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An overview of artificial intelligence in diabetic retinopathy and other ocular diseases
Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of dis...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650481/ https://www.ncbi.nlm.nih.gov/pubmed/36388304 http://dx.doi.org/10.3389/fpubh.2022.971943 |
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author | Sheng, Bin Chen, Xiaosi Li, Tingyao Ma, Tianxing Yang, Yang Bi, Lei Zhang, Xinyuan |
author_facet | Sheng, Bin Chen, Xiaosi Li, Tingyao Ma, Tianxing Yang, Yang Bi, Lei Zhang, Xinyuan |
author_sort | Sheng, Bin |
collection | PubMed |
description | Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology. |
format | Online Article Text |
id | pubmed-9650481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96504812022-11-15 An overview of artificial intelligence in diabetic retinopathy and other ocular diseases Sheng, Bin Chen, Xiaosi Li, Tingyao Ma, Tianxing Yang, Yang Bi, Lei Zhang, Xinyuan Front Public Health Public Health Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology. Frontiers Media S.A. 2022-10-28 /pmc/articles/PMC9650481/ /pubmed/36388304 http://dx.doi.org/10.3389/fpubh.2022.971943 Text en Copyright © 2022 Sheng, Chen, Li, Ma, Yang, Bi and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Sheng, Bin Chen, Xiaosi Li, Tingyao Ma, Tianxing Yang, Yang Bi, Lei Zhang, Xinyuan An overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
title | An overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
title_full | An overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
title_fullStr | An overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
title_full_unstemmed | An overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
title_short | An overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
title_sort | overview of artificial intelligence in diabetic retinopathy and other ocular diseases |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9650481/ https://www.ncbi.nlm.nih.gov/pubmed/36388304 http://dx.doi.org/10.3389/fpubh.2022.971943 |
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