ArticleScientific reports2026
Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Anemia's high global prevalence and socio-economic burden necessitate early diagnosis, yet reliance on invasive blood testing creates significant barriers to diagnosis and treatment. 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 ms. 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.
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