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Cited by 346

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Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

Open access
Cen, Ling-Ping, 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, Weiqi, Pang, Chi Pui, Zhang, Mingzhi·2021·Nature Communications

Abstract

Retinal 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.

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Metadata

Authors
Cen, L.-P., Ji, J., Lin, J.-W., Ju, S.-T., Lin, H.-J., Li, T.-P., Wang, Y., Yang, J.-F., Liu, Y.-F., Tan, S., Tan, L., Li, D., Wang, Y., Zheng, D., Xiong, Y., Wu, H., Jiang, J., Wu, Z., Huang, D., Shi, T., Chen, B., Yang, J., Zhang, X., Luo, L., Huang, C., Zhang, G., Huang, Y., Ng, T. K., Chen, H., Chen, W., Pang, C. P., & Zhang, M.
Venue
Nature Communications
Published
2021-08-10
Volume
12
Issue
1
Pages
4828-4828
ISSN
2041-1723 (ISSN-L)
OpenAlex
W3191934798
Cited by
346
References
44
FWCI
23.81
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. https://app.cochat.ai/papers/10.1038/s41467-021-25138-w
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Cen, L.-P., Ji, J., Lin, J.-W., Ju, S.-T., Lin, H.-J., Li, T.-P., Wang, Y., Yang, J.-F., Liu, Y.-F., Tan, S., Tan, L., Li, D., Wang, Y., Zheng, D., Xiong, Y., Wu, H., Jiang, J., Wu, Z., Huang, D., . . . Zhang, M. (2021). Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Nature Communications. https://doi.org/10.1038/s41467-021-25138-w

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