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      Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Purpose of review

          The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology.

          Recent findings

          In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models.

          Summary

          The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

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          Most cited references74

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
            Bookmark
            • Record: found
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            ImageNet classification with deep convolutional neural networks

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              • Record: found
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              • Article: not found

              Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

              Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
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                Author and article information

                Journal
                Curr Opin Ophthalmol
                Curr Opin Ophthalmol
                COOPH
                Current Opinion in Ophthalmology
                Lippincott Williams & Wilkins (Hagerstown, MD )
                1040-8738
                1531-7021
                September 2023
                13 July 2023
                : 34
                : 5
                : 403-413
                Affiliations
                [a ]Department of Ophthalmology, Taipei Veterans General Hospital
                [b ]School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
                [c ]Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
                [d ]Department of Medicine – Ophthalmology, University of Udine
                [e ]Istituto Europeo di Microchirurgia Oculare, Udine, Italy
                [f ]Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester School of Health Sciences, Manchester, UK
                [g ]International Federation on Ageing, Toronto, Canada
                [h ]Department of Ophthalmology, AP-HP Hôpital Lariboisière, Université Paris Cité, Paris, France
                [i ]Department of Ophthalmology, Hacettepe University, Ankara, Turkey
                [j ]Department of Ophthalmology, University of Münster Medical Center, Münster, Germany
                [k ]Department of Ophthalmology, York Teaching Hospital NHS Foundation Trust, York, UK
                [l ]Service d’ophtalmologie, CHU Bordeaux
                [m ]University of Bordeaux, INSERM, BPH, UMR1219, F-33000 Bordeaux, France
                [n ]Moorfields Eye Hospital Centre, Abu Dhabi, UAE
                [o ]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
                [p ]Xiamen Eye Center, Xiamen University, Xiamen, China
                [q ]Division of Ophthalmology, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
                [r ]Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
                [s ]Department of Ophthalmology, Kagoshima University, Kagoshima, Japan
                [t ]Singapore National Eye Center, Duke-NUS Medical School, Singapore
                [u ]Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia
                [v ]Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
                [w ]Department of Ophthalmology, Landesklinikum Mistelbach-Gänserndorf, Mistelbach, Austria
                [x ]Unity Health Toronto – St. Michael's Hospital, University of Toronto, Toronto, Canada
                [y ]Macula, Vitreous and Retina Associates of Costa Rica, San José, Costa Rica
                [z ]Ophthalmology Department, Hospital Vall d’Hebron
                [aa ]Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain
                Author notes
                Correspondence to Paolo Lanzetta, Department of Medicine – Ophthalmology, University of Udine, Udine, 8-33100, Italy. Tel: +39 43 255 99 07; e-mail: paolo.lanzetta@ 123456uniud.it
                Article
                ICU340514 00011
                10.1097/ICU.0000000000000979
                10399944
                37326222
                e9696ede-a767-4d26-97a4-89771b1dbbfb
                Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

                History
                Categories
                ARTIFICIAL INTELLIGENCE/BIG DATA: Edited by Daniel Ting and Ehsan Rahimy
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                artificial intelligence,diagnosis,retina,retinal imaging

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