TY  - JOUR
AU  - Huang, Kai
AU  - Wu, Xian
AU  - Li, Yixin
AU  - Lv, Chengzhi
AU  - Yan, Yangtian
AU  - Wu, Zhe
AU  - Zhang, Mi
AU  - Huang, Weihong
AU  - Jiang, Zixi
AU  - Hu, Kun
AU  - Li, Mingjia
AU  - Su, Juan
AU  - Zhu, Wu
AU  - Li, Fangfang
AU  - Chen, Mingliang
AU  - Chen, Jing
AU  - Li, Yongjian
AU  - Zeng, Mei
AU  - Zhu, Jianjian
AU  - Cao, Duling
AU  - Huang, Xing
AU  - Huang, Lei
AU  - Hu, Xing
AU  - Chen, Zeyu
AU  - Kang, Jian
AU  - Yuan, Lei
AU  - Huang, Chengji
AU  - Guo, Rui
AU  - Navarini, Alexander
AU  - Kuang, Yehong
AU  - Chen, Xiang
AU  - Zhao, Shuang
PY  - 2023
DA  - 2023/3/16
TI  - Artificial Intelligence–Based Psoriasis Severity Assessment: Real-world Study and Application
JO  - J Med Internet Res
SP  - e44932
VL  - 25
KW  - artificial intelligence
KW  - psoriasis severity assessment
KW  - Psoriasis Area and Severity Index
KW  - PASI
KW  - deep learning system
KW  - mobile app
KW  - psoriasis
KW  - inflammation
KW  - dermatology
KW  - tools
KW  - management
KW  - model
KW  - design
KW  - users
KW  - chronic disease
AB  - Background: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently. Objective: This study aimed to develop an image–artificial intelligence (AI)–based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis. Methods: A deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform. Results: The proposed image-AI–based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users. Conclusions: An image-AI–based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists’ accurate assessment in the real world and chronic disease self-management in patients with psoriasis. 
SN  - 1438-8871
UR  - https://www.jmir.org/2023/1/e44932
UR  - https://doi.org/10.2196/44932
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36927843
DO  - 10.2196/44932
ID  - info:doi/10.2196/44932
ER  -