Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Liu, Y; Jain, A; Eng, C; Way, DH; Lee, K; Bui, P; Kanada, K; de Oliveira Marinho, G; Gallegos, J; Gabriele, S; Gupta, V; Singh, N; Natarajan, V; Hofmann-Wellenhof, R; Corrado, GS; Peng, LH; Webster, DR; Ai, D; Huang, SJ; Liu, Y; Dunn, RC; Coz, D.
A deep learning system for differential diagnosis of skin diseases.
Nat Med. 2020; 26(6):900-908
Doi: 10.1038/s41591-020-0842-3
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- Co-authors Med Uni Graz
- Hofmann-Wellenhof Rainer
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- Abstract:
- Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.
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