{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:46:44Z","timestamp":1790232404391,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T00:00:00Z","timestamp":1714003200000},"content-version":"vor","delay-in-days":24,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Experimental Dermatology"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010term monitoring and precise evaluation pose difficulties for dermatologists and patients, which is time\u2010consuming, 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\u2010quality images from 1486 psoriasis patients, and images were segmented and annotated. Then, we utilized the YOLO\u2010v4 algorithm to establish the model via four modules, we also conducted a human\u2010computer comparison through quadratic weighted Kappa (QWK) coefficients and intra\u2010class correlation coefficients (ICC). The YOLO\u2010v4 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\u2009=\u20090.903; scale, mAP\u2009=\u20090.908; and induration, mAP\u2009=\u20090.882. In addition, the results of human\u2010computer 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\u2010v4 algorithm that can assist dermatologists in long\u2010term and objective PASI scoring, shedding light on similar clinical assessments that can be assisted by computers in a time\u2010saving and objective manner.<\/jats:p>","DOI":"10.1111\/exd.15082","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T04:10:41Z","timestamp":1714104641000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Image\u2010based remote evaluation of PASI scores with psoriasis by the YOLO\u2010v4 algorithm"],"prefix":"10.1111","volume":"33","author":[{"given":"Heng","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Dermatology The Second Xiangya Hospital of Central South University; Hunan Key Laboratory of Medical Epigenomics  Changsha Hunan 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 Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Yu","sequence":"additional","affiliation":[{"name":"General Hospital of Ningxia Medical University  Yinchuan Ningxia China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingyao","family":"Yu","sequence":"additional","affiliation":[{"name":"General Hospital of Ningxia Medical University  Yinchuan Ningxia China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6135-1486","authenticated-orcid":false,"given":"Hai","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Dermatology The Second Xiangya Hospital of Central South University; Hunan Key Laboratory of Medical Epigenomics  Changsha Hunan 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