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Randomized Study Comparing 3D Virtual Reality and Conventional 2D On-Screen Teaching of Cerebrovascular Anatomy

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Randomized Study Comparing 3D Virtual Reality and Conventional 2D On-Screen Teaching of Cerebrovascular Anatomy NEUROSURGICAL FOCUS Neurosurg Focus 51 (2):E18, 2021 Randomized study comparing 3D virtual reality and conventional 2D on-screen teaching of cerebrovascular anatomy *Ladina Greuter, MD,1 Adriana De Rosa,2 Philippe Cattin, PhD,3 Davide Marco Croci, MD,1,4 Jehuda Soleman, MD,1,2 and Raphael Guzman, MD1–3 1Department of Neurosurgery, University Hospital of Basel; 2Faculty of Medicine and 3Department of Biomedical Engineering, University of Basel, Switzerland; and 4Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah OBJECTIVE Performing aneurysmal clipping requires years of training to successfully understand the 3D neurovascu- lar anatomy. This training has traditionally been obtained by learning through observation. Currently, with fewer operative aneurysm clippings, stricter work-hour regulations, and increased patient safety concerns, novel teaching methods are required for young neurosurgeons. Virtual-reality (VR) models offer the opportunity to either train a specific surgical skill or prepare for an individual surgery. With this study, the authors aimed to compare the spatial orientation between traditional 2D images and 3D VR models in neurosurgical residents or medical students. METHODS Residents and students were each randomly assigned to describe 4 aneurysm cases, which could be either 2D images or 3D VR models. The time to aneurysm detection as well as a spatial anatomical description was assessed via an online questionnaire and compared between the groups. The aneurysm cases were 10 selected patient cases treated at the authors’ institution. RESULTS Overall, the time to aneurysm detection was shorter in the 3D VR model compared to 2D images, with a trend toward statistical significance (25.77 ± 37.26 vs 45.70 ± 51.94 seconds, p = 0.052). No significant difference was observed for residents (3D VR 24.47 ± 40.16 vs 2D 33.52 ± 56.06 seconds, p = 0.564), while in students a significantly shorter time to aneurysm detection was measured using 3D VR models (26.95 ± 35.39 vs 59.16 ± 44.60 seconds, p = 0.015). No significant differences between the modalities for anatomical and descriptive spatial mistakes were observed. Most participants (90%) preferred the 3D VR models for aneurysm detection and description, and only 1 participant (5%) described VR-related side effects such as dizziness or nausea. CONCLUSIONS VR platforms facilitate aneurysm recognition and understanding of its spatial anatomy, which could make them the preferred method compared to 2D images in the years to come. https://thejns.org/doi/abs/10.3171/2021.5.FOCUS21212 KEYWORDS virtual reality; aneurysm surgery; vascular neurosurgery; resident teaching; augmented reality NEURYSM surgery is a complex neurosurgical field rising costs of operating theater time and the regulations requiring years of training and a thorough un- of residents’ working hours, hands-on practice and surgi- derstanding of the 3D neurovascular anatomy. cal experience are becoming less frequent and residents AThe spatial 3D comprehension of the aneurysm location, often lack a sufficient case load.2,3 Hence, novel tools are dome orientation, and branching vessels has had to be needed to optimize resident teaching and surgical train- derived from 2D images, a particularly challenging pro- ing. Virtual-reality (VR) platforms offer the opportunity cess for young neurosurgeons.1 Especially in the current to either train a specific surgical skill or to facilitate the era of frequent endovascular aneurysm treatment, opera- surgeon’s preparation for a patient-specific case.4–12 Some tive aneurysm cases are becoming rarer and often have models, including several equipped with haptic feedback complex anatomical characteristics, and therefore are not to practice procedures, have been successfully used in always suitable as teaching cases. Additionally, with the neurosurgical resident training.4 , 8 , 9 , 1 3 In general, the evi- ABBREVIATIONS MCA = middle cerebral artery; PICA = posterior inferior cerebellar artery; VR = virtual reality. SUBMITTED April 1, 2021. ACCEPTED May 13, 2021. INCLUDE WHEN CITING DOI: 10.3171/2021.5.FOCUS21212. * J.S. and R.G. contributed equally to this work. ©AANS 2021, except where prohibited by US copyright law Neurosurg Focus Volume 51 • August 2021 1 Unauthenticated | Downloaded 10/06/21 05:36 PM UTC Greuter et al. TABLE 1. Questions in the survey with possible answer options Question Possible Answers 1. What is your gender? Male, female 2. What is your age? No. of yrs 3. How many years of neurosurgical practice do you have? 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 4. Are you a student or a resident/young neurosurgeon? Student, resident 5. Case number 1–20 6. Time to detect the aneurysm (seconds) No. of seconds 7. Where is the aneurysm located? Lt MCA, Rt MCA, ACOM, BA, Lt PICA, Rt PICA, Lt ICA, Rt ICA, PCOM 8. What is the morphology of the aneurysm? Saccular, additional bleb, polylobulated 9. How is the aneurysm dome oriented? Lateral, medial, anterior, posterior, cranial, caudal 10. How would you treat the aneurysm? Craniotomy, endovascular 11. Which modality do you find easier to detect and describe aneurysms? VR model, 2D images 12. How do you rate the image quality? Very good, good, passable, bad, very bad 13. Did you experience any symptoms like nausea, dizziness, double vision, Yes, no etc., while using the VR platform? ACOM = anterior communicating cerebral artery; BA = basilar artery; ICA = internal carotid artery; PCOM = posterior communicating cerebral artery. dence on objective teaching advantages between 3D VR prepared a 2D image, either a CT angiogram or an MR models and traditional learning methods is scarce.8,9,14 angiogram, and a 3D VR data set for each case, resulting SpectoVR is the 3D VR software used in this study and in 20 cases (10 per modality). The 20 cases were randomly features a fully immersive VR platform allowing for the assigned to the participants using an online randomiza- creation of a patient-specific 3D VR model based on 2D tion tool (randomlists.org), which ensured that the same DICOM images.15 case was analyzed by more than 1 participant. Participants In this study, we aimed to analyze how quickly neu- were able to familiarize themselves with the VR platform rosurgical residents and medical students comprehend using an example case of a middle cerebral artery (MCA) the anatomical characteristics and spatial orientation of aneurysm, which was not used for the study analysis. different aneurysms when using 3D VR as compared to standard image visualization on a radiology monitor. We Outcome Variables hypothesized that with 3D VR it is easier to detect and The primary outcome of the study was time to aneu- describe the spatial anatomy of the aneurysm as compared rysm detection for the two modalities (3D VR vs 2D). to conventional image visualization. The time was measured in seconds from the moment the image was presented to the participants until the moment Methods they detected the aneurysm. Secondary outcomes were Study Participants accuracy of describing the anatomical and spatial features The study group consisted of neurosurgery residents of the aneurysm, consisting of side and anatomical loca- (referred to as the doc group) from the Department of tion, dome orientation (frontal, posterior, cranial, caudal, Neurosurgery, University Hospital of Basel, and medi- medial, or lateral) and morphology (saccular, saccular cal students (4th–6th years, referred to as the med group) with a bleb, or polylobulated). All answers were compared from the Faculty of Medicine, University of Basel. None between the two modalities for all participants, and for the of the participants from the doc group had independently two groups (doc group and med group) separately. As an performed aneurysmal clipping. The participants from the exploratory analysis, linear regression was calculated for med group were either interns at our clinic or volunteered the time to aneurysm detection and the experience of all to take part in this study; all students passed their neu- participants. roanatomy course and examination before participating No approval from the local ethics board was needed in the trial. Every participant analyzed 4 aneurysm cases for this study, but the use of patient images for rendering and filled in a web-based questionnaire (Google Forms, anonymous 3D VR data sets was registered and approved Google Inc.) containing demographic questions, questions by the local ethics board. regarding the anatomical and spatial anatomy of the an- eurysm, and questions about image quality and subjective SpectoVR Software Description impression of the two methods used (Table 1). The doc SpectoVR software renders a fully immersive 3D VR group also answered a question concerning their suggest- model based on patient-specific DICOM data. The DI- ed aneurysm treatment (question 10, Table 1), which was COM images were either based on CT angiography or omitted in the med group due to a knowledge gap in that digital subtraction angiography images. Threshold set- group. The aneurysm cases were 10 selected patient-based tings based on Hounsfield units had to be individually cases treated at our institution between 2018 and 2020. We adapted in the program to create a colorful high-contrast 2 Neurosurg Focus Volume 51 • August 2021 Unauthenticated | Downloaded 10/06/21 05:36 PM UTC Greuter et al. and user-friendly 3D VR model. The software was writ- ten in C++ computer language and developed at the De- partment of Biomedical Engineering, University of Basel. It was CE certified as a class I medical device (Diffuse Inc.), and its geometrical and anatomical accuracy has been previously verified.16,17 The algorithm used is based on ray casting, in which a ray of light is cast through space, and color and opacity are accumulated along this ray. The visualization is based on a real-time calculation with 180 frames per second, each one corrected for the user’s cur- rent perspective on the object and the user’s manipula- tion of the object with a controller (scaling, movement in space, interaction with the model), ensuring an immersive viewing experience through a commercial VR headset.
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