A deep learning system for differential diagnosis of skin diseases
A deep learning system for differential diagnosis of skin diseases
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DOI：
10.1038/s41591-020-0842-3
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发表时间：
2020-05-18
期刊：
影响因子：
82.9
通讯作者：
Coz, David
中科院分区：
文献类型：
--
作者：
Liu, Yuan;Jain, Ayush;Coz, David
能够识别最常见皮肤病的深度学习系统可以帮助临床医生在常规临床实践中做出更准确的诊断皮肤病影响着19亿人。由于缺乏皮肤科医生，大多数病例都是由诊断准确率较低的全科医生来诊断的。我们提出了一个深度学习系统（DLS），使用来自17个站点的远程皮肤病学实践的16，114个去识别病例（照片和临床数据）来提供皮肤状况的鉴别诊断。DLS区分了26种常见的皮肤病，占初级保健病例的80%，同时还提供了涵盖419种皮肤病的二次预测。在963例验证病例中，由三名委员会认证的皮肤科医生组成的旋转小组定义了参考标准，DLS不劣于其他六名皮肤科医生，上级优于六名初级保健医生（PCP）和六名执业护士（NP）（前1名准确度：0.66 DLS，0.63皮肤科医生，0.44 PCP和0.40 NP）。这些结果突出了DLS帮助全科医生诊断皮肤病的潜力。
A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practiceSkin 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.