries management that focuses on preservation of tooth structure is likely to yield better treatment outcomes in the long run.53

2.1.5 Significance of Remaining Coronal Tooth Structure

As discussed above, endodontically treated teeth often lack tooth structure as a result of endodontic procedures, caries, trauma or previous restorations. In these situations, successful restoration of an endodontically treated tooth may be challenging. To improve prosthetic forecast and to ensure functional longevity, endodontically treated teeth must have at least 5 mm of non-carious tooth structure coronal to the crestal bone.54 Three millimeters of biologic width is needed to maintain a healthy soft tissue complex, and two millimeters of coronal tooth structure incisal to the preparation finish line for ferrule effect are necessary to preserve structural integrity.55 Need for Biologic width and ferrule effect are discussed in further detail in following sections.

When remaining coronal tooth structure is less than the desired 5 millimeters in height, it may be increased either surgically through a crown lengthening procedure or orthodontically through forced extrusion of the tooth. Surgical crown lengthening procedure may be as simple as limited removal of soft tissue in some cases. However, other cases may be more involved requiring ostectomy, removal of alveolar bone from the circumference of tooth or teeth being operated on.56 Orthodontic eruption should be considered in cases where traditional crown lengthening via ostectomy cannot be accomplished, such as in the anterior area, as ostectomy would lead to a negative architecture and also remove bone from the adjacent teeth, which can compromise the functional longevity of these teeth.57 Both procedures result in a satisfactory and predictable increase in coronal tooth structure but may be contraindicated in certain situations. Contraindications of crown lengthening include deep caries or fracture requiring excessive bone removal, post-surgical unaesthetic outcomes, tooth with insufficient crown-to- root ratio, non-restorable teeth, tooth with increased risk of furcation involvement, unreasonable compromise of esthetics, and risk to adjacent alveolar bone support.58 Some of the contraindications to orthodontic forced eruption are inadequate crown-to-root ratio, lack of occlusal clearance for necessary amount of eruption, and any possible periodontal complications.59

As coronal tooth structure is increased by crown lengthening or orthodontic eruption, the corresponding osseous-supported tooth structure is decreased. This change in the crown-to- root ratio may render the tooth less resistant to lateral forces and more prone to fracture.

Although 2:1 crown-to-root ratio is ideally preferred, 1:1 crown-to-root ratio has been advocated as the minimum ratio necessary for resisting lateral forces that may occur during function.60 Restoration of coronal tooth structure in endodontically treated teeth could thus involve endodontic, orthodontic, periodontic, and restorative procedures hiking the combined

23 time, effort and cost of the total treatment, as well as the combined risk with inherent failure rate of each procedure. This makes it really important for the provider to perform careful evaluation of the factors and risks, case selection and treatment planning, as well as thoroughly discuss all treatment plan options with the patient before performing irreversible procedures such as crown lengthening or orthodontic forced eruption. When functional restoration with good long-term prognosis cannot be predictably created, providers should consider tooth extraction.61

2.1.6 Significance of Biologic Width

Biologic width is defined as the dimension of the soft tissue, which is attached to the portion of the tooth coronal to the crest of the alveolar bone.57 In other words, it is the distance established by the junctional epithelium and connective tissue attachment to the root surface of a tooth.62 When treatment planning restoration of endodontically treated teeth, biologic width must be taken into account for maintenance of pristine periodontal health. Tooth and dental restoration longevity is tightly coupled with maintenance of gingival health. Violating biologic width integrity tend to result into negative treatment outcomes such as unpredictable loss of alveolar bone, chronic pain, chronic inflammation of the gingiva and subsequent failure of the dental restoration.63

The term “Biologic Width” was based on the work of Gargiulo, Wentz, and Orban64 who published dimensions and relationship of the dentogingival junction in humans in their classic study. Based on this study, biologic width is commonly stated to be 2.04 mm, which represents the sum of the epithelial and connective tissue measurements. In 1977, Ingber, Rose, and

Coslet65 described “Biologic Width” and credited D. Walter Cohen for first coining the term.

Measurement reported in these studies is just an arithmetic mean, actual biologic width differs from patient to patient and for each patient it further differs based on location of the tooth in the alveolus, from tooth to tooth, and also from the aspect of the tooth. It has been shown that

1 mm of supracrestal connective tissue attachment, 1 mm junctional epithelium and 1 mm for gingival sulcus adding up to 3 mm on an average allow for adequate biologic width even when restoration margins are placed 0.5 mm within the gingival sulcus.66

Given the conventional definition and importance of biologic width described above, restorative dentists were still unable to successfully predict restorative treatment outcomes. Restorative procedure performed on one patient might exhibit long-term stability, but resulted into chronic

25 inflammation or even gingival recession around crowns in other patients. However, in the mid-

1990s, Kois published his classic papers on biologic width.67,68 To prevent biologic width violation, he proposed three categories of biologic width based on the total dimension of attachment plus the sulcus depth, namely: Normal Crest, High Crest, and Low Crest. Knowledge of crest category is necessary for restorative dentists to plan optimal position of margin placement, as well as to inform patient of the possible long-term effects of crown margin on gingival health and esthetics. Treatment plan tailored according to biologic width crest category, regular follow-up visits, patient co-operation and compliance are important for improved success of restorative procedures and to maintain optimal periodontal health.69

2.1.7 Significance of Ferrule Effect

Vast majority of literature data suggests that the presence of a ferrule is a significant factor in improving fracture resistance of endodontically treated teeth.12-15,70,71 A ferrule is a metal ring or cap used to strengthen the end of a stick or tube.12 A ferrule effect is defined as a ‘‘360° metal collar of the crown surrounding the parallel walls of the dentine extending coronal to the shoulder of the preparation. The result is an elevation in resistance form of the crown from the extension of dentinal tooth structure’’.13 In other words, “parallel walls of dentin extending coronally from the crown margin provide a ferrule, which after being encircled by a crown provides a protective effect by reducing stresses within a tooth called the ferrule effect’’.72

Maintaining intact coronal tooth structure and cervical tissue to create a ferrule effect are considered key to optimize biomechanical behavior of the restored tooth.15 Figure 2.3 illustrates the concept of ferrule effect and depicts restored tooth with adequate ferrule (on the left) and one without any ferrule (on the right). Studies have shown that an increased amount of dentin extending coronal to the crown margin circumferentially, significantly increases the fracture resistance of endodontically treated teeth. With ferrule length between 1.5 to 2 mm, more

26 successful prognosis of restoration can be expected as compared to cases where ferrule effect is missing.14,70,71,73,74

Figure 2-3. Illustration of the concept of ferrule effect. Figure on the left depicts restored tooth with adequate ferrule and one on the right depicts restored tooth without any ferrule.

Clinicians may encounter severely damaged teeth due to extensive carious lesions, fractures, previous restorations or endodontic treatment in which it may not be possible to obtain 360° circumferential ferrule. In such cases, orthodontic eruption should be treatment planned as opposed to crown lengthening to provide space for ferrule based on remaining amount of sound tooth structure. This approach preserves more coronal tooth structure and ensures better fracture resistance.15,75 If a 360° circumferential ferrule is not achievable, an incomplete ferrule is still considered better than complete lack of ferrule for better treatment outcomes.76

Although including ferrule in the preparation design is desirable, extreme caution should be exercised before performing either orthodontic eruption or crown lengthening when restoring structurally compromised teeth. Both these alternatives could lead to an additional and irreversible loss of tooth tissue compromising remaining coronal or root structure.77 Whenever an adequate ferrule cannot be established, tooth extraction and replacement with conventional

27 or implant-supported prosthodontics is usually a better option as the long-term tooth restoration prognosis is poor.78

2.1.8 Significance of Occlusal Forces

Ensuring clinical symptom free teeth and restoring their functional capability is one of the main goals of endodontic and restorative treatment. Maintaining proper occlusal equilibrium during and following endodontic and restorative treatment is critical.3 Endodontically treated teeth that are successfully treated can withstand a maximum bite force comparable to natural teeth, thus being able to regain a level of masticatory function similar to that in sound teeth.79

However, planning successful restoration of endodontically treated teeth requires careful assessment of subjected occlusal forces. Different forces acting upon teeth include normal chewing, single- and/or multiple-tooth bruxing and clenching, each of which exert variable effects on restored endodontically treated teeth. Another important consideration in the treatment planning process is position of the teeth in dental arch. Posterior teeth tend to receive more extreme forces than anterior teeth.

Nocturnal bite force of bruxing exerts 2-3 times greater force than normal chewing bite force and also lasts longer than normal chewing duration. This longer duration of bruxing with greater force than normal chewing could potentially cause greater damage to teeth restored following endodontic therapy. Similarly, chewing forces are reported to be up to 10 times greater than maximum biting forces distributed in a balanced way. “Maximum biting forces are exerted in the maximum intercuspal position and are distributed according to distance from the condyles: the second molar takes 55% of the maximum force, while the incisors take only 20%. Research demonstrates that, due to progressive cuspal displacement both time- and load-dependent, continuous loading as in clenching is more destructive than cyclic loading as in chewing”.80 Such

28 continuous loading, especially in restored teeth, can cause permanent deformation, leaving dentinal cracks and tears. Overtime and with continued use; these dentinal cracks can propagate further causing fracture in the tooth. A favorable occlusal design that accounts for variable effects of occlusal loads is therefore very important factor for good long-term prognosis of restored endodontically treated teeth.

Bonded intra-coronal restorations are sufficient for restoring endodontically treated teeth with normal functional occlusal load and with minimal loss of tooth structure. However, restoration treatment plan should be different in case of increased functional occlusal load. When endodontically treated teeth serve as abutment teeth for removable or fixed partial dentures and in patients with bruxing and clenching tendencies, the functional occlusal load is heavier than normal. For stable and successful outcomes of the endodontic and restorative treatments in the long-term, both intra- and extra-coronal restorations are recommended in such cases.

Anterior teeth must resist lateral and shearing types of forces warranting need for full cuspal coverage if missing significant coronal tooth structure. Root canal treatment and crown preparation procedures tend to further wear out remaining tooth structure. Post is often indicated in such endodontically treated anterior teeth indicated to receive crown, since pulp chambers of anterior teeth are too small to provide adequate retention and resistance against all acting occlusal forces. Endodontically treated molars should receive cuspal coverage, but in most cases, do not require a post. Unless there is extensive destruction of coronal tooth structure, the pulp chamber and canals provide sufficient retention for a core buildup.81

Premolars are single-rooted with relatively small pulp chambers and more likely than molars to be subjected to lateral forces during mastication. For these reasons, endodontically treated premolars require posts more often than molars. Remaining tooth structure and functional demands are determining factors of need for post-and-core system prior to any prosthetic

29 restoration.29 In the following section, we cover the principles of restoration with posts and core in greater detail.

When patient is predisposed to bruxing, treatment plan must recommend use of a nightguard.

Paranormal occlusal stress caused due to bruxing can be alleviated to some extent with compliant use of nightguard or if patient is a bruxer with complete dentures and is compliant by removing dentures at night. When teeth with heavier than normal occlusal load cannot be restored satisfactorily with good long-term prognosis, tooth extraction followed by implant- supported prosthodontics may be a better option. These considerations are important when selecting materials and techniques for single-tooth coronal restoration or abutment teeth reconstruction following endodontic treatment.82

2.1.9 Principles in the Use of Post and Core System

2.1.9.1 Need for Posts in Endodontically Treated Teeth

Primary purpose of a post and core is to reinforce the remaining coronal tooth structure and to replace missing coronal tooth structure.39 Posts may not necessarily reinforce endodontically treated teeth and are not necessary when substantial tooth structure is intact after teeth have been prepared. Guzy and Nicholls83 evaluated reinforcement capabilities after cementing a post into an endodontically treated tooth and found no significant difference if the tooth was largely intact except for the access opening. Leary, Aquilino, and Svare84 measured the root deflection of endodontically treated tooth before and after posts of various lengths were cemented into prepared root canals. Their results showed that missing tooth structure weakened the tooth, however found no significant differences in strength with or without a post in teeth with adequate remaining coronal tooth structure. There are inherent risks involved in the post-space preparation procedures that may increase chances of root fracture and treatment failure. A

30 study reported that preparation of post space in the roots of endodontically treated teeth significantly weakened remaining tooth structure resulting into fractures as compared with ones in which only an access opening was made, but no post space.85 Finite element analysis carried out in a study86 to understand role of posts reported up to 20% reduction in maximal dentin stress in posted teeth subjected to vertical loadings. However, the study reported that the reinforcement effect of posts is doubtful for anterior teeth subjected to angular forces. Due to these reasons, posts should only be used when significant amount of tooth structure is missing and other options for core retention are not available.

A post and core may help prevent coronal fractures when the remaining coronal tooth structure is very delicate after tooth preparation.87 Kantor and Pines88 determined that cementing a stainless steel rod into prepared post spaces of teeth that had also been prepared for complete coverage crowns increased the fracture resistance of such teeth as compared with those that were only prepared for complete crowns but had no post. Hunter, Feiglin, and Williams36 investigated the effect of endodontic therapy, post-space preparation, and post placement using photoelastic stress analysis. Results of their study indicated that removal of internal tooth structure during endodontic therapy is accompanied by a proportional increase in cervical stress and that post placement helps decrease stresses in this region.

Decision regarding post placement should be made based on position of the tooth in the arch, amount of remaining coronal tooth structure, and functional requirements of the tooth.39 The amount of tooth structure to warrant post insertion can be classified into 5 categories based on remaining axial cavity walls after access preparation. Class I representing 4 remaining cavity walls does not warrant post insertion. Class II describes loss of 1 cavity wall forming mesio- occlusal (MO) or disto-occlusal (DO) cavity. Class III describes an MOD cavity with 2 remaining

31 walls either in “L” or opposing configuration. Treatment planning for class II or class III with 2 walls in opposite configuration should not warrant need for post insertion, as the remaining hard tissue provides enough surface for simple adhesive core restoration. Class IV with 1 remaining cavity wall and class III with 2 remaining walls in “L” configuration, the core material by itself has little or no effect on the fracture resistance of endodontically treated teeth.89 If the tooth has to be used as an abutment for fixed or removable partial dentures, crown preparation will further impact fracture resistance.90 Use of posts in such cases of reduced remaining tooth structure with demanding occlusal loads is highly recommended.18 Class V represents decoronated teeth with high degree of destruction and no cavity wall remaining. Post insertion for core material retention is highly recommended is such cases. Additionally, presence of ferrule effect has a great influence on the fracture resistance, especially in decoronated teeth.18

The integrity of root-canal treated teeth must be carefully evaluated when they are used as abutment teeth for prosthodontic treatments.88 In a clinical study of endodontically treated teeth, Sorensen and Martinoff 91 found that post placement was associated with a significantly decreased success rate for single crowns. They found that use of posts led to no significant change in the success of fixed partial denture abutments, and significantly improved the success rate of removable partial denture abutment teeth.

2.1.9.2 Type of Posts and Post Design

Post retention and resistance are importance principles in restorative treatment plan involving post placement. Post retention refers to the ability of a post to resist vertical dislodging forces, whereas resistance refers to its ability to withstand lateral and rotational forces.29 Retention is influenced by post’s length, diameter and taper, luting material, and whether post is active or passive.92 Resistance is influenced by remaining tooth structure, length and rigidity of the post, presence of anti-rotation features, and the presence of a ferrule. A restoration lacking resistance

32 is not likely to yield successful results in the long-term, regardless of the retentiveness of the post.93 Another important factor related to resistance is failure mode. Loosening of the post and tooth fractures are the two most common types of post and core failures. Although all post systems have an inherent risk of failure, some tend to fail less favorably rendering non- restorable teeth. Posts that are less rigid, presence of adequate ferrule and type of core material are some of the factors affecting likelihood of failures to be restorable.94-96 Post retrievability should also be considered when treatment planning a post. Sometimes endodontic retreatment may become necessary and at such times posts that can be effectively and safely retrieved can significantly improve the chances of successful retreatment.

There exist many different types of posts and they can be classified as active or passive, parallel or tapered, and by material composition. Active posts are threaded and are intended to engage the walls of the canal, whereas passive posts’ retention relies exclusively on the luting agent.

Active posts are more retentive than passive posts, but introduce more stress into the root than passive posts.29,92,97 Parallel posts are more retentive and less likely to cause root fractures than tapered posts. However, tapered posts are preferable in teeth with thin roots and delicate morphology as they require less dentin removal.29,44,92 Prefabricated posts are typically made of stainless steel, nickel, chromium, or titanium alloy. They are very rigid, but offer little resistance to rotational forces. They are also quite challenging to retrieve should endodontic retreatment be necessary. For these reasons, they are less favorable than stronger metal posts. Custom cast posts and cores were the standard for many years and offer advantages in certain clinical situations. Although these require more visits and laboratory fee, it can be more efficient when multiple teeth require posts, a tooth is misaligned and the core must be angled in relation to the post for better alignment with adjacent teeth or in small teeth such as mandibular incisors with minimal coronal tooth structure available for anti-rotation features or bonding. However, they

33 have fallen out of favor in recent years as they generally do not perform as well as other types of posts in most commonly presenting clinical scenarios.98,99 Ceramic and zirconium posts are esthetically better than metal posts. However, these posts have several disadvantages including poor retention to core material, weaker than metal posts, poor retrievability should endodontic retreatment be necessary.29 Fiber posts have gained popularity in recent years as they offer several advantages. They are more flexible than metal posts and have approximately the same stiffness (modulus of elasticity) as dentin. Many in vitro and in vivo studies have shown that forces through fiber posts are distributed more evenly, resulting in fewer root fractures. They are easy to retrieve for endodontic re-treatment, resistant to corrosion and non-hypersensitive.

Newer generation fiber posts also offer better esthetic results, however clinical evaluation to establish long-term efficacy is necessary.29,100-104

In this section we have reviewed many different types of posts with different designs and materials. The reason that these many types of post systems exist is because they all have certain strengths and limitations. Selection criteria should include adequate strength, modulus of elasticity, retention, resistance, biocompatibility, esthetics and retrievability. Understanding of these important principles in the use of post are imperative for the post placement planning process to ensure good long-term prognosis.

2.1.9.3 Post Space Preparation Considerations

Root anatomy differs from tooth to tooth. Even within the same tooth it differs in different patients. Knowing the root anatomy of different teeth is important before preparation of the post space, which must be carefully evaluated and planned for. Post length is one of the important parameters that must be carefully determined. Although increasing post length increases retention, this also increases risk of fracture and perforation of the remaining root.84

In view of these trade-offs, acceptable guidelines for determining post length include2,18: (1) clinical crown length to post length ratio of at least 1:1 should be maintained, (2) post length should reach two-thirds of the remaining root length, and (3) post should extend to one-half the root length that is supported by bone.

Post diameter is also an important parameter while planning for post space preparation.

Although little evidence exists for an optimal post diameter, it should not exceed one-third of the root diameter as has been postulated in literature reviews. 2,18 Any increase in post diameter beyond one-third of the root diameter could increase the risk of root fracture.92,105 Additionally, a minimum of 1 mm of sound dentin should be maintained circumferentially around the post to preserve tooth structure and minimize fracture risk. Studies have postulated a post diameter of at least 1.25 to 1.3 mm.18,93

Post fixation with the help of luting cement is an important step in the post space preparation planning process. Luting cements help bind posts so that they firmly attach to the root structure.

There exist several different luting cements in use in routine dental practice with the most commonly used ones being zinc phosphate, resin, glass ionomer, and resin-modified glass- ionomer cements. Several studies have been conducted to compare failure mode, retention and fracture resistance of different posts and luting agent combination using continuous or intermittent loading with mixed results. A review of these different studies is beyond the scope of this literature review. Schwartz and Robbins29 provide a good summary in their review of these studies comparing strengths and limitations of different luting cements. Their review suggests promising trend in the use of resin cements, which are known to increase retention, tend to leak less than other cements, and resistant to cyclic loading. The dentin, resin and post can be joined via resin adhesion into a single unit. Adhesive fixation of post and core to the

35 dentin enamel has been postulated to stabilize tooth structure and improve fracture resistance.18 However, resin cements are more technique sensitive and require extra steps such as thorough cleaning and etching of the canal walls, which must be performed quickly and carefully to ensure post is completely seated.

2.1.9.4 Core Materials

Purpose of post is to retain core and that of core is to retain crown. Construction of core buildup is necessary as the amount of residual tooth structure is lost and core buildup bolsters retention and resistance form provided by the remaining tooth structure. Ideal characteristics of core material as summarized by Cheung2 and Morgano and Brackett78 include resistance to microleakage at the core-to-tooth junction, sufficient compressive strength to resist intra- occlusal forces, sufficient flexural strength, minimal potential for water absorption, easy maneuverability, biocompatibility, ability to bond well to remaining tooth structure, thermal expansion and compression coefficient similar to natural teeth, dimensional stability and inhibition of dental caries. Such an ideal core material doesn’t yet exist, with all commonly used core materials exhibiting or lacking some ideal characteristics.

The most commonly used core materials are cast gold, amalgam, resin-based composite and glass ionomer cement. An in vitro study96 examining the effect of core stiffness on fracture resistance and failure characteristics of crowned, endodontically treated tooth found no significant differences between resin-based composite, cast gold and amalgam as core material, provided a 2-mm ferrule existed on the margin of healthy tooth substance. However, the study reported glass ionomer cement as having low tensile and compressive strength and low fracture resistance. Another study106 reported a low modulus of elasticity, poor bonding characteristics to dentin and enamel, poor condensability and high solubility for glass ionomer cement. For

36 these reasons, studies have recommended against the use of glass ionomer cement as a core material.

“Both cast gold and amalgam have been used successfully for many years, as they exhibit high strength low solubility, and their coefficient of thermal expansion is similar to that of tooth substance. Placing cast gold post and core, however, is an indirect procedure requiring two visits”.2 “Amalgam requires the addition of pins or other methods to provide retention and resistance to rotation. Placement can be clumsy when there is minimal coronal tooth structure, and the crown preparation must be delayed to permit the material time to set. Amalgam can cause esthetic problems with ceramic crowns and sometimes makes the gingiva appear dark.

There also is a risk of tattooing the cervical gingiva with amalgam particles during the crown preparation. For these reasons, and potential concern about mercury, it is no longer widely used as a buildup material”.29

Currently, composite resin is the most popular core material, because of many desirable characteristics. These include good bonding to many of the current posts and tooth structure improving retention, high tensile strength, low solubility, allows for immediate crown preparation after polymerization, favorable fracture patterns upon failure and esthetically pleasing material especially in the anterior section under an all-porcelain restoration. Its limitations include polymerization shrinkage, hydroscopic expansion due to water adsorption, and creation of voids in the buildup because it cannot be condensed like amalgam. Strict isolation and thorough removal of residual root canal sealer coupled with incremental buildup are absolute requirements to alleviate potential of microleakage and to assure adequate adhesion. 5,89

2.1.9.5 Final Restoration

Ray and Trope107 evaluated relationship between the quality of coronal restoration and that of root canal filling on radiographic periapical status of endodontically treated teeth. They observed that good restorations combined with good endodontic treatments resulted in absence of periapical inflammation in 91.4% of the teeth, whereas poor restorations combined with poor endodontic treatments resulted in the absence of periradicular inflammation in only

18.1% of the examined teeth. Moreover, in their study of poor endodontic treatments followed by good permanent restorations appearing to be radiographically sealed, the resultant success rate was 67.6%. They concluded that apical periodontal health depended significantly more on the coronal restoration than on the technical quality of the endodontic therapy. Given the importance of good restoration to periapical health, careful restorative treatment planning is imperative to achieve high levels of clinical success.

As discussed above, endodontically treated anterior teeth may be conservatively restored with bonded restoration in the access cavity if they are largely intact with minimal loss of tooth structure. Coronal coverage is not necessary unless a significant amount of tooth structure is lost as a result of caries or fracture. If only simple restoration is necessary, resin-based composite with acid etching of enamel and dentin is advisable. Restoring teeth with resin-based composite coupled with acid etching of enamel and dentin can result in recovery of tooth stiffness by up to 88 percent of that of unaltered teeth.30

Castings such as gold onlay, gold crowns, metal-ceramic crowns, and all-porcelain restorations with cuspal coverage are routinely used as acceptable methods for restoring endodontically treated posterior teeth. Although these restorative procedures require extensive preparation and can be expensive, they are necessary for long-term preservation of the remaining tooth

38 structure. If the amount of remaining tooth structure is very thin after tooth preparation, use of post and core may help prevent coronal fractures. Principles involved in the selection of post and core material as discussed in the sections above are important for long-term success. Dental practitioners must evaluate each clinical situation carefully before making decisions about the final restoration, because it is the quality of restorative dentistry performed after root canal treatment that directly impacts the prognosis of endodontically treated teeth.

2.2 Review of Clinical Decision Support Systems

2.2.1 Definition and Concept of Computer-based Clinical Decision Support Systems

Clinical decision support systems (CDSSs) are “computer programs that are designed to provide expert support to healthcare professionals making clinical decisions”.108 Such systems focus on providing “knowledge and patient-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care”.109 CDS systems provide situation- and clinical context-specific information and recommendations. Also, they do not themselves perform clinical decision making; rather they provide relevant knowledge and analyses to ultimate decision makers who could be clinicians, patients, and health care organizations to develop more informed decisions regarding diagnosis, prevention, and treatment of health problems.110 Agency for Healthcare Research and Quality’s “CDS Five Rights Model” states that

“CDS-supported improvements in healthcare outcomes can be achieved if CDS tools are implemented to communicate:

1. The right information: evidence-based, suitable to guide action, pertinent to the

circumstance

2. To the right person: considering all members of the care team, including clinicians,

patients, and their caretakers

3. In the right CDS intervention format: such as an alert, order set, or reference

information to answer a clinical question

4. Through the right channel: for example, a clinical information system (CIS) such as an

electronic medical record (EMR), personal health record (PHR), or a more general

channel such as the Internet or a mobile device

5. At the right time in workflow: for example, at time of decision/action/need”111.

In a study on improving medication use and outcomes with clinical decision support, the authors have addressed each of the above five CDS rights in further detail as failure to address any one or more of these elements have often been traced to failures in CDS implementation.112

2.2.2 Historical Perspective

2.2.2.1 Clinical Decision Support Systems in Medical Field

It is challenging to track down the exact origin of the field of biomedical informatics. However, since the early days of computers, health professionals have anticipated the use of computers for assistance in the diagnostic process. The first article dealing with this possibility appeared in the 1959 paper “Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason” by Robert Ledley and Lee

Lusted.113 This paper described a probabilistic model for medical diagnosis, with grounds in set- theory and Bayesian inference. It proposed the use of an analog computer by clinicians to develop differential diagnosis based on symptoms filled out on punch cards. Soon thereafter in

1961, Warner, Toronto, Veasey, and Stephenson114 published their work on “A mathematical approach to medical diagnosis: Application to congenital heart disease”. They used contingency tables to map clinical signs, symptoms, and electrocardiographic findings in patients with congenital heart disease to develop differential diagnosis in the field. Comparison of their

40 system using gold-standard surgical diagnoses to experienced cardiologists yielded favorable results.

In 1964, a group of clinical researchers at Kaiser Permanente developed a system for “Automatic multiphasic screening and diagnosis”.115 Their system incorporated statistical methods to provide comprehensive, quantitative, and precise testing for the likelihood of the presence or absence of specific diagnoses according to predetermined sensitivity and specificity criteria.

Screening of bronchial asthma was used to illustrate system’s effectiveness in disease detection, prevention of illness, and reduction in morbidity and mortality. In 1969, Howard Bleich developed a system to evaluate clinical and laboratory information concerning patients with acid-base disorders.116 In addition to providing diagnostic information, this was one of the first systems to also recommend therapeutic measures.

Two years later, in 1971, de Dombal and few of his colleagues at University of Leeds developed a computer-based probabilistic model for diagnosis of abdominal pain complaints.117 Their system used sensitivity, specificity, and disease-prevalence data for various signs, symptoms, and test results to calculate the probability of seven possible reasons for abdominal pain (appendicitis, diverticulitis, perforated peptic ulcer, nonspecific abdominal pain, cholecystitis, small bowel obstruction, and pancreatitis). In a controlled prospective comparison study, the system’s overall diagnostic accuracy (91.8 percent) was significantly higher than that of the most senior member of the clinical team who saw each case (79.6 percent).118

In 1975, Ted Shortliffe developed MYCIN, a rule-based expert system for physicians requesting advice regarding appropriate antimicrobial therapy for hospital patients with bacterial infections.119 Knowledge of infectious diseases was obtained with expert collaboration and represented as rules chained together using backward chaining to make decisions. The system

41 had its roots in the field of artificial intelligence that had begun to influence clinical informatics.

Early evaluation on therapy selection for cases of bacteremia120 and meningitis121 showed system’s therapy recommendations were consistent with those of experts’ 90.9% and 65% of the times for bacteremia and meningitis respectively. Although the system was never used clinically, it paved way for a great deal of research and development with many expert system development techniques in current use either developed or based in the MYCIN project.122

In 1983, ATTENDING system developed by Perry Miller at Yale University followed a different approach to clinical decision support.123 Unlike other CDSSs developed to provide diagnostic or therapeutic advice, ATTENDING used a method of interaction called critiquing. In addition to clinical parameters as in other CDSS applications, this system required users to also enter a proposed patient management plan. The system would then make comments and suggestions about the plan, and it would be up to the user to change the plan based on those suggestions.

This system laid the groundwork for CDSS development for other clinical domains in medicine and dentistry.

The 1980s were marked by other important contributions to the field of clinical informatics.

INTERNIST-I system developed by Randolph Miller, Harry Pople and Jack Myers in 1985 attempted to provide diagnostic decision support across the entire field of internal medicine.

Until then, systems were limited to a very specialized and narrow clinical domain. On the other hand, INTERNIST-I’s knowledge base comprised of “15 person-years of work, 570 disease profiles, [and] 3550 manifestations of disease”. System’s evaluation on a series of 19 clinicopathological exercises published in the New England Journal of Medicine yielded qualitatively similar results to that of average clinicians’ diagnosis, but inferior to experts’ who wrote the cases up. Evaluation brought up specific other deficiencies including program’s

42 inability to reason anatomically or temporally, its inability to develop differential diagnosis spanning multiple problem areas, occasional inaccurate diagnosis, and inability to justify recommendations.124 However, the work made an important contribution in the field by laying a foundation for future projects to be built upon. One of its best known successors, Quick Medical

Reference (QMR) provided users with multiple ways of reviewing and manipulation diagnostic information in the knowledge base and allowed hypotheses generation in complex patient cases. QMR is widely distributed commercially for use on personal computers.125,126 In 1990s,

INTERNIST-I was reengineered using Protégé-II to provide a domain ontology, comprehensive knowledge base, and a new diagnostic problem-solving method to model future development and reuse of knowledge-based components.127 Soon after INTERNIST-I, a hybrid system combining the properties of deductive rules and probabilistic reasoning, the DXplain system was released.128 Unlike INTERNIST-I, DXplain was designed to explain its diagnostic reasoning process. The DXplain system is still in use today, has been updated, and made available as a web-based application.129

All systems discussed heretofore are stand-alone decision support systems. Although, each of these systems have their unique strengths, one potential limitation common to them all is the considerable time and effort involved in the clinical data entry process for the systems to come up with meaningful recommendations. To address this limitation, work on CDSSs has focused on integrating these applications with clinical databases such as Electronic Health Records (E.H.R.) and Computerized Provider Order Entry (CPOE) systems. Notable examples of CDSSs in this category include the HELP (Health Evaluation through Logical Processing)130 system developed at

LDS hospital at the University of Utah, and Regenstrief Medical Record System (RMRS)131 developed at Indiana University. With such integrated systems, users do not have to re-enter information already stored electronically. However, major downside of such integrated systems

43 is that there is no easy way to share or reuse their content knowledge. Knowledge maintenance can also be challenging, since new knowledge, such as clinical guidelines to be incorporated or updated into the knowledge base would require entire integrated system’s source code to be revisited to locate all necessary places for insertion/updates. In response to the inability to share decision support content, efforts have been undertaken to develop standards for knowledge representation within CDSSs. Foremost among these are Arden Syntax132 and Guideline

Interchange Format (GLIF)133. Use of standards provide a method for sharing the decision support content, and separates the knowledge representation module from the clinical information system module. However, standards have some inherent limitations and disadvantages. The user of any standard is limited and constrained by its scope while encoding and expressing rules or content knowledge in the knowledge base. Stand-alone or integrated systems do not have this limitation as expression in such systems is based upon expressive capabilities of the underlying programming languages and data models used.

More recent efforts have been made to decompose integrated CDSSs into the information and decision support components and recombine them using a standardized, sharable application programming interface (API). This approach allows creation of service modules that are accessible for use by any CDSS implementing the respective API. API can be placed in front of the clinical information module as demonstrated in the implementation of Shareable Active

Guideline Environment (SAGE) project134,135 or in front of the decision support module as demonstrated in the implementation of the SEBASTIAN project136. SAGE approach, also termed as Virtual Medical Record (VMR)137 approach has the principle advantage that it solves the vocabulary problem. It specifies vocabularies that will be used to access and process the medical records and if any clinical system uses a different terminology they could still access the VMR so long as a suitable mapping is provided. However, problem with this approach is the same as that

44 of Arden Syntax, which is, they both require a standard guideline format limiting the type of decision support that can be implemented. SEBASTIAN approach addresses this problem as it does not require that clinical systems store data in a particular format. It facilitates knowledge maintenance by encapsulating and centralizing executable, version-controlled and metadata- tagged medical knowledge into its service modules. Any clinical system that follows SEBASTIAN protocol for enabling CDS capabilities can gain access its centralized decision support service modules. These modules are located and maintained on the Web, thereby facilitating sharing of decision support content between health care organizations and promoting greater efficiency in the use of CDSSs. However, this approach also has some drawbacks. It places significant demands on clinical systems using its decision support services to provide patient data in return for CDS results. More than one SEBASTIAN systems sharing services, but implemented using different vocabulary standards, would be required to provide support for each of the disparate vocabulary standards to be truly interoperable. Also, this approach requires patient data to be moved to the service module on Web posing a privacy and security risk. SEBASTIAN knowledge base will need to be significantly expanded in order to comprehensively meet the CDS needs of health care organizations in various clinical contexts.138 These limitations aside, SEBASTIAN architecture is promising and serves as the basis for emerging HL7 standard for Web service- based decision support.139

As discussed above, each CDSS implementation approach has some unique strengths and limitations. A perfect system that meets all the needs of health care organizations in various clinical contexts does not exist. However, tremendous research and development has been undertaken, which has laid the foundation for interesting work to be done in the future across the entire spectrum of clinical care. Adam Wright and Dean Sittig140 have provided

45 comprehensive and thorough account of the evolution of CDSS architectures in their review paper. Table 2-1 provides a summary of the evolution of the field with some notable examples.

Table 2-1. Four-phased architectural model of CDSS evolution with notable examples140.

The four-phased sequential evolution model tracks remarkably well the chronological history of

CDSSs and shows the progressive and increasingly sophisticated attempts to ease integration of decision support systems into clinical workflows.

2.2.2.2 Clinical Decision Support Systems in Dentistry

Dental Informatics is a relatively young field compared with medical informatics, however it has seen significant evolution in the last three decades. An examination of dental informatics literature in 2003 presents important clues to the development of the field.141 CDSSs in dentistry have addressed several major areas of dental practice using different knowledge representation approaches. They have been designed to assist with diagnosis and treatment planning and to provide assistance to providers in decision-making. In a study of expert systems in 1992, the authors reviewed medical and dental expert systems to determine criteria for development and evaluation of such systems in clinical settings.142 They evaluated dental expert systems

46 developed in the 1980s for different areas of dental care including oral diagnosis143, oral radiographic diagnosis144, orthodontic treatment advice145, diagnosis of pulpal disease146, and treatment planning of dental trauma147. Their analysis indicated that systems that provide elaborate explanatory function, with user friendly interface, that are designed to fit well into existing diagnostic set-ups, host knowledge base accepted among international experts in the field, provide high specificity and sensitivity among results, and are robust enough to function with incomplete information were desirable to be well-accepted by clinical community.

Siegel, Firriolo, and Finkelstein148 reviewed literature to study the use of computers as aids in oral diagnosis. As per their research, the first example of a true CDSS for oral diagnosis was in

1973 for automated diagnosis and treatment planning of craniofacial pain.149,150 The system used algorithmic reasoning based on a weighted linear pattern recognition technique. It was capable of self-training to some extent by automatically analyzing data from clinical cases to assign weight parameters used by the inference algorithm. The design of the system was quite sophisticated for its time. Other systems reviewed include: CDSS for the diagnosis of pulpally involved teeth using Bayesian classification reasoning,146 systems for analysis and differential diagnosis of oral panoramic radiographs using Bayesian classification algorithms,151,152 the most comprehensive oral radiographic diagnostic (ORAD) system developed by White in 1987 that is still in use today,144 a Computerized Radiographic Differential Diagnostic system (COMRADD)153 for diagnosis of oral osseous radiographic abnormalities and radiographic teeth alterations using weighted and non-weighted pattern recognition algorithms, CDSS to aid in the diagnosis and treatment of dental emergencies and soft tissue lesions using algorithmic decision trees,154 CDSS for differential diagnosis of oral soft tissue lesions called Differential Diagnostic Assistant for Soft

Tissue (DDST),155 a system capable of identifying hard-and-soft-tissue anatomic landmarks used in cephalometric analysis,156 an automated system for periodontal diagnosis and research that

47 can monitor changes in the area and radiographic density of alveolar bone,157 system to analyze dental radiographs to produce quantitative description of angular periodontal bone defects,158 and an automated system159 for the precise measurement of marginal alveolar bone height based on bite-wing radiographs. These reviews was an important contribution in dental informatics literature as it shed light on the advances made in the field as well as its future potential. Table 2-2 lists CDS applications ordered chronologically and classified by knowledge representation approach used.

Table 2-2 Listing of CDS applications142,148 ordered chronologically and classified by knowledge representation scheme.

Knowledge Year CDSS Description Representation Approach 1973 Automated diagnosis of craniofacial pain150 Algorithmic reasoning based on weighted linear pattern recognition technique 1974 Automated diagnosis and treatment planning for Algorithmic reasoning craniofacial pain149 based on weighted linear pattern recognition technique 1983 Computerized endodontic diagnosis146 Bayesian classification reasoning 1983 A dental trauma diagnostic program147 Algorithmic logical classification 1986 CAREOP: A new system for computer-assisted Bayesian classification radiographic evaluation of oral pathology151 reasoning 1986 Computer-aided diagnosis of odontogenic lesions152 Bayesian classification reasoning

1986 Computer-assisted dental diagnosis154 Algorithmic decision tress 1987 A computer-controlled expert system for orthodontic Rule-based fuzzy logic advice145 1987 An expert system for oral diagnosis143 Rule-based pattern matching 1987 Computerized monitoring of alveolar bone by area and Not reported densitometric methods157 1988 Precision of computerized measurement of marginal Not reported alveolar bone height from bite-wing radiographs159 1989 Computer-aided differential diagnosis of oral Bayesian classification radiographic lesions144 reasoning 1990 COMRADD: Computerized radiographic differential Algorithmic pattern diagnosis153 recognition 1990 Computer-assisted diagnosis of soft-tissue lesions155 Not reported 1991 Automated identification of landmarks in Image processing cephalometric radiographs156 1991 Computer-aided interpretation and quantification of Image processing angular periodontal bone defects on dental radiographs158

In a subsequent and more comprehensive review of the literature on decision support systems in dentistry, White160 identified over forty decision support systems. He grouped these systems into seven subareas of dentistry (Table 2-3).

Table 2-3. Seven sub-groups of dentistry for classification of reviewed clinical decision support systems as suggested by White160.

1. Dental emergencies and trauma

2. Oro-facial pain

3. Oral medicine

4. Oral radiology

5. Orthodontics

6. Pulpal diagnosis

7. Restorative dentistry

He also classified the systems according to the knowledge representation used, including algorithmic, statistical, rule-based, and image processing systems. White described the need to closely integrate CDSSs into the practice environment, provide better support for treatment planning in addition to diagnostic support, develop reliable, centralized, and standardized databases for decision analysis, and provide real-time quality assurance of CDSSs.

In 1996, Brickley and Shepherd161 developed and tested 12 neural networks of different architectures to make lower-third-molar treatment planning decisions. Their study indicated that although neural networks seem complex and computer-intensive, they actually integrate well within a clinical environment and serve as important decision-making tools within dentistry.

Neural network expert systems may be trained with only clinical data and can be used where rule-based decision-making is not feasible.162 Use of neural networks was also evaluated in CDSS for identification of people at risk of oral cancer and pre-cancer.163 Bruins, Koole and Jolly164 proposed a dental decision support tool for the development and testing of evidence-based clinical guidelines for pretherapy oral screening and dental management of patients with head and neck cancer. In 2008, a decision support model built on data-driven Bayesian methodology was developed that described mutual relationships among multiple variables for the assessment of endodontic treatment outcomes. Receiver operating characteristic curve analysis showed that the model was highly accurate (area under curve: 0.902). Model predictions for most cases were in line with endodonists’ treatment outcomes predictions and in some cases involving

50 uncertainty, model predictions were found to be more accurate.165 Other notable examples of decision support applications in dentistry include computerized approach for applying evidence- based dentistry to caries management in dental practice17,166 and Web-based intelligent agents for treatment planning.19

2.2.3 Characteristics and Types of Clinical Decision Support Systems

In the previous section, we reviewed the evolution of CDSSs with a brief overview of knowledge representation approaches used. In this section, we delve deeper into characterizing CDSSs by their functionality and architecture. CDSS applications could be stand-alone systems, or they could interface with other systems such as electronic health records, electronic prescribing, or a digital radiology system. CDSS applications could be broadly classified into three distinct styles based on informative content and intervention format: (1) they may provide patient and case- specific treatment recommendations, prognoses, alerts, notifications, or reminders for direct action; (2) they may obtain pertinent documentation from online sources tailored to patient’s needs to aid in diagnoses or treatment planning; or (3) they may structure and display information in a way that facilitates problem-solving and decision-making as in dashboards, order sets, documentation templates, graphical displays and detailed reports.167 Each of these styles caters to a specific need and is equally important. For example, treatment recommendations, prognoses and expected outcomes information based on literature, practice and patient-directed evidence could be of immense help to practitioners in evaluating and treatment planning a patient’s case. Alerts could be critical to avoid potentially dangerous drug allergies if provider orders medication to which patient has an electronically documented allergy or to avoid potentially dangerous drug-drug interactions amongst prescribed medications.

Reminders could be helpful to clinicians in case of routine tasks such as more frequent visits in patients classified into high-risk category for developing caries, or more frequent screening for

51 oral cancer in a smoker, or for periodontal diseases in patients with diabetes, or reminders for use of prophylactic antibiotics in patients with sub-acute bacterial endocarditis. Similarly, applications providing context-aware knowledge retrieval known as “infobuttons” can effectively meet clinicians’ information needs at the point-of-care.168-170

Information within CDSS may either be conveyed via “push” mechanism where relevant information is automatically presented at appropriate times, or by “pull” mechanism where end user solicits information as needed.171 CDSS applications that “pull” information work in synchronous mode where application directly interacts with the end user who is waiting for the output of the system. A typical example is a system that provides possible treatment recommendations based on patient findings entered by the provider. Another example would be that of a system that checks for drug-drug interactions or possible patient allergy to a medication when a provider is entering a prescription order. CDSS applications that “push” information work in asynchronous mode where system is reasoning independently of any users waiting for an output. An example is a system that automatically generates point-of-care reminders for effectively managing dental patients with chronic medical conditions.

CDSS applications can also be classified by the way knowledge is represented in the system.

Knowledge representation deals with converting clinical knowledge and guidelines into computer interpretable format. Based on representation schema, Mendonça172 reviewed and classified CDSS applications into the following four generic categories: algorithmic, neural networks, probabilistic, and logical/deductive (rule-based). Berner173 described a different way of classifying these CDSS applications based on whether they are knowledge-based such as rule- based systems or whether they are non-knowledge-based such as neural networks-based systems that employ machine learning or other pattern recognition approaches. Single

52 representation schema may not be sufficient to represent a particular domain and solve a complex problem; hence many systems use more than one category of knowledge representation.174 These systems are considered hybrid systems that combine the strengths and overcome the weaknesses of individual representation schemas.

Algorithmic systems model clinical decisions in the form of decision trees and flowcharts.

Knowledge is represented with all possible, logically classified decision options for a given choice that finally lead the user to a desired end point.175 Figures 3-14 through 3-17 show graphical representation of decision trees to help with the management of endodontically treated teeth.

An example of clinical decision support system using decision trees can be found in a study by

Gerald et al.176 Study describes a decision tree they developed to assist public health workers in determining which contacts of tuberculosis patients were most likely to have a positive tuberculin skin test. Testing of their decision tree model showed a sensitivity of 94%, specificity of 28%, and a false negative rate of 7%. Notable example of clinical decision support system in dentistry using decision trees includes the Diagnostic Aid Resource Tool (DART) which assists in the diagnosis and management of oral diseases in the head and neck.177,178 Algorithmic systems do not need large sample sizes of data and can be applied across patient populations. However, they cannot reason with uncertainty and lack scalability in terms of incorporating new clinical knowledge as decision points in the system that may require substantial rewriting of the system.

Modeling decisions and treatment options in dentistry are particularly difficult since they involve risk that is continuous over time, and timing is critical in dental care. Problems such as dental caries and periodontal diseases can occur anytime, more than once, and in multiple sites.

Endodontically treated teeth that have been restored may require retreatment and restoration more than once. Under these complex real world conditions, use of conventional decision trees

53 may require unrealistic simplifying assumptions or the system may end up being too complex with decisions impossible to understand and maintain.172,179

Artificial neural networks (ANNs) are non-knowledge based adaptive CDSSs that use machine learning, a form of artificial intelligence, to learn from past experiences/examples and recognize patterns in data presented to them. In a limited way, ANNs replicate some of the functions of the brain and simulate human thinking in learning from examples.162 Research in this area has been going on since the 1940s.180 An ANN contains three layers, which include the input layer, the output layer, and the hidden intermediate layer. The input layer is the data receiver and the output layer communicates the results, while the hidden layer processes input data based on a predictable relationship configured by the network designer to determine the result. ANNs have been utilized to develop predictive expert systems in both clinical as well as non-clinical fields.

Unlike knowledge-based clinical decision support systems with knowledge derived from published literature or clinical experts, ANNs analyze patterns in patient data, to derive associations between patient’s symptoms and a diagnosis.

Applications based on ANNs learn from examples when supplied with known results for a training dataset. The system will study this information, make estimates of the correct output, compare the estimates to the given results, find patterns that match the input to the correct output, and adjust the weighted associations between input and output to produce the correct results. This iterative process is known as training the artificial neural network.181 In the example of clinical diagnosis support system, once the network has been trained, in other words, weighted associations between patient signs and symptoms to a particular diagnosis have been established with the help of the training dataset, the system can be used on new cases with incomplete facts to determine if the patient has that particular diagnosis.

ANNs can process incomplete data by inferring what the data should be and by improving results accurateness each time due to their adaptive learning nature. They do not need large datasets to make predictions about outcomes, but more comprehensive the training dataset is, the more accurate the output results are likely to be. Also, unlike deductive knowledge-based systems, ANNs eliminate the need for coding if-then rules or deriving knowledge from an expert.

However, there exist some challenges in the use of applications based on ANNs. The training process involved can be time consuming. Reliability, accountability, and maintenance of ANNs can also be of concern given the derivation process for weighting and combining data that is often not easily interpretable or justifiable in the way ANNs use certain data. 173,182 Despite these concerns, there are many applications using ANNs in the field of medicine and dentistry. Such

ANN-based systems are particularly well-suited to narrow and well-defined clinical problems such as diagnosis of appendicitis, back pain, dementia, myocardial infarction, psychiatric emergencies, sexually transmitted diseases, skin disorders, and temporal arthritis.183 ANNs have been applied in dentistry to identify people at risk of oral cancer and pre-cancer163 and for lower third molar treatment planning decisions.161

Probabilistic systems leverage known rates of diseases or problems in a population to evaluate the probabilities of the presence of those diseases given their symptoms for a specific clinical case. By using numerical estimates of disease probabilities, findings, and conditional probabilities, such systems typically apply Bayes’ rule to deal with uncertainty that is prominent in clinical decision-making. Bayesian network is a knowledge-based graphical representation of a set of random variables and their conditional probabilities, the probability of an event given the occurrence of another event. In the context of CDSSs, a Bayesian network represents the probabilistic relationships between diseases and symptoms. Bayesian approach provides a mathematical foundation for CDSSs to account for the probability that a certain patient has a

55 particular disease given the prevalence of that disease in a population with similar characteristics and findings as the patient’s.

Bayesian networks can be complex and CDSSs based on this approach can be limited by the fact that necessary probabilities are either not known or are derived from a population differing in characteristics from those of patient case at hand. Also, this approach is not practical for large complex systems given multiple symptoms as Bayesian calculations on multiple simultaneous symptoms can be overwhelming for users. However, their usefulness has been demonstrated through many applications given their ability to represent knowledge in an intuitively appealing manner. They allow causal reasoning and probabilistic inferencing that can aid CDSSs in decision-making or reflecting treatment success in complex and inter-disciplinary clinical domains. Notable examples of Bayesian network based probabilistic CDSSs include Iliad184, a general CDSS, Mammonet185, a mammography CDSS, LUCADA186 based CDSS for lung cancer care187, and SimulConsult188 for initial differential diagnosis and suggestions in the area of neurogenetics. Examples of Bayesian systems in dentistry include Oral Radiographic Differential

Diagnosis (ORAD)144, a program to evaluate the radiographic and clinical features of patients with intrabony lesions in order to assist in their identification and characterization, a system that assists pulpal diagnosis146, a system for evaluating treatment plans for dental caries189, an open case-based decision support system for diagnosis in oral pathology190, and Dental Clinical

Advisory System191,192, a program using probabilistic causal approach and Markov model for addressing prosthodontic decision problem for dental treatment with ongoing risk and changing transition probabilities over time.

Rule-based logical/deductive systems, also referred to as production rule systems, capture knowledge of domain experts as a collection of if-then rules to make decisions. Once enough of

56 these rules have been compiled into the knowledge base, current working knowledge about patient data and findings can be evaluated against the rule base by chaining rules together until a diagnosis or treatment related decision is made. Many successful CDSSs using rule-based expert systems have been developed for specialized areas in health care to aid physicians with diagnosis and treatment.119,193-202

Production rules are an intuitively attractive way to represent knowledge within a CDSS, since much of routine care rendered by physicians in daily practice follows certain well-known rules

(e.g., prescribing antihistamines as a first line of treatment for treating patients with pollen allergies). Another advantage of rule-based CDSSs is that coming up with rules helps clarify the decision-making logic and it makes it easy to store large amount of information in the system as new rules are developed. This also addresses scalability needs of CDSSs, since new rules are inferenced alongside old rules without having to rewrite the inference engine algorithms to come up with correct decisions. However, it may be difficult for an expert to transfer their knowledge into distinct rules, and the CDSS requires vast amount of a priori knowledge in order to provide correct information. This can make the systems quite complicated beyond narrow domains. Moreover, if-then rules may overemphasize certain diseases if they are not adjusted for the rarity or prevalence of particular diseases. In addition, these if-then rules have difficulty dealing with uncertainty. MYCIN203 is an example of a noteworthy early rule-based expert system based on around 600 rules to help identify the type of bacteria causing an infection.

Developers of MYCIN addressed the issue of uncertainty by engineering the concept of

“certainty factors”, numerical estimates of confidence from domain experts about facts in the knowledge base. Certainty factors ranged from -1 (false) to +1 (true) with 0 indicating no belief in either direction in the statement’s veracity. Although, MYCIN project showed sound probabilistic basis for the certainty factor rules, it also helped demonstrate the magnitude of

57 these types of systems by comparing the size of the rule base (600) to the narrow scope of the problem domain. Examples of rule-based expert systems in dentistry include RHINOS, a consultation system for diagnosis of headache and orofacial pain204, and RaPiD205 for designing removable partial dentures (RPDs). RaPiD offers CAD-style graphical interface for design automation and a critiquing model, a variant of rule-based systems, that responds to proposed diagnosis or treatment with agreement or alternatives.

Several other representational schemas have been used in clinical applications that do not fall into the four categories described above and have been successfully implemented. One such schema is case-based where contextualized information from the past is stored in the knowledge base and can be retrieved with the help of an index to solve pertinent problems in future. The key to solving problems with case-based systems is matching the current problem to past experience. This approach has some advantages over traditional knowledge-based systems when problems are open-ended with poorly defined concepts or lack good algorithms. Cases can serve as explanations and help arrive at solutions faster. However, index retrieval efficiency is chief indicator of performance, specifically in large knowledge-bases. Granularity of indices, general framework for index content, and design of case retrieval algorithm remain challenging areas for designers of case-based systems.173 Despite these challenges, case-based reasoning is a viable alternative for building flexible, knowledge-based systems as have been successfully implemented in CDSSs in medicine206-208 and dentistry209.

Frame-based structural representation of clinical knowledge into well-defined pieces is yet another approach for knowledge representation pioneered by Minsky210 in 1970s. Frames are complex data structures which contain information about the concept being described along with procedural information and information about how the frame may change over time.

Frame-based knowledge representation has advantages because frames can be created and conceptualized as self-contained entities that correspond to clinical guidelines. They facilitate knowledge engineering and provide actionable recommendations and understandable explanations for clinicians. Frames could be elements of a network, logical or hierarchical decision process. In addition to processing diagnostic and therapeutic decision-making, frames could also be designed to run database queries, make changes to the user interface or program variables. In the study on the application of frame-based knowledge representation, authors describe the successful implementation of an adequately-explicit bedside clinical decision support system for ventilator weaning.211 However, this approach comes with its own set of challenges. Complex decision-support activities with multiple steps spanning over a period of time, have to face the challenge of changing data. Russell and Norvig212 describe challenges of this “frame of reference” problem, where each step could change the underlying system in ways that make it difficult to predict the final step outcomes. Although humans can naturally reason out the “what if” scenarios, CDSSs require large knowledge bases with complex rules to mimic this type of reasoning involving a dynamic, ever changing domain representation.173

As CDSSs become more sophisticated, information needs wouldn’t just be met with representing facts in the knowledge base. These complex systems will also need access to key concepts underlying the clinical domain. Ontological engineering with its associated knowledge construct of ontologies stands to address this need. Ontologies are “formal, explicit specification of a shared conceptualization”.213 Ontologies help clearly define and standardize key concepts in a given domain, such that all parties using the ontology experience consistency in use of concepts, terms, and relationships. Since knowledge contained in ontologies is codified, it becomes easier to develop CDSSs and also metadata provided by ontologies facilitates extending and maintaining knowledge in the knowledge base. However, the biggest challenge in

59 the use of ontologies for building CDSSs is lack of standards for internal representation format, interface, or term selection. Clinical organizations using ontologies may agree upon use of specific terms in a given context, but they may end up defining these very terms differently.

Thus inconsistency could creep in the use and access of knowledge. Additionally, enormous efforts need to be invested in defining key concepts, actions, and terms for the entire problem domain which could be very complex. Although ontology-based CDSSs are not yet common, there exist excellent examples of systems that use ontological methods of knowledge representation of clinical concepts. Notable examples in the medical field are Unified Medical

Language System (UMLS)214-217 that integrates terms and concepts from over 60 disparate coding and vocabulary systems to provide a high-level conceptual framework for term categorization, Generalized Architecture for Languages, Encyclopedias, and Nomenclatures

(GALEN)218 designed to act as a terminology for use in clinical systems, and SNOMED CT219,220 that is comprehensive clinical terminology encompassing 344,000 concepts and close to 1.3 million semantic relationships. Examples in dentistry include SNODENT221, the Systematized

Nomenclature of Dentistry devised by the American Dental Association (ADA) in early 1990s.

Although in development for over two decades, SNODENT did not yield much practical value for academic or clinical dentist. This led to the initiative and development of EZcodes, a dental diagnostic terminology developed by COHRI222, a work group of dental faculty members.

EZcodes terminology consists of thirteen categories, seventy-eight subcategories, and 1,158 diagnostic terms hierarchically organized and mappable to other terminologies and ontologies such as SNODENT. EZcodes can easily be loaded into dental electronic health record systems that are in use at most dental schools leading to more widespread adoption. Development of this standardized diagnostic terminology shows promise in improving dental research, education, diagnosis-treatment link, quality of care, and provider-patient communication.223

In addition to different approaches for knowledge representation and standardization of clinical concepts, efforts have been made to develop standard knowledge representations for sharing clinical guidelines that are incorporated into CDSSs. Developed by groups of clinical experts and disseminated by professional or government organizations, clinical guidelines represent formal statements of recommended best practices with regard to specific health conditions. Examples of standards for computer-interpretable format for clinical practice guidelines include Arden

Syntax132, and Guideline Interchange Format (GLIF)133. Both these standards facilitate medical education, quality assurance, development and implementation of guideline-based CDSSs with applications in different medical domains. In the field of dentistry, Current Dental Terminology

(CDT)224 is a coding system with descriptive terms developed and updated by ADA for reporting dental services and procedures to dental benefit plans. However, our research indicates there is a lack of standardization of clinical practice guidelines across all dental disciplines.

Widespread adoption of electronic health records and computerized order entry systems in the field of medicine and dentistry have provided fertile ground for offering next-generation decision-support applications. Work done on knowledge representation approaches described in sections above along with their individual strengths and limitations, ontological engineering, coding and terminology development, machine learning, reasoning mechanisms and knowledge acquisition have opened doors for major areas of theoretical and applied research in clinical informatics. Success with the implementation of CDSSs in various clinical domains have also helped with a gradual shift in attitude and acceptance of computer decision tools by healthcare professionals. Enthusiasm of policymakers and proponents of leveraging technology as the key to addressing patient safety and quality improvement concerns has greatly helped with buy-in from provider community. However, this enthusiasm can diminish if researchers and developers

61 of CDSSs do not design products that cater to real world needs, preserve the sanctity of patient- provider relationship, and avoid disruptions in providers’ workflows.

2.2.4 Clinical Decision Support Systems in Restorative Dentistry

In this section, we review decision support systems developed for addressing needs in the field of restorative dentistry. We review the unique strengths and limitations of these systems and provide rationale for our work on a decision support system for restoration of endodontically treated teeth. Restorative dentistry is a complex area that deals with rehabilitation of the dentition to functional and aesthetic requirements of the patient. Procedures such as fillings, veneers, crowns, bridges, full and partial dentures, and dental implants are examples of routinely performed procedures under this specialty. Restorative procedures require multi- faceted care, extra chair side-sessions and sound knowledge of periodontic, orthodontic, prosthodontic and occlusal principles. Restorative decision-making can be challenging and any errors on part of the dentist could lead to increased treatment costs and/or poor treatment outcomes. Justifiably so, decision support systems for consultation while performing restorative procedures can significantly improve chances of better outcomes. One such system, RaPiD, is knowledge-based and has been developed to aid restorative dentists in the design of removable partial dentures (RPDs). System provides graphical representation of denture components that can be directly manipulated by user to build the required denture design. Users can depict arch forms of individual patients by linear movement, rotate icons representing teeth, as well as represent missing teeth. System assists with denture design by dynamically determining the size, shape, and position of many denture components to conform to the shape of abutment teeth and to the juxtaposition of other elements when run in automated mode. In critiquing mode, program utilizes rule-based expert clinical knowledge to analyze the developing design.

The rules of design expertise are represented as constraints in first order predicate logic and

62 proposed design alterations are evaluated against these rules. In case of contradictions suitable critique and alternate approaches are suggested. Resulting denture design may be printed and sent to a laboratory. This is a valuable feature offered by the system as design information necessary for accurate construction of removable partial dentures often times gets miscommunicated between dentists and dental technicians.205,225,226

White160 reviewed two other systems that provide assistance with removable partial denture

(RPD) design. Both these systems use algorithmic decision-tree based reasoning to guide the design process. Beaumont’s MacRPD227,228 facilitates design changes based on patient-specific information and assists with producing a printed drawing for use by the laboratory. Wicks and

Pennell’s229 program interacts with the user to determine necessary information such as missing teeth, periodontal health, residual ridge status, soft tissue attachments, esthetic requirements, and occlusal considerations. It then develops suitable RPD design, which can be modified by users as needed and then submitted to a laboratory with the models.

Finkeissen et al19 developed an Artificial Intelligent Dental Agent (AIDA) project for optimal decision-making related to the planning of a prosthetic construction. Designed as a rule-based expert system, it helps identify treatment alternatives with integrated justifications. This helps deliver decision-making rationale to experts, practicing dentists, and also patients. Planning suggestions are implemented in XML, such that they can be processed by Web or other dental software such as electronic heath records and appropriately presented to the respective user group. Comprehensive evaluation of the system demonstrated that up to 68% of the AIDA suggestions were deemed practicable and relevant by dental experts.

Modeling decisions and treatment options in prosthodontic dentistry are particularly difficult since they involve risk that is continuous over time, and timing is critical in dental care. Umar192

63 proposed the use of Markov Model to model prognoses for clinical problems with ongoing risk and changing transition probabilities over time. This work was suggested as continuation to probabilistic causal approach to their Dental Clinical Advisory System191, which was developed as a decision support and treatment planning tool for prosthodontic treatment. They used influence diagrams to model relationships between predictor and outcome variables in the prosthodontic decision-making process, Bayesian networks to encode existence of probabilistic influences and joint probability distribution over domain’s variables, enhanced entity relationship (EER) diagrams for knowledge representation, and relational databases for storage and retrieval of clinical data needed to perform necessary modelling calculations. System suggested the possibility of calculating risk of a patient becoming endentulous based upon the patient’s actual state within the prosthodontic cycle. Algorithms adapted from Hollenberg230 enabled determination of cost-effectiveness as well as financial cost of being in a certain state for a single or multiple cycle. Although developed as a promising Master’s thesis project, no known implementation of the system or evaluation of its success were found to be published in literature as per our research.

Using a shared decision-making model, Park, Lee, Kim and Kim proposed an ontology-based

CDSS for restorative treatment planning.20 The use of shared decision making (SDM) approach provides enhanced provider-patient communication throughout the treatment planning process and facilitates patient-centered care. System consists of an ontology of restorative treatment alternatives for SDM that captures the clinical knowledge required for treatments. Patient preferences are factored into the model using Analytic Hierarchy Process (AHP)231,232 method to help determine treatment priorities. Proposed CDSS was deployed as a web-based application for the visualization of evidence-based treatment recommendations with preference-based

64 weights. Preliminary evaluations of the system suggested quality improvements and enhanced patient satisfaction.

CDSSs discussed thus far have addressed important needs in the field of decision support for restorative treatment planning, diagnostic and therapeutic measures. However, none of these have focused on the complex domain of decision support for endodontically treated teeth.

Complexity in treatment planning restoration of such teeth is further exacerbated by multiple interdependent factors often leading to high error rates in decision-making and poor treatment outcomes. Focus of our study, is therefore, to build a CDSS that is based on expert and evidence- based guidelines in the area of restoration of endodontically treated teeth. Further, our system is also based on SDM model where patient preferences are factored into the scoring algorithm that ranks treatment recommendations. Research has indicated that eliciting patients’ symptoms and preferences can help health care professionals gain better understanding of patients’ perspectives and thus provide more effective, patient-centered care.233

2.3 Importance and Effectiveness of Technology in Dental Education

Advances in dental research have resulted into an ever increasing body of knowledge, which implies that dental professionals need to manage the flow of information rationally and productively. It is practically impossible to memorize this constant flow of new information, let alone make use of this knowledge meaningfully towards patient care. Modern dental education, therefore, emphasizes the development of skills in accessing relevant knowledge over the memorization of an ever increasing body of facts. Dental professionals must learn how to undertake evidence-based decision-making using relevant knowledge for diagnosis and treatment in a given patient situation. For these reasons, more and more emphasis is being placed on leveraging technology through computer assisted learning (CAL). Current research

65 suggests that CAL enhances learning and provides the clinician with information for decision- making at the point-of-care when treating patients.234

CDSSs are perfect tools to implement CAL, because they can provide a structured questionnaire, diagnoses, explanatory capability, textual information, and visuals on demand. However, CDSSs are not meant to replace traditional education, but rather be used more as a supplement and for self-directed studies. They have the ability to reinforce more traditional learning and create opportunities to illustrate clinical situations in an interactive way. The process has the potential to help students develop skills and knowledge in the area that the expert and decision support systems focus on. Additional advantages of CAL as discussed in the review by Schittek,

234 Mattheos, Lyon and Attström include: (1) It facilitates self-paced learning for students which means students can take their own time to go over the learning material, (2) It is not judgmental if the student makes mistakes in the learning process which means students can learn from their mistakes without embarrassment, and (3) Student can go over the learning material any number of times without computer getting tired. Moreover, anonymity of computerized learning software such as CDSSs can be helpful in providing users with treatment plan, diagnostic, and therapeutic suggestions in an interactive way based on user’s level of expertise and without being judgmental about the user. By helping to standardize terminology and diagnoses, such systems can also improve consistency in diagnosing and treating patients amongst clinical users.160

Given the benefits of CAL in dental education, we propose CDSS for facilitating evidence-based decision-making in restoration of endodontically treated teeth. Our proposed CDSS will enhance learning process for dental students and inexperienced restorative dentists. System walks the user through steps in the treatment planning process and generates treatment

66 recommendations, alerts and prognoses information. Users can interact with the system to provide patient-specific information and receive useful treatment plan information in return.

Since it is a computerized process, users can walk through the steps of the treatment planning process any number of times. They can pace the process as per their learning needs and need not fear being judged as they go through the learning process. Since the system provides explanations for the recommended treatment options, it helps users learn how decisions are made by experts. Also, since relevant information from experts and evidence-based reviews is built into the system, users need not worry about memorizing information. They can just focus on learning about the different factors critical in the decision-making process and how each factor weighs in on the suggested treatment option and its associated outcome. This is expected to result in fewer errors in the decision-making process and improved patient-centric care.

Chapter 3 System Design and Implementation

The decision support system for restoration of endodontically treated teeth is an expert system.

Expert systems are “computer programs that emulate the interaction that a person would have with a human expert for advice or a recommendation”.235 Most decision-making processes can be decomposed into smaller steps that a human expert subconsciously or automatically processes in mind to reach conclusions or solve problems. These inherent rules tend to become second nature to the human experts making decisions and only become apparent at the time the rationale for the decision is explained to someone else. These smaller decision-making factors are called heuristics or rules of thumb. Although each heuristic may not be sufficient to make any conclusion, a series of them, based on the facts provided, can help make the decision.

In order to train a non-expert, an expert must walk him/her through the series of heuristics involved in the decision-making process. However, such knowledge sharing process can be time- consuming, difficult, and impossible to reiterate for each one of the thousands of non-experts needed to be trained. Amongst those trained, likelihood of not being able to recollect necessary heuristics at the right time at point-of-care exists and could lead to major decision-making errors. This is an area in which the value of expert systems can be truly realized. Expert systems are designed to eliminate the need for remembering all of the rules that an expert develops over a long period of time based on his or her experience. For example, a doctor develops his/her understanding of diseases and their symptoms. Part of this is obtained through formal training and education and the rest through experiences and practical observations of patients.

An expert system can capture this valuable knowledge in its knowledge base. This leads to following important applications and benefits of experts systems236:

1. Help non-experts quickly make conclusions: expert systems help efficiently derive

conclusions at the point-of-care without having a non-expert remember all heuristics. They

automate the decision-making process and ensure that different people can follow the rules

consistently and avoid any room for interpretation or personal biases.

2. Scale the knowledge, while reducing learning curve: expert systems can help deliver

knowledge to a large set of people in a consistent fashion, regardless of location or time of

delivery. They can be very effective training and learning tools and also help test acquired

skills.

3. Knowledge transition by codifying the rules and decision making process: most experts do

not have the time to document their experience and rules they use to process information

and make decisions. Developing an expert system to codify these rules can help with future

knowledge transition and avoid risk of losing experts’ knowledge.

4. Allow skills of several people to be combined: leveraging evidence-based information and

combining knowledge of several experts to develop decision support rules can help

eliminate individual biases to result in a truly objective decision making tool.

Our interactive decision support system offers the benefits of expert systems described above and helps address an important gap in the area of automated decision support tools for restoration of endodontically treated teeth. In the next few sections, we describe the system architecture, process flow, rule-based knowledge management approach, and logical system components.

3.1 System Architecture

Decision support system for restoration of endodontically treated teeth is implemented using a three-tier architecture model. System’s components are segmented into three tiers of services, namely presentation tier, logic tier, and database tier. These tiers do not correspond to how system components are distributed in a physical topology, but rather to logical layers of the application. Figure 3.1 depicts each of the three tiers and components allocated to each tier:

Figure 3-1. Block diagram of CDSS components for restoration of endodontically treated teeth. High level system architecture that depicts logical tiers and main working components of the system.

1. Presentation Tier

This tier gives user access to the decision support system. User interface built using HTML,

Javascript and JQuery programming languages can be accessed using any Web browser. Client- side of the application is thin, since decision support logic and rules are handled by the middle or logic tier. This results in less overhead for the presentation tier and ease of maintenance as the user interface can be independently upgraded and maintained without having to re-write the decision support service components.

2. Logic or Middle Tier

This tier consists of components that implement the core decision-making capability of the system. Similar to any typical CDSS, it has four basic components: knowledge base, inference engine, working memory, and decision delivery module. We have leveraged Exsys Corvid’s expert system framework to implement this layer. Section 3.2 of this chapter provides rationale for choosing Corvid framework. In this section, we describe details of Exsys Corvid concepts of variables, action blocks, and command blocks. These building blocks for the components of the logic tier are required to store and process patient-specific input information, output results for the decision delivery module, rules for the knowledge base, and commands for the inference engine. These CDSS components can be hosted on an application server that has Apache Tomcat servlet container installed.

Role and functionality of CDSS components and Corvid concepts are further discussed below:

2.1 Rules and Knowledge Base

As explained in chapter 2, different types of knowledge representation approaches dictate the design of the knowledge base of a CDSS. Restoration of endodontically treated teeth is a decision-making problem that lends itself well to rule-based knowledge representation approach. Given the rules on the basis of which clinical experts make decisions and formulate restorative treatment plan, knowledge base of our expert system is designed to emulate the human expert decision-making process. Decision-making knowledge and procedures that are evidence-based and used by experts are converted to If/Then rules. For example, a simple rule based on tooth restorability would be represented as:

If Expected prognosis for tooth restoration is poor Then Prepare tooth for extraction

A large part of building our knowledge base is identifying individual rules as explained above and codifying them into the system. For rules that are too big and complex, we decompose them into smaller steps iteratively until the decision-making logic can be codified in the form of simple

If/Then rules. These rules are then combined to mirror the complexity of the decision-making logic using operators such as AND, OR, NOT, and relational operators such as less than, less than or equal to, greater than, greater than or equal to, equals, not equals, and approximately equals. Section 3.4 provides further details about rules representation based on logical system flow.

2.2 Inference Engine

Inference engine of a CDSS analyzes knowledge from the knowledge base and combines with patient-specific information to derive conclusions regarding diagnostic or therapeutic decision- making problems. Our CDSS uses Corvid inference engine to reason through rules in the system.

The system poses questions to users and analyses the information provided to determine what additional information is needed to make a decision. Inference engine determines if there is a way to derive or calculate the data from rules in the knowledge base, so that unnecessary questions are not posed to the user. However, it makes sure that relevant areas are examined in depth to gather sufficient information to narrow down choices for possible solution(s). When all of the data is provided, system reaches its conclusion, which may include several recommendations ranked in the order of their expected viability as treatment options given patient preferences and associated treatment prognosis. It is the inference engine with its reasoning capabilities that makes expert systems far more powerful, effective and maintainable for knowledge delivery than traditional programming languages. Inference engine combines rules using backward chaining and/or forward chaining techniques that are explained in further detail in the following section.

2.3 Backward Chaining/Forward Chaining Inference Methods

Backward chaining is an inference method that can be described as “goal driven” or in other words, as working backwards from goals. Setting appropriate goals or hypotheses is part of human expert’s problem-solving process and hence should be part of expert system development. Typically top-level goals are possible answers to the problem or potential recommendations that require several lower level goals be met leading up to the final solution.

Inference engine analyzes what data is required to determine if a top level goal provides appropriate solution to the user for a specific situation. This data can come from other rules, external sources such as databases and spreadsheets, or by asking the user additional questions.

For example, let’s say that the clinician’s goal is to determine if an endodontically treated teeth needs extraction.

The Inference Engine checks the rules to find one that would be relevant to making this decision:

If Expected prognosis for tooth restoration is poor Then Prepare tooth for extraction

The inference engine has found a potentially useful rule, but needs more data before the rule can be used. To make a further determination, it needs to know if “Expected prognosis for tooth restoration is poor”. Determining if this statement is true becomes the new goal of the inference engine. The original goal is not forgotten, but it is temporarily superseded by the new goal.

Inference engine now looks for a rule that can tell something about expected restoration prognosis. It finds the following rule:

If Expected crown-to-root ratio following all tooth preparation is less than 1:1 Then Expected prognosis for tooth restoration is poor

To use this rule, inference engine needs to know expected crown-to-root ratio for the tooth after all preparation. This becomes the new goal, answer to which might come from other rules or user input. Backward chaining process, thus, involves working backwards through a “chain” of goals starting from the highest to the lowest level until lower level goals are met and dropped off the chain as data becomes available and moving up towards meeting the top-level goals providing necessary recommendations to the user. Because the list of goals determines which rules are selected and used, this method is called goal-driven.

Forward chaining inference method is data-driven, rather than goal-driven. It starts with available data and uses inference rules to extract more data, either from rules or the user, to reach conclusions. The name “forward chaining” comes from the fact that the computer starts with the data, uses logic in the rules to analyze it, and reasons its way to the answer. One of the advantages of forward chaining over backward chaining is that availability of new data leads to new inferences, which could be faster for some dynamic problems in which conditions are likely to change.237 However, backward chaining is better suited for clinical decision-making problems where all required data may not be available up front and must be queried for in a focused manner. Backward chaining emulates the problem-solving process followed by human experts that involves starting with certain hypothesis, using heuristics to mine supporting evidence without asking redundant questions, and eventually reaching conclusion.

Our expert system implementation primarily uses the backward chaining inference method to formulate treatment plan and recommendations for restoration of endodontically treated teeth.

However, some rules are needed in the system to analyze available data for which system runs

74 in the forward chaining mode. Our implementation, therefore, benefits from the merits of both types of inference methods.

2.4 Corvid Variables, Logic and Command Blocks

Corvid variables are the building blocks to building expert systems in Corvid. These variables are used to define rules in Logic block, execution commands in Command block, goals for the system, and to hold data in the working memory during execution of the system.236 In our implementation of expert system for restoration of endodontically treated teeth, we have defined variables to represent concepts in restorative domain terminology. These variables are used to define if/then rules in the Logic block and system directives in the Command block.

Corvid Logic blocks are constructs that define, organize, and structure rules into logically related blocks. Rules can be defined as tree diagrams or stated as individual rules. Rules can be nested within or combined using logical operators. As an example, Figure 3-2 represents system snapshot of a Logic block defined to determine type of the tooth selected by user. Both tooth_type and tooth_site are defined as variables in the system. Tooth_site can be assigned value in the numeric range (1-32). Tooth_type can be assigned value of either “Anterior tooth” or “Posterior tooth”.

Corvid Command blocks control the procedural flow of the expert system. Logic blocks contain the heuristics for decision-making, whereas Command blocks control the manner in which Logic blocks and the rules within will be invoked to derive data for variables in the system. Whether the inference engine runs in backward chaining or forward chaining mode is also controlled through Command blocks. Displaying intermediate as well as end results is also controlled from within these blocks. As an example, Figure 3-3 represents system snapshot of a Command block defined to derive information regarding tooth’s existing restoration status. Inference engine will run in the backward chaining mode to find the appropriate Logic block containing rules to derive this variable’s value.

2.5 Working Memory

Working memory of the CDSS allows for easy storage and access of patient specific information.

Information such as patient’s medical history, dental history, existing tooth conditions, parafunctional habits, and treatment preferences are all saved in the repository. Information from the repository is used when user selects a patient’s case to obtain treatment plan recommendations. In case of a new patient, system interface directs the user to enter necessary patient information, so that it can be saved and referenced for future purposes. When the

Command block invokes execution of the inference engine, required information is either fetched from the repository or obtained from the user if found to be missing in the repository.

Fetched information is then stored in the working memory as variable values for use by the

Logic blocks as they chain through system rules.

2.6 Decision Delivery Module

Once the inference engine has chained through system rules to obtain treatment plan recommendations for restoration, results variable in the system is populated with this information to be displayed to the user. System provides all possible options for treatment recommendations by sorting the options in the order of the score attached to each option.

Scoring algorithm that we have developed takes into account patient’s preferences and expected prognoses of the treatment options. Algorithm’s rationale is also presented to the user in the form of alerts and prognosis information, so that dental professionals learn how experts make decisions.

3. Database tier

This tier allows for easy storage and retrieval of information required by the system to make treatment planning decisions as well as system generated treatment recommendations. This saves the user from having to enter the same information each time treatment planning needs to be done for a patient seen before. Being able to save system generated treatment recommendation results can be useful for referencing decisions made in the past. This layer implements data access components that hides the complexity of interfacing with databases away from the presentation and middle tiers. Current version of CDSS uses Oracle 11g Express

Edition that contains fictitious patient data. Future versions of the CDSS can easily interface with real electronic health record (EHR) system database containing real patient information. Figure

3-4 depicts entity-relationship model for the CDSS with current version saving and retrieving data pertaining to PatientChart and TreatmentPlan database tables. Future versions interfacing with a real-world database can inherit the data model of the respective EHR system.

CDSS - Data Model Legend - Cardinality One to One Prov_fname Zero or Many Title Prov_lname Zero or One Prov_ID Legend – Scope of Work Current implementation Prov_License Provider Future scope Specialty

identifies formulates reviews

Alert Pt_chart Hist_ID

Prob_ID Tx Plan Plan Treatment Patient Problems Timestamp Plan History Hist_Time stamp Prob Alt Txplan Description Tool Site

Score Pt_ID Analysis Prov_ID

exhibits accepts has

Pt_lname Pt_fname ISA ISA

Gender Address Dental Medical Patient Chart Is assigned Patient History History Birthdate

Pt_chart Age Pt_ID Pt_chart Existing Carious Stability Restorations Lesions Phone InAsdudrraenscse Number

Figure 3-4. Illustrative entity-relationship diagram describing information aspects of clinical decision support system.

The three-tier approach for system architecture provides several benefits such as flexibility, ease of maintenance, scalability, and better management of application environment. Services and components in each tier can be designed and deployed with greater flexibility to meet changing requirements. Updates can be made to components in any layer without having to rewrite or rewire with components in the other layers providing greater ease of maintenance. Each tier can scale horizontally. For example, load-balancing the presentation tier amongst multiple servers to

79 handle application’s growing user base without adding servers to the logic and database tiers.

Better flexibility, ease of maintenance, and scalability in turns allows for better management of the application environment.

3.2 Rationale for Choosing Exsys Corvid

Exsys Corvid provides a powerful and flexible framework for automated decision support systems. Logical rules and procedural steps used by an expert to make decisions can be efficiently codified into the system in a way that is easy to read, understand and maintain. Once the knowledge base is established, inference engine provided by the framework emulates the problem-solving logic and process of domain experts. Framework allows development of a Web- based interactive interface that users can consult with as if they were talking to an expert. CDSSs developed using this technology have the potential to effectively train and educate users to perform at the level of expert in rendering patient care and thereby improve treatment outcomes.

Design features that are the strengths of the framework are further explored below:

1. Object-oriented Structure

Corvid uses an object-oriented (OO) approach to system design. Rules can be defined using variables that have associated methods and properties. This provides system developers many advantages of OO approach, and yet complexity of OO programming is well-encapsulated within and hidden from users.

2. Powerful Inference Engine

Corvid provides a fast and efficient inference engine that runs all the rules of the decision support system. It supports both forward as well as backward chaining and allows combining both these approaches providing benefits of both. The inference engine can support

80 probabilistic outcomes, which is very important for the clinical domain where several uncertainties need to be factored into the decision-making process.

3. Probabilistic Model

Corvid allows its variables to have specified confidence or certainty factors, which allows for various levels of confidence in potential outcomes of a problem. This support for probabilistic logic allows systems to find the “best fit” solution by probabilistically ranking multiple possible solutions. Having a probabilistic approach to solve problem mimics the real world experts.

4. Integration with Databases

Corvid’s open interface and ability to integrate with databases allows Corvid based systems to be easily plugged into existing IT environments or meet the needs of the real world in creating new environments. This is a huge asset for system developers who do not have to bother with writing cumbersome logic themselves as interfacing with databases is supported by the framework itself.

5. Friendly and Flexible User Interface

Corvid provides a simple, easy-to-use and feature-rich development environment. The decision- making logic is stated in If/Then rules, which are intuitive to write. The rules are written in

English and Algebra, making them easy to read, understand, and maintain. This ensures easy maintenance of logic and also enables quick updates. Being able to easily maintain and update

CDSSs is a huge necessity given the amount of new knowledge being generated in the clinical domain.

3.3 User Interaction Process Flow

Overall process flow of user interaction with the system is depicted in Figure 3-5 as swim lane flowchart. Any dental professional, either student or a licensed dentist could use the system for

81 assistance with treatment planning restoration of endodontically treated teeth. Initial step in the process when the patient is being examined is for the provider to collect patient’s dental and medical history, tooth site, and extent of existing restorations. Next step in the workflow is for the system to chain through rules in the knowledge base to determine tooth restorability on the basis of the basic patient data entered by the provider. If the basic information is not sufficient to make a decision, system prompts provider to enter additional relevant information as may be necessary based on patient’s specific situation and rules in the knowledge base. Given the presenting conditions, it may become necessary to extract the tooth if it cannot be easily restored with good prognosis for the tooth itself and the rest of the natural dentition, and without significant costs. If tooth is deemed non-restorable, system recommends extraction as the most viable treatment plan option. If deemed restorable, system prompts user to provide further relevant information including patient’s general preferences about the treatment process such as aversion to tooth extraction, risk tolerance, cost threshold, and restoration longevity expectations to formulate a treatment plan with best prognosis. These are the various possibilities that the system evaluates for the clinical user and provides as recommendations with explanation to throw light on the underlying heuristics of the decision-making process.

Since the system can be easily launched at the operatory, provider can enter patient data and preferences into the system as it is being collected at point-of-care with the patient in chair.

If it is determined that the tooth can be restored with reasonable or excellent prognosis and while meeting the patient preferences, system proceeds to the third and final step in the process workflow. This is an important step where heuristics pertaining to optimal treatment choice for successful restoration of endodontically treated teeth are processed. System uses logical, rule-based deduction to suggest optimal treatment recommendations, alerts and prognoses information. Suggestions are based on expert knowledge, evidence-based data and rules codified into the knowledge base. A scoring algorithm weighs between several options in light of patient preferences given the specific patient condition. It ranks options based on score and presents them as recommendations with associated prognosis information to the dental professional. If there are any alerts or warnings related to the choice of treatment option and its implication on patient’s treatment outcome, those are presented as well. Final step in the

83 workflow is for the provider to save the system generated treatment to the database for future references.

All data needed for processing of rules and to make decisions about treatment plan are stored in system variables. Values for the variables are set either by forward and backward chaining of rules in the knowledge base or by posing questions to the user if values cannot be determined by logical inference methods. System interactively steps through the treatment planning and decision-making process as dental experts would traditionally step through the problem-solving exercise to reach conclusions about the treatment plan. Dental professionals, thus, learn from the system how experts think through the treatment planning process to make decisions. They can interactively step through the system either at chair-side or at their own place, pace and time. Next section discusses details of system design and implementation.

3.4 System Design and Implementation

3.4.1 Logical System Flow

Our approach for codifying rules in the knowledge base and the order in which rules are executed by the CDSS in building the treatment plan can be logically expressed as three distinct phases as shown in Figure 3-6. By structuring expert and evidence-based guidelines as rules into distinct logical blocks within one of the three phases, we have followed object-oriented (OO) programming concepts that promote high cohesion and loose coupling. Software quality metrics of coupling and cohesion were invented by Larry Constantine in the late 1960s while studying good programming practices that reduced maintenance and modification costs.238 In computer programming, cohesion refers to “the degree to which the elements of a module belong together”.239 It was originally described as binding or “the measure of cohesiveness of a module”.240,241 Coupling, on the other hand, is defined as “the measure of strength of association established by a connection from one module to another”.240 In other words,

84 coupling is a measure of interdependence between classes. Coupling and cohesion are contrasting terms with high cohesion correlating to loose coupling, and vice versa. High cohesion is desirable because it means that elements of the block are strongly related to

Figure 3-6. Logical process flow diagram representing distinct phases the CDSS steps through while processing rules to build most optimal treatment plan. each other and the block is meant for a specific function that it does well. Similarly, loose coupling is desirable because it means that the blocks work more independently of each other, minimizing the cascading effect of changes in one block necessitating changes in others. High cohesion promotes software reusability and understandability, whereas loose coupling promotes high readability and maintainability.

Following sections offer further details about each of the three phases depicted in Figure 3-6.

Phase ordering in the process flow is designed to align with experts’ typical workflow when treating planning restoration of endodontically treated teeth. Workflow begins with collecting

85 necessary patient data, proceeds to the treatment planning phase, follows up with shared decision-making between patient and provider and finally ends with determining best possible restorative treatment plan.

1. Data collection phase:

Most important function of this phase is to collect basic patient data such patient’s medical stability, tooth site and extent of existing restorations. If patient’s medical condition during this phase is found to be unstable, CDSS does not proceed to the next (treatment planning) phase

Figure 3-7. Screenshots of Command and Logic blocks containing rules pertaining to the data collection phase. and instead jumps to the end of the workflow to provide recommendation. CDSS recommends provider to reschedule patient and refer him/her for additional medical assessment prior to dental treatment to avoid any untoward or adverse treatment outcomes. Thorough and extensive evaluation of patient's underlying medical condition(s), impact on physiology, and

86 his/her response to dental management and post-dental treatment healing is critical to plan for appropriate restorative care.

If patient’s medical condition is found to be stable, CDSS infers the tooth type from tooth site data by backward chaining through rules in the “Tooth Type Evaluation” Logic block shown in

Figure 3.7. Based on the tooth type and extent of existing restorations information, CDSS proceeds to the treatment planning phase.

2. Treatment planning phase:

Most important function of this phase is to determine if the tooth is restorable and evaluate all relevant factors to determine most optimal treatment recommendation. Processing of rules within the treatment planning phase is further segregated into three distinct sub-processes, namely, Primary Evaluation, Secondary Evaluation, and Gray Area Evaluation. Rules in the primary evaluation phase are invoked first, followed by rules in the secondary evaluation phase.

Depending on the value of crown-to-root and/or crown-to-post ratio variables established during secondary evaluation phase, rules in the gray area evaluation phase are executed last within the treatment planning phase.

Figure 3-8. Screenshot of Logic block containing rules pertaining to primary evaluation sub-process within treatment planning phase.

2.1 Primary evaluation phase:

Most important function of the primary evaluation phase is to determine the amount of remaining non-carious tooth structure available above the bone level and the amount of dentin wall thickness over the length of the remaining coronal tooth structure. Values for both these variables are determined by prompting the user for accurate information. User would obtain this information by examining radiographs and patient’s mouth. If either of the values are not at the minimum desired level, rules in the “Primary Evaluation” logic block as shown in Figure 3-8 prompt user to consider crown lengthening or forced orthodontic eruption and enter values for the variables again after the projected crown lengthening or forced orthodontic eruption operations. Only after both values are determined to be satisfactory and better than or at the minimum desired levels, CDSS proceeds to the secondary evaluation phase. Otherwise, it jumps

88 to the end of the process flow to recommend tooth extraction since restoration prognosis of the tooth without sufficient remaining coronal tooth structure and/or dentin wall thickness is very poor.

Figure 3-9. Screenshot of Logic block containing rules pertaining to secondary evaluation sub-process within treatment planning phase.

2.2 Secondary evaluation phase:

Most important function of the secondary evaluation phase (Figure 3-9) is to determine the amount of ferrule effect, crown-to-root, and/or crown-to-post ratio. If the pre-operative (prior to crown lengthening or forced orthodontic eruption) remaining coronal tooth structure and dentin wall thickness values processed during primary evaluation phase are sufficient, remaining ferrule effect is evaluated to determine treatment plan as shown in the “Ferrule Evaluation” logic block (Figure 3-10).

Figure 3-10. Screenshot of Logic block containing rules pertaining to secondary evaluation sub-process within treatment planning phase.

If pre-operative remaining coronal tooth structure and post-operative dentin wall thickness are found to be sufficient, crown-to-root ratio is evaluated to determine treatment plan as shown in the “Crown-to-root Ratio Evaluation” logic block (Figure 3-10). If pre-operative remaining coronal tooth structure is found to be insufficient while processing rules in the primary evaluation phase, crown-to-post ratio as shown in the “Crown-to-post Ratio Evaluation” logic block (Figure 3-10) is evaluated in addition to the crown-to-root ratio.

Figure 3-11. Screenshot of Logic block containing rules pertaining to gray area evaluation sub-process within treatment planning phase.

2.3 Gray area evaluation phase:

If either the crown-to-root or the crown-to-post ratio is 1:1, rules in the “Gray Area Evaluation” logic block (as shown in Figure 3-11) are processed to determine tooth restorability prospects in light of rest of the dentition. Important factors that must be considered and that affect the restoration prognosis score include: (1) whether tooth is an abutment for removable partial denture (RPD) or fixed partial denture (FPD) or none, (2) whether tooth’s opposing occlusion is natural tooth, implant or complete denture, (3) patient’s caries risk rate is low (zero new carious lesions in the past six months), medium (one to two new carious lesions in past six months), or high (three or more carious lesions in past six months), and (4) patient is a non-compliant bruxer, bruxer but compliant with night guard, or bruxer with complete denture and compliant with removing denture at night.

If the score after considering these factors is over 40 (out of 100), tooth’s restoration prognosis is considered satisfactory to good (depending on actual score value). In this case, CDSS skips the shared decision making phase as the treatment plan established during execution of primary and secondary rules evaluation can be recommended to the patient.

If the score is below 10 (out of 100), tooth’s restoration prognosis score is considered very poor.

In this case as well, CDSS skips the shared decision making phase and jumps straight to the end of the process flow to recommend tooth extraction.

If the score is between 10 and 40 (out of 100), tooth’s restoration prognosis is considered guarded or questionable. In this case, CDSS proceeds to the next (shared decision making) phase in the process flow. Considering patient preferences will help provider make the decision regarding extraction or restoration with most optimal plan in light of presenting conditions and patient’s preferences.

3. Shared decision-making phase:

Most important function of the shared decision-making phase is to determine patient’s preferences and expectations regarding treatment options and outcomes. After processing rules in the treatment planning phase, if there is no clear and optimal treatment plan option; CDSS helps provider make decisions by factoring in patient preferences.

Figure 3-12. Screenshot of Logic block containing rules pertaining to shared decision-making phase.

Rules regarding patient’s aversion to tooth extraction, restoration longevity expectations, risk tolerance and treatment cost threshold are processed in the “Shared decision-making” Logic block as shown in Figure 3-12. Either treatment recommendation formulated during treatment planning phase is selected (although it has poor or questionable prognosis, but based on patient’s preferences) or an alternate treatment plan is developed in this phase. CDSS then proceeds to the end of the process flow to display the primary treatment plan recommendation, alternate treatment plan recommendation, alerts regarding treatment plan option based on patient’s condition, treatment prognosis information, and detailed restoration prognosis score analyses to help provider understand decision-making rationale used by the expert system and share it with the patient.

Figure 3-13. Screenshot of Command block containing rules to save system generated treatment plan to the database for future references.

In the final step of the workflow, provider is presented with the option to save the entire treatment plan developed for the patient to the database for future references as per rules in the “SaveAndReturn” Command block shown in figure 3-13.

3.4.2 Rule-based Knowledge Representation

In the previous section, we looked at logical process flow of the CDSS in evaluating the most optimal treatment plan option for the endodontically treated tooth. In this section, we look at the specifics of rules pertaining to different values of the determining factors and rule chaining modes. We use combination of forward and backward chaining. Forward chaining is used between and backward chaining is used within Logic blocks.

There are a variety of criteria and considerations that need to be made in order to determine the appropriate restorative treatment plan for a patient with endodontically treated teeth.

Large number of criteria and various possible options associated with them make the decision- making process extremely complex. For example, just a basic evaluation of patient’s tooth involves looking at several different factors and understanding implications of each on potential restoration. In some cases, it may be wise to avoid restoration and plan in favor of an extraction.

Given the complexity of the problem, we have tabulated a comprehensive set of criteria that must be considered in determining the right treatment option. We have categorized these criteria into two high level categories of “basic” criteria and “gray area” criteria. The second set of criteria are not applicable in all situations, but only under certain complex situations where the basic set of criteria do not provide a deterministic treatment option.

Table 3-1. Key considerations for determining optimal treatment recommendation.

Criteria Description / Key Questions Possible Options BASIC CRITERIA Tooth Number / Where is tooth located? Location  [6-11], [22-27] => Anterior Location and access of the tooth has  [1-5], [12-16], [17-21], [28-32] => Posterior different implications (anterior vs. posterior) Extent of How much restoration is needed?  None to minimal Restoration Depending on the condition of the  Moderate to significant tooth, the treatment can be very different Remaining At the proposed crown finishing 1. At least 4.5 mm Coronal Tooth line, what is the remaining non- 2. Less than 4.5 mm Structure carious tooth structure above the [Biologic width (2.5 mm) + ferrule (2 mm) = 4.5 bone level? mm] Dentin Wall At the proposed crown finishing  At least 1 mm Thickness line, what is the dentin wall  Less than 1 mm thickness over the length of coronal tooth structure? Ferrule Effect How much of a ferrule effect is  Anterior teeth present? Ferrule provides necessary . At least 6 mm support structure for restoration. . Less than 6 mm  Posterior teeth . At least 4 mm . Less than 4 mm Crown-to-root What is the post-operative crown-  Less than 1:1 Ratio to-root ratio?  1:1, (see gray area - additional considerations)  1:1.5  1:2 Crown-to-post Is there sufficient tooth height and a  Less than 1:1 Ratio core is enough or is post required?  1:1, (see gray area - additional considerations) If post is required, what will crown-  1:1.5 to-post ratio be?  1:2 GRAY AREA Additional considerations Abutment Is tooth an abutment for fixed or 3. Abutment for Removable Partial Denture (RPD) Considerations removable bridge? Such teeth are 4. Abutment for Fixed Partial Denture (FPD) subjected to greater forces that 5. None affect restoration prognosis Opposing What is the opposing occlusion 6. Complete denture (least force, improves Occlusion against the restored tooth? prognosis) Stronger the occlusion, weaker the 7. Implant (greatest force, degrades prognosis) chances of restoration success and 8. Natural tooth (neutral) longevity

Bruxism Is patient a bruxer (i.e., grinds or 9. Compliant with night guard clenches teeth)? Such para- 10. Non-compliant functional tendencies have negative 11. Bruxer with complete denture, compliant by implications on restoration removing at night prognosis Caries Rate What is the incidence rate of new 12. 0 new carious lesions in the past 6 months carious lesions and/or restorations 13. 1 to 2 new carious lesions in the past 6 months in the past 6 months? Higher the 14. 3 or more new carious lesions in the past 6 caries rate, weaker the chances of months restoration success and longevity

Rule-based knowledge representation is the most logical and suitable knowledge representation approach for the design of the expert system given all the factors involved in the decision- making process, their possible values and rules surrounding each value and its influence on the restoration prognosis of the tooth. Decision-making logic expressed as rules in the knowledge base is expressed using the decision trees depicted in figures 3-14 through 3-17.

Anterior Tooth

None to minimal Moderate to significant Extent of Restoration ? Tx Plan: Simple intra-coronal restoration <4.5mm RCTS >= >= 4.5mm 4.5mm ?

Do not <4.5mm RCTS after B proposed CL or know OE? Tx Plan: Prepare tooth for evaluation and more Tx Plan: definitive recommendation Extraction

>= 4.5mm

< 1mm DW after proposed CL or OE? Tx Plan: Extraction >= 1mm

Figure 3-14-A. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated anterior teeth.

<1:1 >= 1:1.5 <1:1 CR Ratio? CP Ratio?

>= 1:1.5 1:1 1:1 Tx Plan: CL or Tx Plan: See gray area See gray area Tx Plan: OE + post + Extraction decision tree decision tree Extraction core + crown

< 1mm DW on either >= 1mm side of RCTS ?

Do not know Remaining Tx Plan: Prepare tooth for ferrule effect? evaluation and more definitive recommendation

< 6mm >= 6mm No Is there enough Yes root in bone for CL or OE to obtain DW >=1mm? Tx Plan: core + Tx Plan: post + core crown + crown

CR Ratio?

Tx Plan: <1:1 >= 1:1.5 Extraction 1:1 Tx Plan: CL or Tx Plan: See gray area OE + Core + Extraction decision tree Crown

Figure 3-14-B. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated anterior teeth.

Posterior Tooth

None to minimal Moderate to significant Extent of Restoration ? Tx Plan: Intra- + Extra-coronal restoration <4.5mm RCTS >= >= 4.5mm 4.5mm ?

Do not <4.5mm RCTS after B proposed CL or know OE? Tx Plan: Prepare tooth for evaluation and more Tx Plan: definitive recommendation Extraction

>= 4.5mm

< 1mm DW after proposed CL or OE? Tx Plan: Extraction >= 1mm

Figure 3-15-A. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated posterior teeth.

<1:1 >= 1:1.5 <1:1 CR Ratio? CP Ratio?

>= 1:1.5 1:1 1:1 Tx Plan: CL or Tx Plan: See gray area See gray area Tx Plan: OE + post + Extraction decision tree decision tree Extraction core + crown

< 1mm DW on either >= 1mm side of RCTS ?

Do not know Remaining Tx Plan: Prepare tooth for ferrule effect? evaluation and more definitive recommendation

< 4mm >= 4mm No Is there enough Yes root in bone for CL or OE to obtain DW >=1mm? Tx Plan: core + Tx Plan: post + core crown + crown

CR Ratio?

Tx Plan: <1:1 >= 1:1.5 Extraction 1:1 Tx Plan: CL or Tx Plan: See gray area OE + Core + Extraction decision tree Crown

Figure 3-15-B. Decision tree depicting primary and secondary rules evaluation for management of endodontically treated posterior teeth.

100

Starting Score = 100

CR Ratio or CP Ratio is 1:1 C

Tooth is an abutment? Caries rate? No Yes for RPD Yes for FPD 3 or more in past 6 Δ Score = -20 Δ Score = -10 0 in past 6 1 to 2 in past months months 6 months Δ Score = -15 Δ Score = -35 Opposing occlusion? Natural Complete tooth Implant denture Δ Score = -10 Δ Score = +30 Total score?

Less Between Greater Patient is than 10 10 and 40 than 40 bruxer? Yes and compliant with Yes and Yes and night guard and No change to Tx non compliant opposite tooth is See shared plan set after compliant with night Tx Plan: complete decision- primary and guard denture Extraction making block secondary rules Δ Score = -75 Δ Score = -15 Δ Score = -5 evaluation

Figure 3-16. Decision tree depicting gray area logic for management of endodontically treated teeth when crown- to-root ratio or crown-to-post ratio is 1:1.

Table 3-2 summarizes the factors that contribute either positively or negatively to the restoration prognosis score which is initialized to 100. As factors are evaluated and rules are

101 processed, the prognosis score gets updated and the final value is used to choose between different treatment plan options.

Table 3-2. Table indicating how rules affect restoration prognosis score (initialized to 100).

Factor Score Contribution Moderate to significant existing restorations -10 Pre-op dentin wall thickness < 1 mm -10 Insufficient root in bone -90 Remaining coronal tooth structure < 4.5 mm -20 Post-op dentin wall thickness < 1 mm -70 Remaining ferrule effect < 6 mm (anterior teeth) -10 Remaining ferrule effect < 4 mm (posterior teeth) -10 Crown-to-root ratio < 1:1 -90 Crown-to-root ratio = 1:1.5 -5 Crown-to-post ratio < 1:1 -90 Crown-to-post ratio = 1:1.5 -5 Tooth is abutment for RPD -20 Tooth is abutment for FPD -10 Opposing occlusion is complete denture +30 Opposing occlusion is implant -10 Patient is non-compliant bruxer -75 Patient is bruxer, but compliant with wearing night guard -15 Patient is bruxer but compliant with wearing night guard and -5 opposing occlusion is complete denture Patient has 1-2 new carious lesions in past 6 months -15 Patient has 3 or more carious lesions in past 6 months -35

102

103

Chapter 4 Results and Case Studies

4.1 Preliminary System Testing

In this section, we present the results of our preliminary system testing that illustrates the decision criteria and the set of rules that are used by the CDSS to make a treatment recommendation for restoration of endodontically treated teeth. The case examples are designed to demonstrate the ability of the CDSS to form treatment decisions based on expert and evidence-based guidelines for a range of different scenarios and presenting conditions considering the tooth alone in vacuum, in context with the rest of the natural dentition and also considering patient preferences.

Case Example 1: Endodontically treated molar with moderate to significant restorations

Table 4-1. Summary of case description and screenshots – case example 1.

Case Description . Patient has endodontically treated molar (site: 30) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is greater than 4.5 mm . Remaining dentin wall thickness at the proposed finishing line is greater than 1mm . Existing ferrule on the tooth measures at least 4 mm CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

104

105

106

107

Case Example 2: Endodontically treated incisor with none to minimal restorations

Table 4-2. Summary of case description and screenshots – case example 2.

Case Description . Patient has endodontically treated incisor (site: 9) and is medically stable . Existing restorations are none to minimal CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Step 5 similar to step 8 of case example 1

108

Case Example 3: Endodontically treated canine with moderate to significant restorations

Table 4-3. Summary of case description and screenshots – case example 3.

Case Description . Patient has endodontically treated canine (site: 27) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is greater than 4.5 mm . Remaining dentin wall thickness at the proposed finishing line is greater than 1mm . Existing ferrule on the tooth measures less than 6 mm CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1, 2, and 8 similar to those of case example 1

109

110

111

Case Example 4: Endodontically treated premolar with moderate to significant restorations

Table 4-4. Summary of case description and screenshots – case example 4.

Case Description . Patient has endodontically treated premolar (site: 20) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is less than 4.5 mm . Remaining dentin wall thickness after projected crown lengthening/orthodontic eruption is less than 1mm CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Step 7 similar to step 8 of case example 1

112

113

114

Case Example 5: Endodontically treated incisor with moderate to significant restorations

Table 4-5. Summary of case description and screenshots – case example 5.

Case Description . Patient has endodontically treated incisor (site: 10) and is currently medically unstable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is greater than 4.5 mm . Remaining dentin wall thickness at the proposed finishing line is less than 1mm . There is enough root in the bone to perform crown lengthening/orthodontic eruption . Crown-to-root ratio after projected crown lengthening/orthodontic eruption appears to be greater than 1:1 CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Step 3 similar to step 8 of case example 1

115

Case Example 6: Endodontically treated molar with moderate to significant restorations

Table 4-6. Summary of case description and screenshots – case example 6.

Case Description . Patient has endodontically treated molar (site: 3) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is less than 4.5 mm . Remaining dentin wall thickness after proposed crown lengthening/orthodontic eruption is more than 1 mm . Crown-to-root ratio after projected crown lengthening/orthodontic eruption is 1:1 . Opposing occlusion is complete denture, patient is bruxer and compliant with removing denture at night, has no new carious lesions in the past 6 months, tooth is not an abutment for RPD or FPD . Crown-to-post ratio after projected crown lengthening/orthodontic eruption is greater than 1:1 CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Step 4 similar to that of case example 4 Step 13 similar to step 8 of case example 1

116

117

118

119

120

Case Example 7: Endodontically treated premolar with moderate to significant restorations

Table 4-7. Summary of case description and screenshots – case example 7.

Case Description . Patient has endodontically treated premolar (site: 29) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is more than 4.5 mm . Remaining dentin wall thickness at the proposed finishing line is less than 1mm . There is enough root in the bone to perform crown lengthening/orthodontic eruption . Crown-to-root ratio after projected crown lengthening/orthodontic eruption is less than 1:1 CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Step 4 similar to that of case example 3 Step 9 similar to step 8 of case example 1

121

122

123

Case Example 8: Endodontically treated canine with moderate to significant restorations

Table 4-8. Summary of case description and screenshots – case example 8.

Case Description . Patient has endodontically treated canine (site: 11) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is less than 4.5 mm . Remaining dentin wall thickness after proposed crown lengthening/orthodontic eruption is more than 1 mm . Crown-to-root ratio after projected crown lengthening/orthodontic eruption is greater than1:1 . Crown-to-post ratio after projected crown lengthening/orthodontic eruption is 1:1 . Tooth is an abutment for RPD, opposing occlusion is natural tooth, patient is bruxer and compliant with night guard, has 3 new carious lesions in the past 6 months CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Step 4 similar to that of case example 4 Step 5 similar to that of case example 6 Step 13 similar to step 8 of case example 1

124

125

126

Case Example 9: Endodontically treated molar with moderate to significant restorations

Table 4-9. Summary of case description and screenshots – case example 9.

Case Description . Patient has endodontically treated premolar (site: 5) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is more than 4.5 mm

127

. Remaining dentin wall thickness at the proposed finishing line is not clearly known CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Step 4 similar to that of case example 3

128

129

Case Example 10: Endodontically treated premolar with moderate to significant restorations

Table 4-10. Summary of case description and screenshots – case example 10.

Case Description . Patient has endodontically treated molar (site: 18) and is medically stable . Existing restorations are moderate to significant . Remaining coronal tooth structure at the proposed finishing line is less than 4.5 mm . Remaining dentin wall thickness after proposed crown lengthening/orthodontic eruption is more than 1 mm . Crown-to-root ratio after projected crown lengthening/orthodontic eruption is 1:1 . Tooth is an abutment for FPD, opposing occlusion is implant, patient is bruxer and compliant with night guard, has 2 new carious lesions in the past 6 months, . Crown-to-post ratio after projected crown lengthening/orthodontic eruption is greater than 1:1 . Patient has high aversion to tooth extraction, low risk tolerance, expects good restoration longevity, and can afford higher cost for treatment CDSS Steps Illustrating Decision-Making Process & Final Treatment Plan Recommendation

Steps 1 and 2 similar to those of case example 1 Steps 4, 5, 6, and 11 similar to those of case example 6 Step 14 similar to step 8 of case example 1

130

131

132

4.2 System Validation Using Patient Radiographs

We validated system generated treatment plan results by comparing them with expert generated treatment plan by using real patient radiographs. Tables 4-11 through 4-20 summarize our system validation results:

133

Case Scenario 1:

Scenario evaluation and decision-making factors:

• Anterior tooth with endodontic access opening

• None to minimal existing restorations

Table 4-11. Comparison of system generated treatment plan with expert opinion – case scenario 1.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable (intra-coronal restoration)

Expert Opinion

• Restorable (simple intra-coronal restoration)

Case Scenario 2:

Scenario evaluation and decision-making factors:

• Posterior tooth with endodontic access opening

• None to minimal existing restorations

134

Table 4-12. Comparison of system generated treatment plan with expert opinion – case scenario 2.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable (intra-coronal + extra-coronal restoration)

Expert Opinion

• Restorable (intra-coronal + extra-coronal restoration)

Case Scenario 3:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major existing restoration

• Remaining coronal tooth structure > 4.5mm

• Dentin wall thickness > 1mm

• Remaining ferrule effect < 4 mm

135

Table 4-13. Comparison of system generated treatment plan with expert opinion – case scenario 3.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable (post + core + crown)

Expert Opinion

• Restorable (post + core + crown) • Might need crown lengthening or forced orthodontic eruption after evaluating actual tooth dimensions

Case Scenario 4:

Scenario evaluation and decision-making factors:

• Anterior tooth

• Major existing restoration

• No ferrule effect (core and crown finishing line are at the same level)

• Projected remaining coronal tooth structure and dentin wall thickness after proposed

crown lengthening or forced orthodontic eruption are 4.5 (2.5 for Biologic Width + 2 for

ferrule) mm and 1 mm on either side respectively

136

Table 4-14. Comparison of system generated treatment plan with expert opinion – case scenario 4.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable (crown lengthening (CL) or forced orthodontic eruption (OE) + post + core + crown)

Expert Opinion

• Restorable (CL or OE + post + core + crown)

Case Scenario 5:

Scenario evaluation and decision-making factors:

• Anterior tooth

• Major existing restoration

• Remaining coronal tooth structure > 4.5mm

• Remaining dentin wall thickness < 1mm

• Crown lengthening or forced orthodontic eruption cannot be performed as there isn’t

enough root in the bone to have sufficient dentin wall thickness

137

Table 4-15. Comparison of system generated treatment plan with expert opinion – case scenario 5.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Non-restorable (extraction as restoration prognosis is very poor)

Expert Opinion

• Non-restorable (Extraction) • Not enough width of root to perform CL or OE

Case Scenario 6:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major existing restorations

138

• Remaining non-carious tooth structure above bone level < 4.5 mm

• Requires caries excavation followed with crown lengthening or forced orthodontic

eruption to obtain minimum desired biologic width (2.5 mm), ferrule effect (2 mm), and

dentin wall thickness (1 mm)

• Projected Crown-to-root and crown-to-post ratios appear to be satisfactory

Table 4-16. Comparison of system generated treatment plan with expert opinion – case scenario 6.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable (crown lengthening or forced orthodontic eruption + post + core + crown)

Expert Opinion

• Restorable (CL or OE on the distal + post + core + crown)

139

Case Scenario 7:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major tooth loss

• Projected remaining coronal tooth structure after proposed crown lengthening or

forced orthodontic eruption is < 4.5 mm

Table 4-17. Comparison of system generated treatment plan with expert opinion – case scenario 7.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Non-restorable (extraction as restoration prognosis is very poor)

Expert Opinion

• Non-restorable (extraction)

Case Scenario 8:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major existing restorations

• Remaining non-carious tooth structure above bone level < 4.5 mm

140

• Requires caries excavation followed with crown lengthening or forced orthodontic

eruption to obtain minimum desired biologic width (2.5 mm), ferrule effect (2 mm), and

dentin wall thickness (1 mm)

• Projected Crown-to-root and crown-to-post ratios appear to be 1:1

• Evaluate gray area factors (abutment, opposing occlusion, caries risk rate and bruxism)

• If restoration prognosis is questionable, evaluate patient preferences for cost threshold,

risk tolerance, restoration longevity expectations and aversion to tooth extraction

Table 4-18. Comparison of system generated treatment plan with expert opinion – case scenario 8.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Crown lengthening or forced orthodontic eruption + post + core + crown (If restoration prognosis is fair to good) • Extraction (if restoration prognosis is very poor or if prognosis is guarded, but patient has no aversion to extraction) • Supporting tooth with implant (if prognosis guarded, patient has high aversion to extraction, and high cost threshold)

141

• Supporting tooth by using it as overdenture abutment for RPD (if prognosis guarded, patient has high aversion to extraction, and low cost threshold)

Expert Opinion

• 2 missing dentin walls and remaining 2 walls in “L” configuration • Questionable or guarded restoration prognosis • Prepare for caries excavation and then re-evaluate

Case Scenario 9:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major existing restorations

• Remaining non-carious tooth structure above bone level < 4.5 mm

• Requires caries excavation followed with crown lengthening or forced orthodontic

eruption to obtain minimum desired biologic width (2.5 mm), ferrule effect (2 mm), and

dentin wall thickness (1 mm)

• Projected Crown-to-root and crown-to-post ratios appear to be satisfactory

• Evaluate gray area factors (abutment, opposing occlusion, caries risk rate and bruxism)

• If restoration prognosis is questionable, evaluate patient preferences for cost threshold,

risk tolerance, restoration longevity expectations and aversion to tooth extraction

Table 4-19. Comparison of system generated treatment plan with expert opinion – case scenario 9.

Radiograph

142

CDSS Generated Treatment Plan Recommendation

Expert Opinion

• Restorable (CL or OE + post + core + crown)

Case Scenario 10:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major existing restorations

• Remaining non-carious tooth structure above bone level < 4.5 mm

• Requires caries excavation followed with crown lengthening or forced orthodontic

eruption to obtain minimum desired biologic width (2.5 mm), ferrule effect (2 mm), and

dentin wall thickness (1 mm)

143

• Projected crown-to-root and crown-to-post ratios < 1:1

Table 4-20. Comparison of system generated treatment plan with expert opinion – case scenario 10.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Non-restorable (extraction as restoration prognosis is very poor)

Expert Opinion

• Non-restorable (Extraction) • Caries at the bone level

Case Scenario 11:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major tooth loss

• Projected remaining coronal tooth structure after proposed crown lengthening or

forced orthodontic eruption may be > 4.5 mm

144

Table 4-21. Comparison of system generated treatment plan with expert opinion – case scenario 11.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Tooth is restorable with CL or OE + post + core + crown

Expert Opinion

• Tooth is non-restorable • Crown-to-root ratio is 1:1 • Width of root is insufficient for CL or OE

Case Scenario 12:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major tooth loss

• Projected remaining coronal tooth structure after proposed crown lengthening or

forced orthodontic eruption may be > 4.5 mm

145

Table 4-22. Comparison of system generated treatment plan with expert opinion – case scenario 12.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Tooth is restorable with CL or OE + Post + core + crown

Expert Opinion

• Non-restorable due to insufficient tooth structure. Might be marginally restorable after CL or OE + post + core + crown

Case Scenario 13:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major tooth loss

• Projected remaining coronal tooth structure after proposed crown lengthening or

forced orthodontic eruption is < 4.5 mm

146

Table 4-23. Comparison of system generated treatment plan with expert opinion – case scenario 13.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Non-restorable tooth (plan for extraction)

Expert Opinion

• Tooth is non-restorable due to following reasons: o Crown-to-root ratio is less than 1:1 o Width of root is poor o Not enough tooth structure for CL or OE

Case Scenario 14:

Scenario evaluation and decision-making factors:

• Posterior tooth with endodontic access opening

• None to minimal existing restorations

147

Table 4-24. Comparison of system generated treatment plan with expert opinion – case scenario 14.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable with intra + extra-coronal restoration

Expert Opinion

• Restorable with intra + extra-coronal restoration

Case Scenario 15:

Scenario evaluation and decision-making factors:

• Posterior tooth

• Major tooth loss

• Projected remaining coronal tooth structure after proposed crown lengthening or

forced orthodontic eruption may be > 4.5 mm

148

Table 4-25. Comparison of system generated treatment plan with expert opinion – case scenario 15.

Radiograph

CDSS Generated Treatment Plan Recommendation

• Restorable with CL or OE + post + core + crown

Expert Opinion

• Most likely non-restorable (plan for extraction). Minimally restorable with CL or OE (crown-to-root ratio guarded after CL or OE)

Table 4-26. Summary of agreement between system-generated treatment plan and expert opinion on restoration for the endodontically treated tooth in the 15 case scenarios used for system validation

CDSS-generated treatment plan recommendation CDSS determination of tooth restorability for 15 case scenarios Tooth Restorable Marginally or Total restorability Non-restorable for 15 case scenarios Restorable 7 0 7 (m1) based on Marginally or 3 5 8 (m0) expert opinion Non-restorable

Total 10 (n1) 5 (n0) 15 (n)

149

We used the Kappa statistic test to measure the interobserver agreement between the system generated treatment plan and expert opinion.

Observed agreement as per Table 4-26 above is calculated as follows:

Observed agreement (Po) = (7 + 5)/15 = 0.8

Expected agreement that reflects any agreement by chance alone is calculated as follows:

Expected agreement (Pe) = [10/15 * 7/15] + [5/15 * 8/15] = 0.49

Kappa, K = (p0 – pe)/(1 – pe) = (0.8 – 0.49)/(1 – 0.49) = 0.61

The value of Kappa obtained through our system validation tests reflects substantial agreement between system generated treatment plan and expert opinion using the Kappa interpretation on a 0 to 1 scale.242

150

Chapter 5 Discussion & Conclusions

5.1 Discussion

Proper prosthodontic reconstruction of endodontically treated teeth is critical to ensure their long-term survival. Although the topic of best way to restore teeth after root canal treatment has been widely researched and published, it is a complex area for clinical decision-making that is subject to opinions and differing viewpoints. Treatment process involves multi-factorial considerations that require good understanding of principles from endodontic, periodontic, orthodontic, and prosthodontic disciplines of dentistry. Decision tree with all the rules involved in restorative decision-making can be very intricate, which makes it arduous and challenging for less experienced clinicians and dental students to recollect and apply required rules at point-of- care. This often leads to critical errors in decision-making causing significant inconvenience to patients and poor treatment outcomes. Expert clinicians with their knowledge and years of experience can be of great help in the process, but cannot always be on the floor for assistance.

Such a challenging and error-prone area within restorative dentistry lends itself very well to the use of clinical decision support system. Rules based on expert knowledge and evidence-based clinical guidelines can be codified into the system that can be launched at the operatory for use by providers at the point-of-care. Such a system helps train dental providers to think like experts in treatment planning the restoration without necessarily having to memorize all the facts and rules in the process. They learn to think about the problem holistically rather than focusing on individual procedures which leads to improved treatment outcomes. Treatment plan allows provider to present rationale to the patient before performing the procedure and obtain patient buy-in. It helps in educating patient about the preferred treatment option and its expected outcome. If provider’s proposed treatment plan and patient preferences do not match, provider

151 can make timely changes to the treatment plan so long as patient is made aware of the treatment prognosis of altered plan. This helps ensure patient satisfaction, compliance, and improves treatment outcomes.

Although, clinical decision support systems have been developed for various applications in the field of restorative dentistry, there is still an unmet need for such a system in the area of restoration for endodontically treated teeth. Our goal in developing a clinical decision support system for restoration of endodontically treated teeth helps address this critical gap.

We used Exsys Corvid expert system development framework to develop a scalable architecture for the clinical decision support and training system. With research, new knowledge is actively being discovered in the field of dentistry. It is, therefore, very important that knowledge-based applications such as ours are designed to be scalable. As new knowledge is discovered in the field of restorative dentistry for the management of endodontically treated teeth, design of our system allows new rules to be easily added to the knowledge base without the need to rewrite existing logic in the system. Rules are divided into appropriate logic and command blocks, which are highly cohesive and loosely coupled. Following these object oriented design concepts, allows these building blocks for rules in the knowledge base to be reusable, understandable, readable, and maintainable.

Rules are based on expert knowledge and evidence-based guidelines and treatment plan generated by the system uses these rules in the knowledge base. Therefore, treatment plan information disseminated by the system to its users is practical and actionable. Users can learn about how different factors must be taken into consideration when making decisions regarding restorability of endodontically treated teeth. Dental students can use the system at their own pace and time, which facilitates the learning process for them. System also generates alerts and

152 prognosis information in addition to the treatment plan. This helps students understand the significance of each factor in the decision-making process and how each factor weighs in on the final treatment outcome. Detailed restoration prognosis score analysis provides further insight into expected outcomes with available treatment options. Use of the system, thus, trains students to systematically plan for the restoration of endodontically treated teeth, consider necessary and relevant factors, and evaluate different treatment options similar to the process followed by experts.

Our system is also designed to take into consideration patient preferences when restoration prognosis of possible treatment options is guarded or questionable. In such scenarios, shared decision making with patients can help the provider come up with an alternate treatment option that matches patient preferences. This ensures patient satisfaction and compliance, which are key to improving outcomes of the restorative treatment in the long run.

We validated our system results by using real patient radiographs. We carefully selected 15 different radiographs that represented the range of complexity in determining restorability of endodontically treated teeth. We then compared how closely the treatment plan recommendation made by our system matches with expert opinion. We obtained Kappa statistic test value of 0.61, which denotes substantial agreement on a scale of 0 to 1. We hope to further improve our system validation test results by conducting a more extensive study with the system. We plan to recruit dental students into our study and train them to study the difference in treatment outcomes with use of the system. We plan to evaluate system generated treatment plan recommendations against expert opinions using a large sample of patient radiographs to see if we can obtain an interobserver agreement rate better than 0.61 and closer to 1, which denotes perfect agreement.

153

5.2 Conclusions

Using Corvid expert system development framework, we have developed functional prototype of clinical decision support system for restoration of endodontically treated teeth. Our system is web-based and can be launched at the operatory and can be easily integrated into providers’ workflow. In a classroom scenario, the tool can be incorporated as part of class curriculum to supplement traditional teaching methods. Through this CDSS, knowledge in restoration of endodontically treated teeth can be delivered to the students as well as less experienced clinicians at the right time at point-of-care to minimize decision-making errors and improve treatment outcomes. Recommendations generated by the system are based on many years of experience and knowledge of experts and evidence-based guidelines. They have been proven effective and are actionable given the tool’s decision delivery mechanism that is clear and easy to understand.

Design of the system based on object-oriented concepts, rule-based knowledge representation and use of Corvid expert system development framework have helped us build scalable architecture that can be extended to process growing knowledge base of rules without having to rewrite existing rules in the system. Since the tool is computer-based system, it can efficiently evaluate several considerations that are needed to make the right decision. This ensures that all the necessary criteria are evaluated before a treatment plan is recommended. Computer generated treatment plan can be stored in the patient record for audit as well as legal purposes.

Compiled plan signed by the patient also serves as legal evidence of professional competence and patient understanding and acceptance. This helps explain and justify clinician’s decision in solving patient case and helps record patient’s understanding and acceptance of the decision and its implications. This allows for a more transparent patient care process.

154

Our functional prototype has been upgraded to offer the following additional features:

1. We have added functionality to store patient information in a database repository, such

that pertinent information can be retrieved as needed from the database and does not

need to be re-entered in scenarios where user is returning to a specific patient case.

Being able to save and retrieve from the database, users of the system gain efficiency by

not having to re-enter patient information and/or regenerate treatment plans that were

created in the past.

2. We have developed interface components seeking patient preferences, such that latter

can be factored into the decision-making process

3. We have worked on the system user interface to make it more user-friendly. We have

added URLs that providers can use to search the Web to obtain additional and

explanatory information about concepts they may not be certain about.

Our system design considerations are based on review studies that have identified features necessary for success of CDSSs. We have conducted extensive system testing as highlighted in preliminary system testing section of chapter 4. Additionally, we performed system validation using real patient radiographs and compared system generated treatment plan results with expert guidelines to prove that our CDSS meets our hypothesized goals described in chapter 1.

Our system can be further extended to add more features, details of which are discussed in the next section.

5.3 Future Direction

Given the scalable design of the system, it can further be developed to support other challenging sub-areas within restorative dentistry as well as areas within other dental disciplines.

155

Steps that can be taken to further develop the system and improve its wide-spread adoption include:

1. Work can be done in the future to integrate the CDSS with a live Electronic Health

Records (E.H.R.) system. This can tremendously improve the utility of both the E.H.R.

and our expert system as providers can automatically pull in all information stored in

patient e-records into the CDSS, obtain CDSS recommendations and develop most

effective treatment plans in shared decision-making with patients.

2. Work can also be done to either integrate the CDSS with another expert system that

processes radiographs or develop an extended module of our CDSS that processes

radiographs based on rules defined from expert and evidence-based guidelines. This can

automate the steps that the provider currently performs manually in the CDSS in terms

of answering patient specific information that can be obtained by examining

radiographs.

3. Since the real purpose and value of our tool comes from using it as a teaching aid for

students, efforts must be made by dental education institutions to reassess curriculum

and make arrangements to incorporate the tool as a supplement to traditional

classroom teaching.

4. Finally, the CDSS can be deployed on clinic floor for use by dental professionals as they

treatment plan restoration of endodontically treated teeth. A long-term study can be

undertaken to evaluate differences in decision-making confidence of students/clinicians

before/after use of CDSS.

156

References

1. Morgano SM, Rodrigues AH, Sabrosa CE. Restoration of endodontically treated teeth. Dental Clinics of North America 2004;48:397-416.

2. Cheung W. A review of the management of endodontically treated teeth. J Am Dent Assoc 2005;136:611-9.

3. Vârlan C, Dimitriu B, Vârlan V, Bodnar D, Suciu I. Current opinions concerning the restoration of endodontically treated. Journal of Medicine and Life 2009;2:165-72.

4. Yonker CM, Rubinstein S, Nidetz A. Restoring Endodontically Treated Teeth. Inside Dentistry Sept 2011;7:56-62.

5. Heling I, Gorfil C, Slutzky H, Kopolovic K, Zalkind M, Slutzky-Goldberg I. Endodontic failure caused by inadequate restorative procedures: Review and treatment recommendations. The Journal of Prosthetic Dentistry 2002;87:674-8.

6. Zadik Y, Sandler V, Bechor R, Salehrabi R. Analysis of factors related to extraction of endodontically treated teeth. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 2008;106:e31-e5.

7. Newsome P, Smales R, Yip K. Oral diagnosis and treatment planning: part 1. Introduction. British Dental Journal 2012;213:15-9.

8. CDT 2011-2012: current dental terminology: the ADA practical guide to dental procedure codes: American Dental Association; 2010.

9. Hook CR, Comer RW, Trombly RM, Guinn J, Shrout MK. Treatment planning processes in dental schools. Journal of Dental Education 2002;66:68-74.

10. Morris RB. Strategies in dental diagnosis and treatment planning: Taylor & Francis; 1999.

11. Gulabivala K. Restoration of the root-treated tooth. In: Stock, CJR and Gulabivala, K and Walker, RT, (eds.) Endodontics.(279 - 305). Elsevier Mosby: Edinburgh. 2004.

12. Stankiewicz N, Wilson P. The ferrule effect: a literature review. International endodontic journal 2002;35:575-81.

13. Sorensen JA, Engelman MJ. Ferrule design and fracture resistance of endodontically treated teeth. The Journal of Prosthetic Dentistry 1990;63:529-36.

157

14. Pereira JR, de Ornelas F, Rodrigues Conti PC, Lins do Valle A. Effect of a crown ferrule on the fracture resistance of endodontically treated teeth restored with prefabricated posts. The Journal of Prosthetic Dentistry 2006;95:50-4.

15. Juloski J, Radovic I, Goracci C, Vulicevic ZR, Ferrari M. Ferrule Effect: A Literature Review. Journal of Endodontics 2012;38:11-9.

16. Association AD. Council on Access Prevention and Interprofessional Relations. Caries diagnosis and risk assessment: a review of preventive strategies and management. J Am Dent Assoc 1995;126:1S-24S.

17. Benn DK. Applying evidence-based dentistry to caries management in dental practice A computerized approach. The Journal of the American Dental Association 2002;133:1543-8.

18. Peroz I, Blankenstein F, Lange K-P, Naumann M. Restoring endodontically treated teeth with posts and cores—a review. Quintessence Int 2005;36:737-46.

19. Finkeissen E, Böhret S, Stamm I, et al. AIDA: Web agents to support dental treatment planning. International journal of computerized dentistry 2002;5:133.

20. Park SG, Lee S, Kim M-K, Kim H-G. Shared decision support system on dental restoration. Expert Systems with Applications 2012;39:11775-81.

21. Exsys Corvid Expert System Development Framework. 2011. (Accessed June 12, 2013, at http://www.exsys.com/exsyscorvid.html.)

22. Hendricson WD, Panagakos F, Eisenberg E, et al. Electronic curriculum implementation at North American dental schools. Journal of dental education 2004;68:1041-57.

23. Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes. JAMA: the journal of the American Medical Association 1998;280:1339-46.

24. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Bmj 2005;330:765.

25. Carter J, Sorensen S, Johnson R, Teitelbaum R, Levine M. Punch shear testing of extracted vital and endodontically treated teeth. Journal of biomechanics 1983;16:841-8.

26. Helfer AR, Melnick S, Schilder H. Determination of the moisture content of vital and pulpless teeth. Oral Surgery, Oral Medicine, Oral Pathology 1972;34:661-70.

158

27. Sedgley CM, Messer HH. Are endodontically treated teeth more brittle? Journal of Endodontics 1992;18:332-5.

28. Huang T-JG, Schilder H, Nathanson D. Effects of moisture content and endodontic treatment on some mechanical properties of human dentin. Journal of Endodontics 1992;18:209-15.

29. Schwartz RS, Robbins JW. Post placement and restoration of endodontically treated teeth: a literature review. Journal of Endodontics 2004;30:289-301.

30. Reeh ES, Messer HH, Douglas WH. Reduction in tooth stiffness as a result of endodontic and restorative procedures. Journal of Endodontics 1989;15:512-6.

31. Pantvisai P, Messer HH. Cuspal deflection in molars in relation to endodontic and restorative procedures. Journal of Endodontics 1995;21:57-61.

32. Alves J, Walton R, Drake D. Coronal leakage: endotoxin penetration from mixed bacterial communities through obturated, post-prepared root canals. Journal of Endodontics 1998;24:587-91.

33. Saunders W, Saunders E. Coronal leakage as a cause of failure in root‐canal therapy: a review. Dental Traumatology 1994;10:105-8.

34. Tronstad L, Asbjørnsen K, Døving L, Pedersen I, Eriksen H. Influence of coronal restorations on the periapical health of endodontically treated teeth. Dental Traumatology 2000;16:218-21.

35. Hommez G, Coppens C, De Moor R. Periapical health related to the quality of coronal restorations and root fillings. International Endodontic Journal 2002;35:680-9.

36. Hunter A, Feiglin B, Williams J. Effects of post placement on endodontically treated teeth. The Journal of prosthetic dentistry 1989;62:166-72.

37. Torabinejad M, Ung B, Kettering JD. In vitro bacterial penetration of coronally unsealed endodontically treated teeth. Journal of Endodontics 1990;16:566-9.

38. Fathi B, Bahcall J, Maki JS. An in vitro comparison of bacterial leakage of three common restorative materials used as an intracoronal barrier. Journal of Endodontics 2007;33:872-4.

39. Robbins JW. Restoration of the endodontically treated tooth. Dental Clinics of North America 2002;46:367-84.

159

40. Slutzky-Goldberg I, Slutzky H, Gorfil C, Smidt A. Restoration of endodontically treated teeth review and treatment recommendations. International journal of dentistry 2010;2009.

41. Sorensen JA, Martinoff JT. Intracoronal reinforcement and coronal coverage: a study of endodontically treated teeth. The Journal of prosthetic dentistry 1984;51:780-4.

42. Barkhordar RA, Radke R, Abbasi J. Effect of metal collars on resistance of endodontically treated teeth to root fracture. The Journal of prosthetic dentistry 1989;61:676-8.

43. Hemmings K, King P, Setchell D. Resistance to torsional forces of various post and core designs. The Journal of prosthetic dentistry 1991;66:325-9.

44. Sorensen JA, Martinoff JT. Clinically significant factors in dowel design. The Journal of Prosthetic Dentistry 1984;52:28-35.

45. Linn J, Messer HH. Effect of restorative procedures on the strength of endodontically treated molars. Journal of Endodontics 1994;20:479-85.

46. Aquilino SA, Caplan DJ. Relationship between crown placement and the survival of endodontically treated teeth. The Journal of Prosthetic Dentistry 2002;87:256-63.

47. Scurria MS, Shugars DA, Hayden WJ, Felton DA. General dentists' patterns of restoring endodontically treated teeth. The Journal of the American Dental Association 1995;126:775-9.

48. Fejerskov O, Kidd E. Dental caries: the disease and its clinical management: John Wiley & Sons; 2008.

49. Selwitz RH, Ismail AI, Pitts NB. Dental caries. The Lancet 2007;369:51-9.

50. Featherstone JD. The science and practice of caries prevention. Journal-American Dental Association 2000;131:887-900.

51. Pitts N. Are we ready to move from operative to non-operative/preventive treatment of dental caries in clinical practice? Caries research 2004;38:294-304.

52. Mjör IA, Toffenetti F. Secondary caries: A literature review with case reports. Quintessence international (Berlin, Germany: 1985) 2000;31:165.

53. Tyas MJ, Anusavice KJ, Frencken JE, Mount GJ. Minimal intervention dentistry—a review. International dental journal 2000;50:1-12.

160

54. Ng CCH, Dumbrigue HB, Al-Bayat MI, Griggs JA, Wakefield CW. Influence of remaining coronal tooth structure location on the fracture resistance of restored endodontically treated anterior teeth. The Journal of Prosthetic Dentistry 2006;95:290-6.

55. Spear F. When to restore, when to remove: the single debilitated tooth. Compendium of continuing education in dentistry (Jamesburg, NJ: 1995) 1999;20:316.

56. Allen E. Surgical crown lengthening for function and esthetics. Dental Clinics of North America 1993;37:163.

57. Nugala B, Kumar BS, Sahitya S, Krishna PM. Biologic width and its importance in periodontal and restorative dentistry. Journal of conservative dentistry: JCD 2012;15:12.

58. Jorgić-Srdjak K, Dragoo MR, Bošnjak A, Plančak D, Filipović I, Lazić D. Periodontal and Prosthetic Aspect of the Biological Width Part II: Reconstruction of Anatomy and Function. Acta stomatologica Croatica 2000;34:441-4.

59. Wise M. Stability of gingival crest after surgery and before anterior crown placement. The Journal of Prosthetic Dentistry 1985;53:20-3.

60. Shillingburg HT, Hobo S, Whitsett LD, Jacobi R, Brackett SE. Fundamentals of fixed prosthodontics: Wiley Online Library; 1997.

61. Wagnild GW, Mueller KI. Restoration of the endodontically treated tooth. Pathways of the Pulp, 8th edn St Louis, MO, USA: Mosby Inc 2002:765-95.

62. Takei HH, Azzi R, Han TJ. Preparation of the periodontium for restorative dentistry. Carranza’s Clinical Periodontology, 9th Edition Philadelphia: WB Saunders Company 2002:945.

63. Nevins M, Skurow H. The intracrevicular restorative margin, the biologic width, and the maintenance of the gingival margin. The International journal of periodontics & restorative dentistry 1984;4:30.

64. Gargiulo AW, Wentz FM, Orban B. Dimensions and relations of the dentogingival junction in humans. Journal of Periodontology 1961;32:261-7.

65. Ingber J, Rose L, Coslet J. The" biologic width"--a concept in periodontics and restorative dentistry. The Alpha Omegan 1977;70:62-5.

66. Rosenberg E, Cho S, Garber D. Crown lengthening revisited. Compendium of continuing education in dentistry (Jamesburg, NJ: 1995) 1999;20:527-32, 34, 36-8 passim; quiz 42.

161

67. Kois JC. Altering gingival levels: the restorative connection part I: biologic variables. Journal of Esthetic and Restorative Dentistry 1994;6:3-7.

68. Kois JC. The restorative‐periodontal interface: biological parameters. Periodontology 2000 1996;11:29-38.

69. Robbins JW. Tissue management in restorative dentistry. Funct Esthet Restor Dent 2007;1:40-3.

70. Akkayan B. An in vitro study evaluating the effect of ferrule length on fracture resistance of endodontically treated teeth restored with fiber-reinforced and zirconia dowel systems. The Journal of prosthetic dentistry 2004;92:155-62.

71. Tan PL, Aquilino SA, Gratton DG, et al. In vitro fracture resistance of endodontically treated central incisors with varying ferrule heights and configurations. The Journal of prosthetic dentistry 2005;93:331-6.

72. Stankiewicz N, Wilson P. The ferrule effect. Dental update 2008;35:222-4, 7-8.

73. Libman W, Nicholls J. Load fatigue of teeth restored with cast posts and cores and complete crowns. The International journal of prosthodontics 1994;8:155-61.

74. Cho H, Michalakis KX, Kim Y, Hirayama H. Impact of interproximal groove placement and remaining coronal tooth structure on the fracture resistance of endodontically treated maxillary anterior teeth. Journal of Prosthodontics 2009;18:43-8.

75. Meng Q-F, Chen L-J, Meng J, Chen Y-M, Smales RJ, Yip K. Fracture resistance after simulated crown lengthening and forced tooth eruption of endodontically-treated teeth restored with a fiber post-and-core system. American journal of dentistry 2009;22:147-50.

76. Al‐Wahadni A, Gutteridge D. An in vitro investigation into the effects of retained coronal dentine on the strength of a tooth restored with a cemented post and partial core restoration. International endodontic journal 2002;35:913-8.

77. Jotkowitz A, Samet N. Rethinking ferrule–a new approach to an old dilemma. British dental journal 2010;209:25-33.

78. Morgano SM, Brackett SE. Foundation restorations in fixed prosthodontics: Current knowledge and future needs. The Journal of Prosthetic Dentistry 1999;82:643-57.

79. Woodmansey KF, Ayik M, Buschang PH, White CA, He J. Differences in Masticatory Function in Patients with Endodontically Treated Teeth and Single-implant–supported Prostheses: A Pilot Study. Journal of endodontics 2009;35:10-4.

162

80. Larson TD. Part one: The restoration of non-vital teeth: structural, biological, and micromechanical issues in maintaining tooth longevity. Northwest dentistry 2005;85:29, 31, 3-5 passim.

81. Kane JJ, Burgess JO. Modification of the resistance form of amalgam coronal-radicular restorations. The Journal of prosthetic dentistry 1991;65:470-4.

82. Goga R, Purton DG. The use of endodontically treated teeth as abutments for crowns, fixed partial dentures, or removable partial dentures: a literature review. Quintessence International 2007;38.

83. Guzy GE, Nicholls JI. In vitro comparison of intact endodntically treated teeth with and without endo-post reinforcement. The Journal of prosthetic dentistry 1979;42:39-44.

84. Leary JM, Aquilino SA, Svare CW. An evaluation of post length within the elastic limits of dentin. The Journal of prosthetic dentistry 1987;57:277-81.

85. Trope M, Maltz DO, Tronstad L. Resistance to fracture of restored endodontically treated teeth. Dental Traumatology 1985;1:108-11.

86. Ko C-C, Chu C-S, Chung K-H, Lee M-C. Effects of posts on dentin stress distribution in pulpless teeth. The Journal of prosthetic dentistry 1992;68:421-7.

87. Goodacre CJ, Spolnik KJ. The prosthodontic management of endodontically treated teeth: a literature review. Part I. Success and failure data, treatment concepts. Journal of Prosthodontics 1994;3:243-50.

88. Kantor ME, Pines MS. A comparative study of restorative techniques for pulpless teeth. The Journal of prosthetic dentistry 1977;38:405-12.

89. Foley J, Saunders E, Saunders W. Strength of core build-up materials in endodontically treated teeth. American journal of dentistry 1997;10:166-72.

90. Burke F, Shaglouf A, Combe E, Wilson N. Fracture resistance of five pin-retained core build-up materials on teeth with and without extracoronal preparation. Operative dentistry 1999;25:388-94.

91. Sorensen JA, Martinoff JT. Endodontically treated teeth as abutments. The Journal of prosthetic dentistry 1985;53:631-6.

92. Standlee J, Caputo A, Hanson E. Retention of endodontic dowels: effects of cement, dowel length, diameter, and design. The Journal of prosthetic dentistry 1978;39:401-5.

163

93. Lambjerg‐Hansen H, Asmussen E. Mechanical properties of endodontic posts. Journal of oral rehabilitation 1997;24:882-7.

94. Newman MP, Yaman P, Dennison J, Rafter M, Billy E. Fracture resistance of endodontically treated teeth restored with composite posts. The Journal of prosthetic dentistry 2003;89:360-7.

95. Al‐Hazaimeh N, Gutteridge D. An in vitro study into the effect of the ferrule preparation on the fracture resistance of crowned teeth incorporating prefabricated post and composite core restorations. International Endodontic Journal 2001;34:40-6.

96. Pilo R, Cardash HS, Levin E, Assif D. Effect of core stiffness on the in vitro fracture of crowned, endodontically treated teeth. The Journal of prosthetic dentistry 2002;88:302-6.

97. Felton D, Webb E, Kanoy B, Dugoni J. Threaded endodontic dowels: effect of post design on incidence of root fracture. The Journal of prosthetic dentistry 1991;65:179-87.

98. Torbjörner A, Karlsson S, Ödman PA. Survival rate and failure characteristics for two post designs. The Journal of Prosthetic Dentistry 1995;73:439-44.

99. Isidor F, Brøndum K. Intermittent loading of teeth with tapered, individually cast or prefabricated, parallel-sided posts. International Journal of Prosthodontics 1992;5.

100. Drummond JL, Bapna MS. Static and cyclic loading of fiber-reinforced dental resin. Dental Materials 2003;19:226-31.

101. Sidoli GE, King PA, Setchell DJ. An in vitro evaluation of a carbon fiber-based post and core system. The Journal of prosthetic dentistry 1997;78:5-9.

102. Cormier CJ, Burns DR, Moon P. In vitro comparison of the fracture resistance and failure mode of fiber, ceramic, and conventional post systems at various stages of restoration. Journal of Prosthodontics 2001;10:26-36.

103. Drummond JL. In vitro evaluation of endodontic posts. American journal of dentistry 2000;13:5B-8B.

104. Raygot CG, Chai J, Jameson L. Fracture Resistance and Primary Failure Mode of Endodontically Treated Teeth Restored with a Carbon Fiber--Reinforced Resin Post System In Vitro. International Journal of Prosthodontics 2001;14.

105. Caputo AA, Standlee JP. Restoration of endodontically involved teeth. In: Biomechanics in clinical dentistry: Quintessence Publishing Company Chicago, Ill; 1987.

164

106. Millstein P, Ho J, Nathanson D. Retention between a serrated steel dowel and different core materials. The Journal of prosthetic dentistry 1991;65:480-2.

107. Ray H, Trope M. Periapical status of endodontically treated teeth in relation to the technical quality of the root filling and the coronal restoration. International Endodontic Journal 1995;28:12-8.

108. Musen MA, Shahar Y, Shortliffe EH. Clinical decision-support systems. Biomedical informatics: Springer; 2006:698-736.

109. Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. Journal of the American medical informatics association 2007;14:141-5.

110. Musen MA, Middleton B, Greenes RA. Clinical decision-support systems. Biomedical informatics: Springer; 2014:643-74.

111. Osheroff JA. Improving medication use and outcomes with clinical decision support: a step-by-step guide: Healthcare Information and Management Systems Society Mission; 2009.

112. Sirajuddin AM, Osheroff JA, Sittig DF, Chuo J, Velasco F, Collins DA. Implementation pearls from a new guidebook on improving medication use and outcomes with clinical decision support: effective CDS is essential for addressing healthcare performance improvement imperatives. Journal of Healthcare Information Management 2009;23:38.

113. Ledley RS, Lusted LB. Reasoning foundations of medical diagnosis. Science 1959;130:9- 21.

114. Warner HR, Toronto AF, Veasey LG, Stephenson R. A mathematical approach to medical diagnosis: application to congenital heart disease. Jama 1961;177:177-83.

115. Collen MF, Rubin L, Neyman J, Dantzig GB, Baer RM, Siegelaub A. Automated multiphasic screening and diagnosis. American Journal of Public Health and the Nations Health 1964;54:741-50.

116. Bleich HL. Computer evaluation of acid-base disorders. Journal of Clinical Investigation 1969;48:1689.

117. De Dombal F, Horrocks JC, Staniland J, Guillou P. Construction and uses of a'data-base'of clinical information concerning 600 patients with acute abdominal pain. Proceedings of the Royal Society of Medicine 1971;64:978.

118. De Dombal F, Leaper D, Staniland JR, McCann A, Horrocks JC. Computer-aided diagnosis of acute abdominal pain. British medical journal 1972;2:9.

165

119. Shortliffe EH, Davis R, Axline SG, Buchanan BG, Green CC, Cohen SN. Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the MYCIN system. Computers and biomedical research 1975;8:303-20.

120. Yu VL, Buchanan BG, Shortliffe EH, et al. Evaluating the performance of a computer- based consultant. Computer programs in biomedicine 1979;9:95-102.

121. Yu VL, Fagan LM, Wraith SM, et al. Antimicrobial selection by a computer. A blinded evaluation by infectious diseases experts. JAMA: the journal of the American Medical Association 1979;242:1279-82.

122. Luger GF, Stubblefield WA. Artificial Intelligence, Structures and Strategies for Complex Problem Solving. Addison-Wesley; 1998.

123. Miller PL. ATTENDING: Critiquing a physician's management plan. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1983:449-61.

124. Miller RA, Pople Jr HE, Myers JD. INTERNIST-I, an experimental computer-based diagnostic consultant for general internal medicine: Springer; 1985.

125. Miller R, Masarie FE, Myers JD. Quick medical reference (QMR) for diagnostic assistance. MD computing: computers in medical practice 1985;3:34-48.

126. Miller RA, McNeil MA, Challinor SM, Masarie Jr FE, Myers JD. The INTERNIST-1/quick medical REFERENCE project—Status report. Western Journal of Medicine 1986;145:816.

127. Musen MA, Gennari JH, Wong WW. A rational reconstruction of internist-i using protégé-ii. Annual Symposium on Computer Applications in Medical Care; 1995: Citeseer. p. 289-93.

128. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain: an evolving diagnostic decision- support system. Jama 1987;258:67-74.

129. DXplain. 2007. (Accessed May, 2014, at http://lcs.mgh.harvard.edu/projects/dxplain.html.)

130. Kuperman GJ, Gardner RM, Pryor TA. HELP: a dynamic hospital information system: Springer Publishing Company, Incorporated; 2012.

131. McDonald CJ, Overhage JM, Tierney WM, et al. The Regenstrief medical record system: a quarter century experience. International journal of medical informatics 1999;54:225-53.

166

132. Hripcsak G, Ludemann P, Pryor TA, Wigertz OB, Clayton PD. Rationale for the Arden syntax. Computers and Biomedical Research 1994;27:291-324.

133. Ohno-Machado L, Gennari JH, Murphy SN, et al. The GuideLine Interchange Format A Model for Representing Guidelines. Journal of the American Medical Informatics Association 1998;5:357-72.

134. Ram P, Berg D, Tu S, et al. Executing clinical practice guidelines using the SAGE execution engine. Studies in health technology and informatics 2003;107:251-5.

135. Huff SM, McClurec R, Parkerc C, et al. Modeling guidelines for integration into clinical workflow. MEDINFO 2004: Proceedings of the 11th World Congress on Medical Informatics; 2004: IOS Press. p. 174.

136. Kawamoto K, Lobach DF. Design, implementation, use, and preliminary evaluation of SEBASTIAN, a standards-based Web service for clinical decision support. AMIA Annual Symposium Proceedings; 2005: American Medical Informatics Association. p. 380.

137. Johnson PD, Tu SW, Musen M, Purves I. A virtual medical record for guideline-based decision support. Proceedings of the AMIA symposium; 2001: American Medical Informatics Association. p. 294.

138. Kawamoto K, Del Fiol G, Orton C, Lobach DF. System-agnostic clinical decision support services: benefits and challenges for scalable decision support. The open medical informatics journal 2010;4:245.

139. Kawamoto K, Honey A, Rubin K. The HL7-OMG healthcare services specification project: motivation, methodology, and deliverables for enabling a semantically interoperable service- oriented architecture for healthcare. Journal of the American Medical Informatics Association 2009;16:874-81.

140. Wright A, Sittig DF. A four-phase model of the evolution of clinical decision support architectures. International journal of medical informatics 2008;77:641-9.

141. Schleyer T. Dental informatics: A work in progress. Advances in dental research 2003;17:9-15.

142. Stheeman S, Van der Stelt P, Mileman P. Expert systems in dentistry. Past performance—future prospects. Journal of dentistry 1992;20:68-73.

143. Abbey L. An expert system for oral diagnosis. Journal of dental education 1987;51:475- 80.

167

144. White S. Computer-aided differential diagnosis of oral radiographic lesions. Dentomaxillofacial Radiology 1989;18:53-9.

145. Sims-Williams J, Brown I, Matthewman A, Stephens C. A computer-controlled expert system for orthodontic advice. British dental journal 1987;163:161-6.

146. Hyman J, Doblecki W. Computerizd endodontic diagnosis. The Journal of the American Dental Association 1983;107:755-8.

147. Hyman JJ, Diehl M. A Dental Trauma Diagnostic Program. Proceedings/the Annual Symposium on Computer Application [sic] in Medical Care Symposium on Computer Applications in Medical Care; 1983: American Medical Informatics Association. p. 133-4.

148. Siegel M, Firriolo F, Finkelstein M. Computer applications in oral diagnosis. Dental Clinics of North America 1993;37:113-31.

149. Leonard M, Kilpatrick K, Fast T, Mahan P, Mackenzie RS. Automated diagnosis and treatment planning for craniofacial pain. Journal of dental research 1974;53:1155-9.

150. Leonard MS, Roberts SD, Fast TB, Mahan PE. Automated diagnosis of craniofacial pain. Journal of dental research 1973;52:1297-302.

151. Firriolo F. CAREOP: A new system for computer-assisted radiographic evaluation of oral pathology (senior thesis). Baltimore, MD: Baltimore College of Dental Surgery; 1986.

152. Wiener F, Laufer D, Ribak A. Computer-aided diagnosis of odontogenic lesions. International journal of oral and maxillofacial surgery 1986;15:592-6.

153. Hubar J, Manson-Hing L, Heaven T. COMRADD: Computerized radiographic differential diagnosis. Oral surgery, oral medicine, oral pathology 1990;69:263-5.

154. Ralls S, Cohen M, Southard T. Computer-assisted dental diagnosis. Dental Clinics of North America 1986;30:695-712.

155. Caine R, Johansen R. Computer-assisted Diagnosis of Soft-tissue Lesions. Journal of Dental Research; 1990: Amer Assoc Dental Research 1619 Duke st, Alexandria, VA 22314. p. 202.

156. Pass B, Gregson P. Automated identification of landmarks in cephalometric radiographs, Abstract number 2099. J Dental Res 1991;70:528.

157. Allen K, Christersson L, Hausmann E. Computerized monitoring of alveolar bone by area and densitometric methods. Journal of Dental Research; 1987: Amer Assoc Dental Research 1619 Duke st, Alexandria, VA 22314. p. 247.

168

158. van der Stelt PF, Geraets WG. Computer-aided interpretation and quantification of angular periodontal bone defects on dental radiographs. Biomedical Engineering, IEEE Transactions on 1991;38:334-8.

159. Fredriksson M, Zimmerman M, Martinsson T. Precision of computerized measurement of marginal alveolar bone height from bite-wing radiographs. Swedish dental journal 1988;13:163-7.

160. White SC. Decision-support systems in dentistry. Journal of dental education 1996;60:47-63.

161. Brickley MR, Shepherd JP. Performance of a neural network trained to make third-molar treatment-planning decisions. Medical decision making 1996;16:153-60.

162. Brickley MR, Shepherd JP, Armstrong RA. Neural networks: a new technique for development of decision support systems in dentistry. Journal of Dentistry 1998;26:305-9.

163. Speight P, Elliott A, Jullien J, Downer M, Zakzrewska J. The use of artificial intelligence to identify people at risk of oral cancer and precancer. British dental journal 1995;179:382-7.

164. Bruins HH, Koole R, Jolly DE. Pretherapy dental decisions in patients with head and neck cancer: a proposed model for dental decision support. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 1998;86:256-67.

165. Suebnukarn S, Rungcharoenporn N, Sangsuratham S. A Bayesian decision support model for assessment of endodontic treatment outcome. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology 2008;106:e48-e58.

166. Benn D, Clark T, Dankel 2nd D, Kostewicz S. Practical approach to evidence-based management of caries. The Journal of the American College of Dentists 1998;66:27-35.

167. Greenes RA. Clinical decision support: the road ahead: Academic Press; 2011.

168. Cimino JJ, Elhanan G, Zeng Q. Supporting infobuttons with terminological knowledge. Proceedings of the AMIA Annual Fall Symposium; 1997: American Medical Informatics Association. p. 528.

169. Del Fiol G, Huser V, Strasberg HR, Maviglia SM, Curtis C, Cimino JJ. Implementations of the HL7 context-aware knowledge retrieval (“infobutton”) standard: Challenges, strengths, limitations, and uptake. Journal of biomedical informatics 2012;45:726-35.

170. Del Fiol G, Rocha R, Cimino JJ. HL7 infobutton standard API proposal. Draft, February 2004;24.

169

171. Hochadel M. How Publishers are Developing Clinical Decision Support. Journal of Evidence Based Dental Practice 2008;8:206-8.

172. Mendonça EA. Clinical decision support systems: perspectives in dentistry. Journal of dental education 2004;68:589-97.

173. Berner ES. Clinical Decision Support Systems: Springer Science+ Business Media, LLC; 2007.

174. Minsky ML. Logical versus analogical or symbolic versus connectionist or neat versus scruffy. AI magazine 1991;12:34.

175. Hunink MM. Decision Making in Health and Medicine with CD-ROM: Integrating Evidence and Values: Cambridge University Press; 2001.

176. Gerald LB, Tang S, Bruce F, et al. A decision tree for tuberculosis contact investigation. American journal of respiratory and critical care medicine 2002;166:1122-7.

177. Rudin J. Dart (Diagnostic Aid and Resource Tool): a computerized clinical decision support system for oral pathology--Part 2. Compendium (Newtown, Pa) 1995;16:8, 10, 2 passim; quiz 7-8, , 2 passim; quiz 7.

178. Rudin JL. DART (Diagnostic Aid and Resource Tool): a computerized clinical decision support system for oral pathology. Compendium (Newtown, Pa) 1994;15:1316, 8, 20 passim.

179. Umar H. Capabilities of computerized clinical decision support systems: The implications for the practicing dental professional. J Contemp Dent Pract 2002;3:27-42.

180. McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 1943;5:115-33.

181. Hirshberg A, Adar R. Artificial neural networks in medicine. Israel journal of medical sciences 1997;33:700-2.

182. Marakas GM. Decision support systems in the 21st century: Prentice Hall ^ eNew Jersey New Jersey; 2003.

183. Baxt WG. Application of artificial neural networks to clinical medicine. The lancet 1995;346:1135-8.

184. Warner Jr H. Iliad: moving medical decision-making into new frontiers. Methods of information in medicine 1989;28:370-2.

170

185. Kahn C, Roberts LM, Wang K, Jenks D, Haddawy P. Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer. Annual Symposium on Computer Applications in Medical Care; 1995: IEEE Computer Society Press. p. 208-12.

186. Rich AL, Tata LJ, Stanley RA, et al. Lung cancer in England: Information from the national lung cancer audit (LUCADA). Lung cancer 2011;72:16-22.

187. Sesen MB, Nicholson AE, Banares-Alcantara R, Kadir T, Brady M. Bayesian Networks for Clinical Decision Support in Lung Cancer Care. PloS one 2013;8:e82349.

188. Segal MM, Williams MS, Gropman AL, et al. Evidence-Based Decision Support for Neurological Diagnosis Reduces Errors and Unnecessary Workup. Journal of child neurology 2013:0883073813483365.

189. Bhatia A, Singh R. Using Bayesian Network as Decision making system tool for deciding Treatment plan for Dental caries. Journal of Academia and Industrial Research (JAIR) 2013;2:93.

190. Borra R, Andrade P, Corrêa L, Novelli M. Development of an open case‐based decision‐ support system for diagnosis in oral pathology. European Journal of Dental Education 2007;11:87-92.

191. Umar H, Druzdzel M, Brass S. A Bayesian Approach to a Dental Clinical Advisory System with EER Prosthodontic Knowledge Representation. Computer Assisted Radiology and Surgery: Proceedings of the 14th International Congress and Exhibition CARS; 2000. p. 835-40.

192. Umar H. A Markov process in clinical dentistry: the prosthodontic cycle. International Congress Series; 2001: Elsevier. p. 719-25.

193. Ambrosiadou B, Goulis D, Pappas C. Clinical evaluation of the DIABETES expert system for decision support by multiple regimen insulin dose adjustment. Computer methods and programs in biomedicine 1996;49:105-15.

194. Marchevsky A, Coons G. Expert systems as an aid for the pathologist's role of clinical consultant: CANCER-STAGE. Modern pathology: an official journal of the United States and Canadian Academy of Pathology, Inc 1993;6:265-9.

195. Nguyen AN, Hartwell EA, Milam JD. A rule-based expert system for laboratory diagnosis of hemoglobin disorders. Archives of Pathology and Laboratory Medicine 1996;120:817-27.

196. Papaloukas C, Fotiadis DI, Likas A, Stroumbis CS, Michalis LK. Use of a novel rule-based expert system in the detection of changes in the ST segment and the T wave in long duration ECGs. Journal of electrocardiology 2002;35:27-34.

171

197. Riss PA, Koelbl H, Reinthaller A, Deutinger J. Development and application of simple expert systems in obstetrics and gynecology. Journal of Perinatal Medicine-Official Journal of the WAPM 1988;16:283-7.

198. Sailors RM, East T. A model-based simulator for testing rule-based decision support systems for mechanical ventilation of ARDS patients. Proceedings of the Annual Symposium on Computer Application in Medical Care; 1994: American Medical Informatics Association. p. 1007.

199. Reichley RM, Seaton TL, Resetar E, et al. Implementing a commercial rule base as a medication order safety net. Journal of the American Medical Informatics Association 2005;12:383-9.

200. Miller PL, Frawley SJ, Sayward FG, Yasnoff WA, Duncan L, Fleming DW. Combining tabular, rule-based, and procedural knowledge in computer-based guidelines for childhood immunization. Computers and Biomedical Research 1997;30:211-31.

201. Adlassnig K-P, Horak W. Development and retrospective evaluation of Hepaxpert-I: a routinely-used expert system for interpretive analysis of hepatitis A and B serologic findings. Artificial Intelligence in Medicine 1995;7:1-24.

202. Kahn M, Steib S, Fraser V, Dunagan W. An expert system for culture-based infection control surveillance. Proceedings of the Annual Symposium on Computer Application in Medical Care; 1993: American Medical Informatics Association. p. 171.

203. Shortliffe E. Computer-based medical consultations: MYCIN: Elsevier; 2012.

204. Matsumura Y. RHINOS: A consulatation system for diagnosis of headache and facial pain. Computer methods and programs in biomedicine 1986;23:65-71.

205. Davenport JC, Hammond P, Hazlehurst P. Knowledge-based systems, removable partial denture design and the development of RaPiD. Dent Update 1997;24:227-33.

206. Stamper R, Todd B, Macpherson P. Case-based explanation for medical diagnostic programs, with an example from gynaecology. Methods of information in medicine 1994;33:205-213.

207. Holt A, Bichindaritz I, Schmidt R, Perner P. Medical applications in case-based reasoning. The Knowledge Engineering Review 2005;20:289-92.

208. Koton P. Reasoning about E-vi ence in Causal Expllanations. 1988.

172

209. Wang S-L, Yeh S-Y. Framework of computer-assisted instruction and clinical decision support system for orthodontics with case-based reasoning. Medical Biometrics: Springer; 2010:344-52.

210. Minsky M. A framework for representing knowledge. 1974.

211. Sorenson D, Grissom C, Carpenter L, et al. A frame-based representation for a bedside ventilator weaning protocol. Journal of biomedical informatics 2008;41:461-8.

212. Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD. Artificial intelligence: a modern approach: Prentice hall Englewood Cliffs; 1995.

213. Gruber TR. A translation approach to portable ontology specifications. Knowledge acquisition 1993;5:199-220.

214. Campbell KE, Oliver DE, Spackman KA, Shortliffe EH. Representing thoughts, words, and things in the UMLS. Journal of the American Medical Informatics Association 1998;5:421-31.

215. Goldberg H, Goldsmith D, Law V, Keck K, Tuttle M, Safran C. An evaluation of UMLS as a controlled terminology for the Problem List Toolkit. Studies in health technology and informatics 1997;52:609-12.

216. McCray AT, Nelson SJ. The representation of meaning in the UMLS. Methods of information in medicine 1995;34:193-201.

217. Burgun A, Bodenreider O. Mapping the UMLS Semantic Network into general ontologies. Proceedings of the AMIA Symposium; 2001: American Medical Informatics Association. p. 81.

218. Rector A, Nowlan W, Consortium G. The GALEN project. Computer methods and programs in biomedicine 1994;45:75-8.

219. Spackman KA, Campbell KE, CÃ R. SNOMED RT: a reference terminology for health care. Proceedings of the AMIA annual fall symposium; 1997: American Medical Informatics Association. p. 640.

220. Wang AY, Barrett J, Bentley T, et al. Mapping between SNOMED RT and Clinical terms version 3: a key component of the SNOMED CT development process. Proceedings of the AMIA Symposium; 2001: American Medical Informatics Association. p. 741.

221. Goldberga LJ, Ceustersb W, Eisnerc J, Smithd B. The significance of SNODENT. 2005.

173

222. Stark PC, Kalenderian E, White JM, et al. Consortium for oral health-related informatics: improving dental research, education, and treatment. Journal of dental education 2010;74:1051-65.

223. Kalenderian E, Ramoni RL, White JM, et al. The development of a dental diagnostic terminology. Journal of dental education 2011;75:68-76.

224. Current Dental Terminology: the ADA Practical Guide to Dental Procedure Codes: American Dental Association; 2010.

225. Davenport J, Hammond P, Fitzpatrick F. Computerized design of removable partial dentures: a knowledge-based system for the future. Dental update 1993;20:221-6.

226. Hammond P, Davenport J, Potts A. Knowledge‐based design of removable partial dentures using direct manipulation and critiquing. Journal of oral rehabilitation 1993;20:115-23.

227. Beaumont Jr AJ, Bianco Jr HJ. Microcomputer-aided removable partial denture design. The Journal of prosthetic dentistry 1989;62:417-21.

228. Beaumont Jr J. Microcomputer-aided removable partial denture design: The next evolution. The Journal of prosthetic dentistry 1989;62:551-6.

229. Wicks R, Pennell M. Computer assisted design guide for removable partial denture frameworks. Trends & techniques in the contemporary dental laboratory 1990;7:51.

230. Hollenberg J. Markov Cycle Trees-A New Representation for Complex Markov Processes. Medical Decision Making; 1984: Hanley & Belfus Inc. 210 S 13th st, Philadelphia, PA 19107. p. 529.

231. Dolan JG. Involving patients in decisions regarding preventive health interventions using the analytic hierarchy process. Health Expectations 2000;3:37-45.

232. Singpurwalla N, Forman E, Zalkind D. Promoting shared health care decision making using the analytic hierarchy process. Socio-economic planning sciences 1999;33:277-99.

233. Ruland CM, Bakken S. Developing, implementing, and evaluating decision support systems for shared decision making in patient care: a conceptual model and case illustration. Journal of biomedical informatics 2002;35:313-21.

234. Schittek M, Mattheos N, Lyon H, Attström R. Computer assisted learning. A review. European Journal of Dental Education 2001;5:93-100.

174

235. Jackson P. Introduction to expert systems: Addison-Wesley Longman Publishing Co., Inc.; 1998.

236. Exsys Corvid software manual and addendums. 2007. (Accessed June 12, 2013, at http://www.exsys.com/manuals.html.)

237. Hayes-Roth F, Waterman DA, Lenat DB. Building expert systems. Teknowledge Series in Knowledge Engineering, Reading: Addison-Wesley, 1983, edited by Hayes-Roth, Frederick; Waterman, Donald A; Lenat, Douglas B 1983;1.

238. Cohesion (computer science). 2014. (Accessed 29 August 2014, at http://en.wikipedia.org/w/index.php?title=Cohesion_(computer_science)&oldid=619664276.)

239. Yourdon E, Constantine LL. Structured design: Fundamentals of a discipline of computer program and systems design: Prentice-Hall Englewood Cliffs; 1979.

240. Stevens WP, Myers GJ, Constantine LL. Structured design. IBM Systems Journal 1974;13:115-39.

241. Darcy DP, Kemerer CF. Software complexity: Toward a unified theory of coupling and cohesion. Friday Workshops, Management Information Systems Research Center, Carlson School of Management, University of Minnesota; 2002.

242. Viera AJ, Garrett JM. Understanding interobserver agreement: the kappa statistic. Fam Med 2005;37:360-3.

175