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      Applications of Artificial Intelligence in Ophthalmology: General Overview

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          Abstract

          With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.

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          An efficient k-means clustering algorithm: analysis and implementation

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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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              Deep Learning in Medical Imaging: General Overview

              The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.
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                Author and article information

                Contributors
                Journal
                J Ophthalmol
                J Ophthalmol
                JOPH
                Journal of Ophthalmology
                Hindawi
                2090-004X
                2090-0058
                2018
                19 November 2018
                : 2018
                : 5278196
                Affiliations
                1Eye Center, Renmin Hospital of Wuhan University, Eye Institute of Wuhan University, Wuhan, Hubei, China
                2Hisee Medical Artificial Intelligent Lab, Wuhan University, Wuhan, Hubei, China
                Author notes

                Academic Editor: Hiroshi Kunikata

                Author information
                http://orcid.org/0000-0002-0218-569X
                http://orcid.org/0000-0003-4847-2405
                http://orcid.org/0000-0002-9147-4644
                http://orcid.org/0000-0001-5534-1951
                http://orcid.org/0000-0002-7281-552X
                http://orcid.org/0000-0002-4201-3948
                Article
                10.1155/2018/5278196
                6276430
                30581604
                850c0324-a8c5-468c-80f0-114be134efab
                Copyright © 2018 Wei Lu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 July 2018
                : 6 October 2018
                : 17 October 2018
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81470628
                Funded by: International Science & Technology Cooperation Program of China
                Award ID: 2017YFE0103400
                Categories
                Review Article

                Ophthalmology & Optometry
                Ophthalmology & Optometry

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