Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
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Published:2021-08-10
Issue:1
Volume:12
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Cen Ling-PingORCID, Ji Jie, Lin Jian-Wei, Ju Si-Tong, Lin Hong-Jie, Li Tai-Ping, Wang Yun, Yang Jian-Feng, Liu Yu-Fen, Tan Shaoying, Tan Li, Li Dongjie, Wang Yifan, Zheng Dezhi, Xiong Yongqun, Wu Hanfu, Jiang Jingjing, Wu Zhenggen, Huang Dingguo, Shi Tingkun, Chen Binyao, Yang Jianling, Zhang Xiaoling, Luo Li, Huang Chukai, Zhang Guihua, Huang Yuqiang, Ng Tsz Kin, Chen Haoyu, Chen WeiqiORCID, Pang Chi Pui, Zhang MingzhiORCID
Abstract
AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
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