期刊
NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-021-25138-w
关键词
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资金
- National Natural Science Foundation of China (NSFC) [81570849, 81800822]
- Natural Science Foundation of Guangdong Province, China (NSFG) [2020A1515010415]
- Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program [002-18119101]
- Joint Shantou International Eye Center of The Shantou University
- Chinese University of Hong Kong [17-003]
- [2020LKSFG16B]
The developed deep learning platform can effectively detect multiple retinal diseases and conditions, demonstrating high accuracy and efficiency.
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. Systems for automatic detection of a single disease may miss other important conditions. Here, the authors show a deep learning platform can detect 39 common retinal diseases and conditions.
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