Image-based remote evaluation of PASI scores with psoriasis by the YOLO-v4 algorithm

As a chronic relapsing disease, psoriasis is characterized by widespread skin lesions. The Psoriasis Area and Severity Index (PASI) is the most frequently utilized tool for evaluating the severity of psoriasis in clinical practice. Nevertheless, long-term monitoring and precise evaluation pose difficulties for dermatologists and patients, which is time-consuming, subjective and prone to evaluation bias. To develop a deep learning system with high accuracy and speed to assist PASI evaluation, we collected 2657 high-quality images from 1486 psoriasis patients, and images were segmented and annotated. Then, we utilized the YOLO-v4 algorithm to establish the model via four modules, we also conducted a human-computer comparison through quadratic weighted Kappa (QWK) coefficients and intra-class correlation coefficients (ICC). The YOLO-v4 algorithm was selected for model training and optimization compared with the YOLOv3, RetinaNet, EfficientDet and Faster_rcnn. The model evaluation results of mean average precision (mAP) for various lesion features were as follows: erythema, mAP = 0.903; scale, mAP = 0.908; and induration, mAP = 0.882. In addition, the results of human-computer comparison also showed a median consistency for the skin lesion severity and an excellent consistency for the area and PASI score. Finally, an intelligent PASI app was established for remote disease assessment and course management, with a pleasurable agreement with dermatologists. Taken together, we proposed an intelligent PASI app based on the image YOLO-v4 algorithm that can assist dermatologists in long-term and objective PASI scoring, shedding light on similar clinical assessments that can be assisted by computers in a time-saving and objective manner.