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      Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review

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          Abstract

          Rising prevalence of diabetes worldwide has necessitated the implementation of population-based diabetic retinopathy (DR) screening programs that can perform retinal imaging and interpretation for extremely large patient cohorts in a rapid and sensitive manner while minimizing inappropriate referrals to retina specialists. While most current screening programs employ mydriatic or nonmydriatic color fundus photography and trained image graders to identify referable DR, new imaging modalities offer significant improvements in diagnostic accuracy, throughput, and affordability. Smartphone-based fundus photography, macular optical coherence tomography, ultrawide-field imaging, and artificial intelligence-based image reading address limitations of current approaches and will likely become necessary as DR becomes more prevalent. Here we review current trends in imaging for DR screening and emerging technologies that show potential for improving upon current screening approaches.

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          Most cited references68

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          Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group.

          (1991)
          The modified Airlie House classification of diabetic retinopathy has been extended for use in the Early Treatment Diabetic Retinopathy Study (ETDRS). The revised classification provides additional steps in the grading scale for some characteristics, separates other characteristics previously combined, expands the section on macular edema, and adds several characteristics not previously graded. The classification is described and illustrated and its reproducibility between graders is assessed by calculating percentages of agreement and kappa statistics for duplicate gradings of baseline color nonsimultaneous stereoscopic fundus photographs. For retinal hemorrhages and/or microaneurysms, hard exudates, new vessels, fibrous proliferations, and macular edema, agreement was substantial (weighted kappa, 0.61 to 0.80). For soft exudates, intraretinal microvascular abnormalities, and venous beading, agreement was moderate (weighted kappa, 0.41 to 0.60). A double grading system, with adjudication of disagreements of two or more steps between duplicate gradings, led to some improvement in reproducibility for most characteristics.
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            Automated Identification of Diabetic Retinopathy Using Deep Learning

            Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.
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              Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs

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                Author and article information

                Contributors
                gemmy.cheung.c.m@singhealth.com.sg
                Journal
                Ophthalmol Ther
                Ophthalmol Ther
                Ophthalmology and Therapy
                Springer Healthcare (Cheshire )
                2193-8245
                2193-6528
                10 November 2018
                10 November 2018
                December 2018
                : 7
                : 2
                : 333-346
                Affiliations
                [1 ]ISNI 0000 0000 9960 1711, GRID grid.419272.b, Residency Program, , Singapore National Eye Centre, ; Singapore, Singapore
                [2 ]ISNI 0000 0004 1937 0482, GRID grid.10784.3a, Department of Ophthalmology and Visual Sciences, , The Chinese University of Hong Kong, ; Hong Kong, China
                [3 ]ISNI 0000000121742757, GRID grid.194645.b, Department of Ophthalmology, , The University of Hong Kong, ; Shatin, Hong Kong
                [4 ]ISNI 0000 0000 9960 1711, GRID grid.419272.b, Surgical Retina Department, , Singapore National Eye Centre, ; Singapore, Singapore
                [5 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Ophthlamology and Visual Sciences Academic Clinical Program, , Duke-NUS Graduate Medical School, ; Singapore, Singapore
                [6 ]ISNI 0000 0001 0706 4670, GRID grid.272555.2, Retina Research Group, , Singapore Eye Research Institute, ; Singapore, Singapore
                [7 ]ISNI 0000 0000 9960 1711, GRID grid.419272.b, Medical Retina Department, , Singapore National Eye Centre, ; Singapore, Singapore
                Article
                153
                10.1007/s40123-018-0153-7
                6258577
                30415454
                bf757364-4fc5-4b4e-8e3c-ae3d3179c2ae
                © The Author(s) 2018
                History
                : 12 June 2018
                Categories
                Review
                Custom metadata
                © Springer Healthcare Ltd., part of Springer Nature 2018

                artificial intelligence,deep learning,diabetic retinopathy,optical coherence tomography,retina,ultrawide field imaging

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