A deep learning system for differential diagnosis of skin diseases.
Journal:
Nature medicine
PMID:
32424212
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.
Authors
Keywords
Acne Vulgaris
Adult
Alaska Natives
Asian
Black or African American
Carcinoma, Basal Cell
Carcinoma, Squamous Cell
Deep Learning
Dermatitis, Seborrheic
Dermatologists
Diagnosis, Differential
Eczema
Female
Folliculitis
Hispanic or Latino
Humans
Indians, North American
Keratosis, Seborrheic
Male
Melanoma
Middle Aged
Native Hawaiian or Pacific Islander
Nurse Practitioners
Photography
Physicians, Primary Care
Psoriasis
Skin Diseases
Skin Neoplasms
Telemedicine
Warts
White People