AI sees what doctors miss: Fatty liver disease hidden in chest x-rays
Lifesaving deep learning model developed using standard radiographs.
- Date:
- June 27, 2025
- Source:
- Osaka Metropolitan University
- Summary:
- Researchers in Japan created an AI that can detect fatty liver disease from ordinary chest X-rays—an unexpected and low-cost method that could transform early diagnosis. The model proved highly accurate and may offer a fast, affordable way to flag this silent but serious condition.
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Fatty liver disease, caused by the accumulation of fat in the liver, is estimated to affect one in four people worldwide. If left untreated, it can lead to serious complications, such as cirrhosis and liver cancer, making it crucial to detect early and initiate treatment.
Currently, standard tests for diagnosing fatty liver disease include ultrasounds, CTs, and MRIs, which require costly specialized equipment and facilities. In contrast, chest X-rays are performed more frequently, are relatively inexpensive, and involve low radiation exposure. Although this test is primarily used to examine the condition of the lungs and heart, it also captures part of the liver, making it possible to detect signs of fatty liver disease. However, the relationship between chest X-rays and fatty liver disease has rarely been a subject of in-depth study.
Therefore, a research group led by Associate Professor Sawako Uchida-Kobayashi and Associate Professor Daiju Ueda at Osaka Metropolitan University's Graduate School of Medicine developed an AI model that can detect the presence of fatty liver disease from chest X-ray images.
In this retrospective study, a total of 6,599 chest X-ray images containing data from 4,414 patients were used to develop an AI model utilizing controlled attenuation parameter (CAP) scores. The AI model was verified to be highly accurate, with the area under the receiver operating characteristic curve (AUC) ranging from 0.82 to 0.83.
"The development of diagnostic methods using easily obtainable and inexpensive chest X-rays has the potential to improve fatty liver detection. We hope it can be put into practical use in the future," stated Professor Uchida-Kobayashi.
Story Source:
Materials provided by Osaka Metropolitan University. Note: Content may be edited for style and length.
Journal Reference:
- Daiju Ueda, Sawako Uchida-Kobayashi, Akira Yamamoto, Shannon L. Walston, Hiroyuki Motoyama, Hideki Fujii, Toshio Watanabe, Yukio Miki, Norifumi Kawada. Performance of a Chest Radiograph–based Deep Learning Model for Detecting Hepatic Steatosis. Radiology: Cardiothoracic Imaging, 2025; 7 (3) DOI: 10.1148/ryct.240402
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