{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:33:35Z","timestamp":1792690415875,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002476","name":"Eulji University","doi-asserted-by":"publisher","award":["EJRG-25-17"],"award-info":[{"award-number":["EJRG-25-17"]}],"id":[{"id":"10.13039\/501100002476","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Pressure injury stage classification was performed using object detection models to address inconsistencies in clinical assessment due to variability in nurses\u2019 experience and education levels. Methods: A dataset of 1282 pressure injury images from a medical institution was used to train and compare five representative architectures, YOLOv5x, YOLOv7, YOLOv8x, YOLOv8n, and YOLOv11x, and Faster R-CNN across Stages 1\u20134, excluding Deep Tissue Injury and unclassified cases. A mobile application incorporating YOLOv7 was deployed at Eulji University Daejeon Medical Center and tested by 10 nurses over 2 weeks, processing 46 cases. Results: YOLOv7 demonstrated superior performance with mAP@0.5 of 0.97 and mAP@0.5:0.95 of 0.68, achieving 93% accuracy for Stage 2 classification, the most challenging diagnostic category. Clinical validation demonstrated 87% diagnostic accuracy, 4.0\/5 user satisfaction, and workflow improvement with assessment time reduced from 4\u20136 min to 1 min. The application proved valuable as both a diagnostic support tool and educational resource for novice nurses, with zero critical misclassifications recorded. Conclusions: This study establishes the practical utility of AI-based pressure injury classification systems in clinical practice and their potential for enhancing nursing competency and workflow efficiency.<\/jats:p>","DOI":"10.3390\/diagnostics16050747","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T15:22:21Z","timestamp":1772551341000},"page":"747","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clinical Validation of Object Detection Models for AI-Based Pressure Injury Stage Classification"],"prefix":"10.3390","volume":"16","author":[{"given":"Sang Hyun","family":"Jang","sequence":"first","affiliation":[{"name":"Department of Neurology, Daejeon Medical Center, Eulji University, Daejeon 35233, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhwa","family":"Ihm","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, Daejeon Medical Center, Eulji University, Daejeon 35233, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3958-7867","authenticated-orcid":false,"given":"Jun-Woo","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Medical Artificial Intelligence, Eulji University, Seongnam 13135, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0439-5973","authenticated-orcid":false,"given":"Dong-Hun","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Medical Artificial Intelligence, Eulji University, Seongnam 13135, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyunghwa","family":"Bae","sequence":"additional","affiliation":[{"name":"Department of Quality improvement, Daejeon Medical Center, Eulji University, Daejeon 35233, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3931-7430","authenticated-orcid":false,"given":"Minsoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Bigdata Medical Convergence, Eulji University, Seongnam 13135, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"ref_1","unstructured":"Bergstrom, N. 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