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To address this, we developed a deep learning model using the Detection Transformer framework for the rapid, non-invasive assessment of anemia severity in a real-world emergency department setting. Comparing a lip-focused model to a full-face approach, the former proved superior, achieving 85.0% accuracy. This significantly outperformed the full-face model (77.0%) and clinical judgments by both senior (59.3%) and junior (49.95%) physicians, with a rapid processing time of 127.50\u00a0ms. By integrating key medical knowledge to classify anemia into three severity levels, our model surpasses clinician performance, demonstrating its potential as a powerful, automated tool for clinical decision support.<\/jats:p>","DOI":"10.1038\/s41598-026-40210-5","type":"journal-article","created":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T03:35:28Z","timestamp":1781926528000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology"],"prefix":"10.1038","volume":"16","author":[{"given":"Zhaofan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yugui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhan","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanglei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyue","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunhua","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"key":"40210_CR1","unstructured":"World Health Organization. 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