S Zhao1,2,3, B Xie4, Y Li4, X Zhao4, Y Kuang1,2,3, J Su6, X He4, X Wu5, W Fan5, K Huang1,2,3, J Su6, Y Peng6, A A Navarini7,8, W Huang9, X Chen1,2,3. 1. Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China. 2. Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China. 3. Hunan Engineering Research Center of Skin Health and Disease, Changsha, China. 4. School of Information Science and Engineering, Central South University, Changsha, China. 5. Tencent Medical AI Lab, Beijing, China. 6. Faculty of Computer Science, University of Sunderland, Sunderland, UK. 7. Department of Dermatology, University Hospital of Basel, Basel, Switzerland. 8. Department of Biomedical Engineering, University Hospital of Basel, Basel, Switzerland. 9. Mobile Health Ministry of Education - China Mobile Joint Laboratory, Xiangya Hospital, Central South University, Changsha, China.
Abstract
BACKGROUND: Psoriasis is a chronic inflammatory skin disease, which holds a high incidence in China. However, professional dermatologists who can diagnose psoriasis early and correctly are insufficient in China, especially in the rural areas. A smart approach to identify psoriasis by pictures would be highly adaptable countrywide and could play a useful role in early diagnosis and regular treatment of psoriasis. OBJECTIVES: Design and evaluation of a smart psoriasis identification system based on clinical images (without relying on a dermatoscope) that works effectively similar to a dermatologist. METHODS: A set of deep learning models using convolutional neural networks (CNNs) was explored and compared in the system for automatic identification of psoriasis. The work was carried out on a standardized dermatological dataset with 8021 clinical images of 9 common disorders including psoriasis along with full electronic medical records of patients built over the last 9 years in China. A two-stage deep neural network was designed and developed to identify psoriasis. In the first stage, a multilabel classifier was trained to learn the visual patterns for each individual skin disease. In the second stage, the output of the first stage was utilized to distinguish psoriasis from other skin diseases. RESULTS: The area under the curve (AUC) of the two-stage model reached 0.981 ± 0.015, which outperforms a single-stage model. And, the classifier showed superior performance (missed diagnosis rate: 0.03, misdiagnosis rate: 0.04) than 25 Chinese dermatologists (missed diagnosis rate: 0.19, misdiagnosis rate: 0.10) in the diagnosis of psoriasis on 100 clinical images. CONCLUSIONS: Using clinical images to identify psoriasis is feasible and effective based on CNNs, which also builds a solid technical base for smart care of skin diseases especially psoriasis using mobile/tablet applications for teledermatology in China.
BACKGROUND:Psoriasis is a chronic inflammatory skin disease, which holds a high incidence in China. However, professional dermatologists who can diagnose psoriasis early and correctly are insufficient in China, especially in the rural areas. A smart approach to identify psoriasis by pictures would be highly adaptable countrywide and could play a useful role in early diagnosis and regular treatment of psoriasis. OBJECTIVES: Design and evaluation of a smart psoriasis identification system based on clinical images (without relying on a dermatoscope) that works effectively similar to a dermatologist. METHODS: A set of deep learning models using convolutional neural networks (CNNs) was explored and compared in the system for automatic identification of psoriasis. The work was carried out on a standardized dermatological dataset with 8021 clinical images of 9 common disorders including psoriasis along with full electronic medical records of patients built over the last 9 years in China. A two-stage deep neural network was designed and developed to identify psoriasis. In the first stage, a multilabel classifier was trained to learn the visual patterns for each individual skin disease. In the second stage, the output of the first stage was utilized to distinguish psoriasis from other skin diseases. RESULTS: The area under the curve (AUC) of the two-stage model reached 0.981 ± 0.015, which outperforms a single-stage model. And, the classifier showed superior performance (missed diagnosis rate: 0.03, misdiagnosis rate: 0.04) than 25 Chinese dermatologists (missed diagnosis rate: 0.19, misdiagnosis rate: 0.10) in the diagnosis of psoriasis on 100 clinical images. CONCLUSIONS: Using clinical images to identify psoriasis is feasible and effective based on CNNs, which also builds a solid technical base for smart care of skin diseases especially psoriasis using mobile/tablet applications for teledermatology in China.