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AI in Medical Imaging Informatics: Current Challenges and Future Directions This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2020.2991043, IEEE Journal of Biomedical and Health Informatics > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 AI in Medical Imaging Informatics: Current Challenges and Future Directions A. S. Panayides, Senior Member, IEEE, A. Amini, Fellow, IEEE, N.D. Filipovic, IEEE, A. Sharma, IEEE, S. A. Tsaftaris, Senior Member, IEEE, A. Young, IEEE, D. Foran, IEEE, N. Do, S. Golemati, Member, IEEE, T. Kurc, K. Huang, IEEE, K. S. Nikita, Fellow, IEEE, B.P. Veasey, IEEE Student Member, M. Zervakis, Senior Member, IEEE, J.H. Saltz, Senior Member, IEEE, C.S. Pattichis, Fellow, IEEE Abstract—This paper reviews state-of-the-art research I. INTRODUCTION solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes EDICAL imaging informatics covers the application of advances in medical imaging acquisition technologies for different M modalities, highlighting the necessity for efficient medical data information and communication technologies (ICT) to medical management strategies in the context of AI in big healthcare data imaging for the provision of healthcare services. A wide- analytics. It then provides a synopsis of contemporary and spectrum of multi-disciplinary medical imaging services have emerging algorithmic methods for disease classification and evolved over the past 30 years ranging from routine clinical organ/ tissue segmentation, focusing on AI and deep learning practice to advanced human physiology and pathophysiology. architectures that have already become the de facto approach. The Originally, it was defined by the Society for Imaging clinical benefits of in-silico modelling advances linked with Informatics in Medicine (SIIM) as follows [1]-[3]: evolving 3D reconstruction and visualization applications are “Imaging informatics touches every aspect of the imaging further documented. Concluding, integrative analytics approaches chain from image creation and acquisition, to image driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today distribution and management, to image storage and retrieval, across the healthcare continuum for both radiology and digital to image processing, analysis and understanding, to image pathology applications. The latter, is projected to enable informed, visualization and data navigation; to image interpretation, more accurate diagnosis, timely prognosis, and effective treatment reporting, and communications. The field serves as the planning, underpinning precision medicine. integrative catalyst for these processes and forms a bridge with imaging and other medical disciplines.” Index Terms—Medical Imaging, Image Analysis, Image The objective of medical imaging informatics is thus, Classification, Image Processing, Image Segmentation, Image according to SIIM, to improve efficiency, accuracy, and Visualization, Integrative Analytics, Machine Learning, Deep reliability of services within the medical enterprise [3], Learning, Big Data. A. Amini, N. D. Filipovic, A. Sharma, S. A. Tsaftaris, and A. Young, acted David J. Foran is with Department of Pathology and Laboratory Medicine, as Section leaders (in ascending surname order). D. Foran, S. Golemati, T. Robert Wood Johnson Medical School, Rutgers, the State University of New Kurc, A. Laine, K. S. Nikita, B. P. Veasey, and M. Zervakis acted as Section Jersey, Piscataway NJ 08854, USA (e-mail: [email protected]). contributors (in ascending surname order). A. S. Panayides, J. H. Saltz and C. Nhan Do, M.D., is with the U.S. Dept. of Veterans Affairs Boston S. Pattichis acted as manuscript’s editors. Healthcare System, 150 S. Huntington Ave, MA 02130, Boston, USA (e-mail: A. S. Panayides is with the Department of Computer Science of the [email protected]). University of Cyprus, Nicosia, Cyprus (e-mail: [email protected]). S.Golemati is with the Medical School, National and Kapodistrian A. Amini and B. Veasey are with the Electrical and Computer Engineering University of Athens, Greece (e-mail: [email protected]). Department, University of Louisville, Louisville, KY 40292 (e-mail: T. Kurc is with the Stony Brook University, USA (e-mail: [email protected], [email protected]). [email protected]). N. Filipovic is with the University of Kragujevac, Serbia (e-mail: K. Huang is with the School of Medicine, Regenstrief Institute, Indiana [email protected]). University, USA (e-mail: [email protected]). A. Sharma is with the Emory University, USA (e-mail: K.S. Nikita is with the Biomedical Simulations and Imaging Lab, School of [email protected]). Electrical and Computer Engineering, National Technical University of Athens, S.A. Tsaftaris is with the School of Engineering, The University of Greece (e-mail: [email protected]). Edinburgh, UK and also with The Alan Turing Institute, UK (e-mail: M. Zervakis is with the Technical University of Crete, Greece, (e-mail: [email protected]). S.A Tsaftaris acknowledges the support of the Royal [email protected]). Academy of Engineering under the Research Chairs and Senior Research J. Saltz is with the Stony Brook University, USA (e-mail: Fellowships scheme. [email protected]). A. Young is with the Department of Anatomy and Medical Imaging, C. S. Pattichis is with the Department of Computer Science of the University University of Auckland, Auckland 1142, New Zealand (e-mail: of Cyprus, Nicosia, Cyprus and the Research Centre on Interactive Media, [email protected]). Smart Systems and Emerging Technologies (RISE CoE), Nicosia, Cyprus (e- mail: [email protected]). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2020.2991043, IEEE Journal of Biomedical and Health Informatics > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 2 concerning medical image usage and exchange throughout using the phase-shifts of the X-rays as they traverse through the complex healthcare systems [4]. In that context, linked with the tissue [10]. X-ray projection imaging has been pervasive in associate technological advances in big-data imaging, -omics cardiovascular, mammography, musculoskeletal, and and electronic health records (EHR) analytics, dynamic abdominal imaging applications among others [11]. workflow optimization, context-awareness, and visualization, a Ultrasound imaging (US) employs pulses in the range of 1- new era is emerging for medical imaging informatics, 10 MHz to image tissue in a noninvasive and relatively prescribing the way towards precision medicine [5]-[7]. This inexpensive way. The backscattering effect of the acoustic paper provides an overview of prevailing concepts, highlights pulse interacting with internal structures is used to measure the challenges and opportunities, and discusses future trends. echo to produce the image. Ultrasound imaging is fast, Following the key areas of medical imaging informatics in enabling, for example, real-time imaging of blood flow in the definition given above, the rest of the paper is organized as arteries through the Doppler shift. A major benefit of ultrasonic follows: Section II covers advances in medical image imaging is that no ionizing radiation is used, hence less harmful acquisition highlighting primary imaging modalities used in to the patient. However, bone and air hinder the propagation of clinical practice. Section III discusses emerging trends sound waves and can cause artifacts. Still, ultrasound remains pertaining to the data management and sharing in the medical one of the most used imaging techniques employed extensively imaging big data era. Then, Section IV introduces emerging for real-time cardiac and fetal imaging [11]. Contrast-enhanced data processing paradigms in radiology, providing a snapshot ultrasound has allowed for greater contrast and imaging of the timeline that has today led to increasingly adopting AI accuracy with the use of injected microbubbles to increase and deep learning analytics approaches. Likewise, Section V reflection in specific areas in some applications [12]. reviews the state-of-the-art in digital pathology. Section VI Ultrasound elasticity imaging has also been used for measuring describes the challenges pertaining to 3D reconstruction and the stiffness of tissue for virtual palpation [13]. Importantly, visualization in view of different application scenarios. Digital ultrasound is not limited to 2D imaging and use of 3D and 4D pathology visualization challenges are further documented in imaging is expanding, though with reduced temporal resolution this section, while in-silico modelling advances are presented [14]. next, debating the need of introducing new integrative, multi- MR imaging [15] produces high spatial resolution volumetric compartment modelling approaches. Section VII discusses the images primarily of Hydrogen nuclei, using an externally need of integrative analytics and discusses emerging applied magnetic
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