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<p>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&#xa0;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.</p>

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Development of an anemia detection model in emergency departments using lip region images based on medical knowledge and deep learning technology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月20日

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  • Zhaofan Li,
  • Yugui Zhang,
  • Yuhang Tian,
  • Yizhan Gu,
  • Guanglei Wang,
  • Xin Ning,
  • Qingyue Duan,
  • Jiayi He,
  • Mingyue Zhu,
  • Yunhua Yu,
  • Lili Wang,
  • Li Chen

摘要

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.