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Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases

IMPORTANCE: The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. OBJECTIVE: To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus...

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Autores principales: Dong, Li, He, Wanji, Zhang, Ruiheng, Ge, Zongyuan, Wang, Ya Xing, Zhou, Jinqiong, Xu, Jie, Shao, Lei, Wang, Qian, Yan, Yanni, Xie, Ying, Fang, Lijian, Wang, Haiwei, Wang, Yenan, Zhu, Xiaobo, Wang, Jinyuan, Zhang, Chuan, Wang, Heng, Wang, Yining, Chen, Rongtian, Wan, Qianqian, Yang, Jingyan, Zhou, Wenda, Li, Heyan, Yao, Xuan, Yang, Zhiwen, Xiong, Jianhao, Wang, Xin, Huang, Yelin, Chen, Yuzhong, Wang, Zhaohui, Rong, Ce, Gao, Jianxiong, Zhang, Huiliang, Wu, Shouling, Jonas, Jost B., Wei, Wen Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066285/
https://www.ncbi.nlm.nih.gov/pubmed/35503220
http://dx.doi.org/10.1001/jamanetworkopen.2022.9960
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author Dong, Li
He, Wanji
Zhang, Ruiheng
Ge, Zongyuan
Wang, Ya Xing
Zhou, Jinqiong
Xu, Jie
Shao, Lei
Wang, Qian
Yan, Yanni
Xie, Ying
Fang, Lijian
Wang, Haiwei
Wang, Yenan
Zhu, Xiaobo
Wang, Jinyuan
Zhang, Chuan
Wang, Heng
Wang, Yining
Chen, Rongtian
Wan, Qianqian
Yang, Jingyan
Zhou, Wenda
Li, Heyan
Yao, Xuan
Yang, Zhiwen
Xiong, Jianhao
Wang, Xin
Huang, Yelin
Chen, Yuzhong
Wang, Zhaohui
Rong, Ce
Gao, Jianxiong
Zhang, Huiliang
Wu, Shouling
Jonas, Jost B.
Wei, Wen Bin
author_facet Dong, Li
He, Wanji
Zhang, Ruiheng
Ge, Zongyuan
Wang, Ya Xing
Zhou, Jinqiong
Xu, Jie
Shao, Lei
Wang, Qian
Yan, Yanni
Xie, Ying
Fang, Lijian
Wang, Haiwei
Wang, Yenan
Zhu, Xiaobo
Wang, Jinyuan
Zhang, Chuan
Wang, Heng
Wang, Yining
Chen, Rongtian
Wan, Qianqian
Yang, Jingyan
Zhou, Wenda
Li, Heyan
Yao, Xuan
Yang, Zhiwen
Xiong, Jianhao
Wang, Xin
Huang, Yelin
Chen, Yuzhong
Wang, Zhaohui
Rong, Ce
Gao, Jianxiong
Zhang, Huiliang
Wu, Shouling
Jonas, Jost B.
Wei, Wen Bin
author_sort Dong, Li
collection PubMed
description IMPORTANCE: The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. OBJECTIVE: To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies. EXPOSURES: Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study. MAIN OUTCOMES AND MEASURES: The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score. RESULTS: In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment. CONCLUSIONS AND RELEVANCE: In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas.
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spelling pubmed-90662852022-05-18 Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases Dong, Li He, Wanji Zhang, Ruiheng Ge, Zongyuan Wang, Ya Xing Zhou, Jinqiong Xu, Jie Shao, Lei Wang, Qian Yan, Yanni Xie, Ying Fang, Lijian Wang, Haiwei Wang, Yenan Zhu, Xiaobo Wang, Jinyuan Zhang, Chuan Wang, Heng Wang, Yining Chen, Rongtian Wan, Qianqian Yang, Jingyan Zhou, Wenda Li, Heyan Yao, Xuan Yang, Zhiwen Xiong, Jianhao Wang, Xin Huang, Yelin Chen, Yuzhong Wang, Zhaohui Rong, Ce Gao, Jianxiong Zhang, Huiliang Wu, Shouling Jonas, Jost B. Wei, Wen Bin JAMA Netw Open Original Investigation IMPORTANCE: The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases. OBJECTIVE: To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice. DESIGN, SETTING, AND PARTICIPANTS: This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies. EXPOSURES: Based on 120 002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study. MAIN OUTCOMES AND MEASURES: The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score. RESULTS: In the prospective validation data set of 208 758 images collected from 110 784 individuals (median [range] age, 42 [8-87] years; 115 443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment. CONCLUSIONS AND RELEVANCE: In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas. American Medical Association 2022-05-03 /pmc/articles/PMC9066285/ /pubmed/35503220 http://dx.doi.org/10.1001/jamanetworkopen.2022.9960 Text en Copyright 2022 Dong L et al. JAMA Network Open. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Dong, Li
He, Wanji
Zhang, Ruiheng
Ge, Zongyuan
Wang, Ya Xing
Zhou, Jinqiong
Xu, Jie
Shao, Lei
Wang, Qian
Yan, Yanni
Xie, Ying
Fang, Lijian
Wang, Haiwei
Wang, Yenan
Zhu, Xiaobo
Wang, Jinyuan
Zhang, Chuan
Wang, Heng
Wang, Yining
Chen, Rongtian
Wan, Qianqian
Yang, Jingyan
Zhou, Wenda
Li, Heyan
Yao, Xuan
Yang, Zhiwen
Xiong, Jianhao
Wang, Xin
Huang, Yelin
Chen, Yuzhong
Wang, Zhaohui
Rong, Ce
Gao, Jianxiong
Zhang, Huiliang
Wu, Shouling
Jonas, Jost B.
Wei, Wen Bin
Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases
title Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases
title_full Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases
title_fullStr Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases
title_full_unstemmed Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases
title_short Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases
title_sort artificial intelligence for screening of multiple retinal and optic nerve diseases
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066285/
https://www.ncbi.nlm.nih.gov/pubmed/35503220
http://dx.doi.org/10.1001/jamanetworkopen.2022.9960
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