Dermatologist‐level classification of malignant lip diseases using a deep convolutional neural network

作者：Soo Ick Cho, Shiding Sun, Je‐Ho Mun, C. Kim, Sang Youn Kim, Soyun Cho, Sung Won Youn, Hyunjun Kim, Jin Ho Chung · 发表于：British Journal of Dermatology · 年份：2019 · DOI：10.1111/bjd.18459 · 被引用次数：58 · 研究领域：Cutaneous Melanoma Detection and Management、Oral Health Pathology and Treatment、Nonmelanoma Skin Cancer Studies

BACKGROUND: Deep convolutional neural networks (DCNNs) can classify skin diseases at a level equivalent to a dermatologist, but their performance in specific areas requires further research. OBJECTIVE: To evaluate the performance of a trained DCNN-based algorithm in classifying benign and malignant lip diseases. METHODS: A training set of 1629 images (743 malignant, 886 benign) was used with Inception-Resnet-V2. Performance was evaluated using another set of 344 images and 281 images from other hospitals. Classifications by 44 participants (six board-certified dermatologists, 12 dermatology residents, nine medical doctors not specialized in dermatology and 17 medical students) were used for comparison. RESULTS: The outcomes based on the area under curve, sensitivity and specificity were 0·827 [95% confidence interval (CI) 0·782-0·873], 0·755 (95% CI 0·673-0·827) and 0·803 (95% CI 0·752-0·855), respectively, for the set of 344 images; and 0·774 (95% CI 0·699-0·849), 0·702 (95% CI 0·579-0·808) and 0·759 (95% CI 0·701-0·813), respectively, for the set of 281 images. The DCNN was equivalent to the dermatologists and superior to the nondermatologists in classifying malignancy. After referencing the DCNN result, the mean ± SD Youden index increased significantly for nondermatologists, from 0·201 ± 0·156 to 0·322 ± 0·141 (P < 0·001). CONCLUSIONS: DCNNs can classify lip diseases at a level similar to dermatologists. This will help unskilled physicians discriminate between benign and ...