Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population

• Published in: Chinese medical journal Vol. 133; no. 17; pp. 2027 - 2036
• Main Authors: Wang, Shi-Qi, Zhang, Xin-Yuan, Liu, Jie, Tao, Cui, Zhu, Chen-Yu, Shu, Chang, Xu, Tao, Jin, Hong-Zhong
• Format: Journal Article
• Language: English
• Published: China Lippincott Williams & Wilkins 05-09-2020; Lippincott Williams & Wilkins Ovid Technologies; Wolters Kluwer
• Subjects: Accuracy; Acne; Algorithms; Artificial intelligence; Classification; Datasets; Deep learning; Dermatitis; Dermatology; Disease; Eczema; Inflammatory diseases; Machine learning; Neural networks; Original; Psoriasis; Skin cancer; Skin diseases; Tumors; China
• ISSN: 0366-6999; 2542-5641
• DOI: 10.1097/CM9.0000000000001023

Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists' efforts. We aimed to evaluate the classification sensitivity and specificity of deep learning models to classify skin tumors and psoriasis for Chinese population with a modest number of dermoscopic images.
We developed a convolutional neural network (CNN) based on two datasets from a consecutive series of patients who underwent the dermoscopy in the clinic of the Department of Dermatology, Peking Union Medical College Hospital, between 2016 and 2018, prospectively. In order to evaluate the feasibility of the algorithm, we used two datasets. Dataset I consisted of 7192 dermoscopic images for a multi-class model to differentiate three most common skin tumors and other diseases. Dataset II consisted of 3115 dermoscopic images for a two-class model to classify psoriasis from other inflammatory diseases. We compared the performance of CNN with 164 dermatologists in a reader study with 130 dermoscopic images. The experts' consensus was used as the reference standard except for the cases of basal cell carcinoma (BCC), which were all confirmed by histopathology.
The accuracies of multi-class and two-class models were 81.49% ± 0.88% and 77.02% ± 1.81%, respectively. In the reader study, for the multi-class tasks, the diagnosis sensitivity and specificity of 164 dermatologists were 0.770 and 0.962 for BCC, 0.807 and 0.897 for melanocytic nevus, 0.624 and 0.976 for seborrheic keratosis, 0.939 and 0.875 for the "others" group, respectively; the diagnosis sensitivity and specificity of multi-class CNN were 0.800 and 1.000 for BCC, 0.800 and 0.840 for melanocytic nevus, 0.850 and 0.940 for seborrheic keratosis, 0.750 and 0.940 for the "others" group, respectively. For the two-class tasks, the sensitivity and specificity of dermatologists and CNN for classifying psoriasis were 0.872 and 0.838, 1.000 and 0.605, respectively. Both the dermatologists and CNN achieved at least moderate consistency with the reference standard, and there was no significant difference in Kappa coefficients between them (P > 0.05).
The performance of CNN developed with relatively modest number of dermoscopic images of skin tumors and psoriasis for Chinese population is comparable with 164 dermatologists. These two models could be used for screening in patients suspected with skin tumors and psoriasis respectively in primary care hospital.