Artificial intelligence-based detection of epimacular membrane from color fundus photographs

Abstract Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT)...

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Autores principales:	Enhua Shao, Congxin Liu, Lei Wang, Dan Song, Libin Guo, Xuan Yao, Jianhao Xiong, Bin Wang, Yuntao Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a8fcc22dff5141bcb83ba249a08a3810
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spelling	oai:doaj.org-article:a8fcc22dff5141bcb83ba249a08a38102021-12-02T17:37:12ZArtificial intelligence-based detection of epimacular membrane from color fundus photographs10.1038/s41598-021-98510-x2045-2322https://doaj.org/article/a8fcc22dff5141bcb83ba249a08a38102021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98510-xhttps://doaj.org/toc/2045-2322Abstract Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ2 = 0.038, P = 0.845, McNemar’s test), higher sensitivity (75.90% vs. 63.86%, χ2 = 4.500, P = 0.034, McNemar’s test), under the cost of lower but reasonable specificity (78.69% vs. 91.80%, χ2 = 6.125, P = 0.013, McNemar’s test). Thus our AI model can serve as a possible alternative for manual diagnosis in ERM screening.Enhua ShaoCongxin LiuLei WangDan SongLibin GuoXuan YaoJianhao XiongBin WangYuntao HuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Enhua Shao Congxin Liu Lei Wang Dan Song Libin Guo Xuan Yao Jianhao Xiong Bin Wang Yuntao Hu Artificial intelligence-based detection of epimacular membrane from color fundus photographs
description	Abstract Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ2 = 0.038, P = 0.845, McNemar’s test), higher sensitivity (75.90% vs. 63.86%, χ2 = 4.500, P = 0.034, McNemar’s test), under the cost of lower but reasonable specificity (78.69% vs. 91.80%, χ2 = 6.125, P = 0.013, McNemar’s test). Thus our AI model can serve as a possible alternative for manual diagnosis in ERM screening.
format	article
author	Enhua Shao Congxin Liu Lei Wang Dan Song Libin Guo Xuan Yao Jianhao Xiong Bin Wang Yuntao Hu
author_facet	Enhua Shao Congxin Liu Lei Wang Dan Song Libin Guo Xuan Yao Jianhao Xiong Bin Wang Yuntao Hu
author_sort	Enhua Shao
title	Artificial intelligence-based detection of epimacular membrane from color fundus photographs
title_short	Artificial intelligence-based detection of epimacular membrane from color fundus photographs
title_full	Artificial intelligence-based detection of epimacular membrane from color fundus photographs
title_fullStr	Artificial intelligence-based detection of epimacular membrane from color fundus photographs
title_full_unstemmed	Artificial intelligence-based detection of epimacular membrane from color fundus photographs
title_sort	artificial intelligence-based detection of epimacular membrane from color fundus photographs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a8fcc22dff5141bcb83ba249a08a3810
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Artificial intelligence-based detection of epimacular membrane from color fundus photographs

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