Artificial intelligence in screening,diagnosis, and classification of diabetic macular edema: A systematic review |
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| Institution: | 1. Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran;2. Ophthalmic Epidemiology Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran;3. Department of Optometry, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran;1. Department of Computing, University of A Coruña, A Coruña, Spain;2. CITIC-Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain;3. Instituto Oftalmológico Gómez-Ulla, Santiago de Compostela, Spain;4. Department of Ophthalmology, Complejo Hospitalario, Universitario de Santiago, Santiago de Compostela, Spain;5. University of Santiago de Compostela, Santiago de Compostela, Spain;1. Carver College of Medicine, University of Iowa, Iowa City, IA, USA;2. Schepens Eye Institute, Harvard Medical School, Boston, MA, USA;3. Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, IN, USA;1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China;2. School of Ophthalmology & Optometry, Wenzhou Medical College, Wenzhou, PR China;3. St Mary''s Hospital, Isle of Wight NHS Trust, Isle of Wight, United Kingdom;4. Faculty of Medicine, Imperial College London, London, United Kingdom;1. Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China;2. Department of Ophthalmology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands;3. Department of Ophthalmology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China;4. Division of Nephrology, Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands;5. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands |
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| Abstract: | We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases– PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM– from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection. |
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