A research team led by Associate Professors Sawako Uchida and Daiju Ueda from the Graduate School of Medicine at Osaka Metropolitan University has published an innovative study in Radiology: Cardiothoracic Imaging, successfully developing an AI model that detects fatty liver using chest X-ray images. This technology offers a more accessible diagnostic solution for the approximately one-quarter of the global population affected by fatty liver disease.

The team trained the model on 6,599 chest X-ray images from 4,414 patients, using controlled attenuation parameter (CAP) scores as the reference standard. The AI achieved excellent diagnostic performance with an AUC of 0.82–0.83. Professor Uchida stated: "This approach leverages routine chest X-rays to improve fatty liver detection rates while reducing diagnostic costs."

Compared to conventional ultrasound, CT, and MRI, chest X-rays are lower cost, involve lower radiation exposure, and are widely available. Although primarily used for screening cardiopulmonary diseases, the study confirms their effectiveness for early fatty liver detection. This breakthrough provides a new strategy for large-scale fatty liver screening and supports earlier diagnosis and treatment.

The team's next step is to advance clinical translation of the AI model, integrating it as a powerful complement to existing diagnostic systems. The technology is particularly suitable for regions with limited medical resources and is expected to significantly increase fatty liver screening coverage.