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

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.