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Artificial intelligence and deep learning in ophthalmology
Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362807/ https://www.ncbi.nlm.nih.gov/pubmed/30361278 http://dx.doi.org/10.1136/bjophthalmol-2018-313173 |
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author | Ting, Daniel Shu Wei Pasquale, Louis R Peng, Lily Campbell, John Peter Lee, Aaron Y Raman, Rajiv Tan, Gavin Siew Wei Schmetterer, Leopold Keane, Pearse A Wong, Tien Yin |
author_facet | Ting, Daniel Shu Wei Pasquale, Louis R Peng, Lily Campbell, John Peter Lee, Aaron Y Raman, Rajiv Tan, Gavin Siew Wei Schmetterer, Leopold Keane, Pearse A Wong, Tien Yin |
author_sort | Ting, Daniel Shu Wei |
collection | PubMed |
description | Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward. |
format | Online Article Text |
id | pubmed-6362807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-63628072019-02-27 Artificial intelligence and deep learning in ophthalmology Ting, Daniel Shu Wei Pasquale, Louis R Peng, Lily Campbell, John Peter Lee, Aaron Y Raman, Rajiv Tan, Gavin Siew Wei Schmetterer, Leopold Keane, Pearse A Wong, Tien Yin Br J Ophthalmol Review Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward. BMJ Publishing Group 2019-02 2018-10-25 /pmc/articles/PMC6362807/ /pubmed/30361278 http://dx.doi.org/10.1136/bjophthalmol-2018-313173 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0 |
spellingShingle | Review Ting, Daniel Shu Wei Pasquale, Louis R Peng, Lily Campbell, John Peter Lee, Aaron Y Raman, Rajiv Tan, Gavin Siew Wei Schmetterer, Leopold Keane, Pearse A Wong, Tien Yin Artificial intelligence and deep learning in ophthalmology |
title | Artificial intelligence and deep learning in ophthalmology |
title_full | Artificial intelligence and deep learning in ophthalmology |
title_fullStr | Artificial intelligence and deep learning in ophthalmology |
title_full_unstemmed | Artificial intelligence and deep learning in ophthalmology |
title_short | Artificial intelligence and deep learning in ophthalmology |
title_sort | artificial intelligence and deep learning in ophthalmology |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362807/ https://www.ncbi.nlm.nih.gov/pubmed/30361278 http://dx.doi.org/10.1136/bjophthalmol-2018-313173 |
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