{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:08:10Z","timestamp":1789517290718,"version":"build-2803163510"},"reference-count":0,"publisher":"SRUMB - Romanian Society for Ultrasonography in Medicine and Biology","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Med Ultrason"],"abstract":"<jats:p>Aim: This study aimed to develop a deep learning (DL) model for automatic detection and diagnosis of gouty arthritis\u00a0(GA) in the first metatarsophalangeal joint (MTPJ) using ultrasound (US) images.Materials and methods: A retrospective\u00a0study included individuals who underwent first MTPJ ultrasonography between February and July 2023. A five-fold cross-validation method (training set = 4:1) was employed. A deep residual convolutional neural network (CNN) was trained, and\u00a0Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visualization. Different ResNet18 models with varying residual blocks (2, 3, 4, 6) were compared to select the optimal model for image classification. Diagnostic decisions were\u00a0based on a threshold proportion of abnormal images, determined from the training set.Results: A total of 2401 US images\u00a0from 260 patients (149 gout, 111 control) were analyzed. The model with 3 residual blocks performed best, achieving an AUC\u00a0of 0.904 (95% CI: 0.887~0.927). Visualization results aligned with radiologist opinions in 2000 images. The diagnostic model\u00a0attained an accuracy of 91.1% (95% CI: 90.4%~91.8%) on the testing set, with a diagnostic threshold of 0.328.Conclusion:\u00a0The DL model demonstrated excellent performance in automatically detecting and diagnosing GA in the first MTPJ.<\/jats:p>","DOI":"10.11152\/mu-4495","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T13:20:20Z","timestamp":1742822420000},"page":"268","source":"Crossref","is-referenced-by-count":4,"title":["Deep learning-based automated detection and diagnosis of gouty arthritis in ultrasound images of the first metatarsophalangeal joint"],"prefix":"10.11152","volume":"27","author":[{"given":"Lishan","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhe","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengmeng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunping","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4840","published-online":{"date-parts":[[2025,9,17]]},"container-title":["Medical Ultrasonography"],"original-title":[],"link":[{"URL":"https:\/\/medultrason.ro\/medultrason\/index.php\/medultrason\/article\/viewFile\/4495\/2505","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/medultrason.ro\/medultrason\/index.php\/medultrason\/article\/viewFile\/4495\/2505","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T18:37:58Z","timestamp":1758134278000},"score":1,"resource":{"primary":{"URL":"https:\/\/medultrason.ro\/medultrason\/index.php\/medultrason\/article\/view\/4495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,17]]},"references-count":0,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9,17]]}},"URL":"https:\/\/doi.org\/10.11152\/mu-4495","relation":{},"ISSN":["2066-8643","1844-4172"],"issn-type":[{"value":"2066-8643","type":"electronic"},{"value":"1844-4172","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9,17]]}}}