期刊
SCIENTIFIC REPORTS
卷 8, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-018-35044-9
关键词
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资金
- Heidelberg Engineering
- Carl Zeiss Meditec
- Centervue
- Genentech
- Konan Medical
- National Eye Institute
- Optos
- Optovue Research to Prevent Blindness
- EyeSight Foundation of Alabama
- Research to Prevent Blindness
- Research to Prevent Blindness (New York, NY)
- Topcon Medical System Inc.
- NEI [EY11008, P30 EY022589, EY026590, EY022039, EY021818, EY023704, EY029058, T32 EY026590, R21 EY027945]
- NATIONAL EYE INSTITUTE [R01EY027510, R01EY021818, T32EY026590, R01EY023704, R01EY022039, R21EY027945, R01EY026574, R01EY011008, P30EY022589, R01EY029058] Funding Source: NIH RePORTER
The ability of deep learning architectures to identify glaucomatous optic neuropathy (GON) in fundus photographs was evaluated. A large database of fundus photographs (n = 14,822) from a racially and ethnically diverse group of individuals (over 33% of African descent) was evaluated by expert reviewers and classified as GON or healthy. Several deep learning architectures and the impact of transfer learning were evaluated. The best performing model achieved an overall area under receiver operating characteristic (AUC) of 0.91 in distinguishing GON eyes from healthy eyes. It also achieved an AUC of 0.97 for identifying GON eyes with moderate-to-severe functional loss and 0.89 for GON eyes with mild functional loss. A sensitivity of 88% at a set 95% specificity was achieved in detecting moderate-to-severe GON. In all cases, transfer improved performance and reduced training time. Model visualizations indicate that these deep learning models relied on, in part, anatomical features in the inferior and superior regions of the optic disc, areas commonly used by clinicians to diagnose GON. The results suggest that deep learning-based assessment of fundus images could be useful in clinical decision support systems and in the automation of large-scale glaucoma detection and screening programs.
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