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Liu, Yuan; Jain, Ayush; Eng, Clara; Way, David H; Lee, Kang; Bui, Peggy; Kanada, Kimberly; de Oliveira Marinho, Guilherme; Gallegos, Jessica; Gabriele, Sara; Gupta, Vishakha; Singh, Nalini; Natarajan, Vivek; Hofmann-Wellenhof, Rainer; Corrado, Greg S; Peng, Lily H; Webster, Dale R; Ai, Dennis; Huang, Susan J; Liu, Yun; Dunn, R Carter; Coz, David
Nature medicine, 06/2020, Letnik: 26, Številka: 6Journal Article
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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in: SICRIS
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