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

Ling-Ping Cen, Jie Ji, Jian-Wei Lin, Si-Tong Ju, Hong-Jie Lin, Tai-Ping Li, Yun Wang, Jian-Feng Yang, Yu-Fen Liu, Shaoying Tan, Li Tan, Dongjie Li, Yifan Wang, Dezhi Zheng, Yongqun Xiong, Hanfu Wu, Jingjing Jiang, Zhenggen Wu, Dingguo Huang, Tingkun Shi, Binyao Chen, Jianling Yang, Xiaoling Zhang, Li Luo, Chukai Huang, Guihua Zhang, Yuqiang Huang, Tsz Kin Ng, Haoyu Chen, Weiqi Chen, Chi Pui Pang and Mingzhi Zhang ()
Additional contact information
Ling-Ping Cen: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Jie Ji: Shantou University
Jian-Wei Lin: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Si-Tong Ju: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Hong-Jie Lin: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Tai-Ping Li: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Yun Wang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Jian-Feng Yang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Yu-Fen Liu: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Shaoying Tan: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Li Tan: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Dongjie Li: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Yifan Wang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Dezhi Zheng: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Yongqun Xiong: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Hanfu Wu: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Jingjing Jiang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Zhenggen Wu: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Dingguo Huang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Tingkun Shi: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Binyao Chen: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Jianling Yang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Xiaoling Zhang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Li Luo: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Chukai Huang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Guihua Zhang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Yuqiang Huang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Tsz Kin Ng: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Haoyu Chen: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Weiqi Chen: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Chi Pui Pang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong
Mingzhi Zhang: Joint Shantou International Eye Centre of Shantou University and The Chinese University of Hong Kong

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-25138-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25138-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-25138-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().