News Release

AI finds a way to people’s hearts (literally!)

Unveiling a groundbreaking and accurate AI-based method to classify cardiac function and disease using chest X-Rays

Peer-Reviewed Publication

Osaka Metropolitan University

Evaluation of Left Ventricular Ejection Fraction

image: Left: Chest radiograph Right: Visualization of the grounds for the AI's judgment view more 

Credit: Daiju Ueda, OMU


Osaka, Japan - AI (artificial intelligence) may sound like a cold robotic system, but Osaka Metropolitan University scientists have shown that it can deliver heartwarming—or, more to the point, “heart-warning”—support. They unveiled an innovative use of AI that classifies cardiac functions and pinpoints valvular heart disease with unprecedented accuracy, demonstrating continued progress in merging the fields of medicine and technology to advance patient care. The results will be published in The Lancet Digital Health.

Valvular heart disease, one cause of heart failure, is often diagnosed using echocardiography. This technique, however, requires specialized skills, so there is a corresponding shortage of qualified technicians. Meanwhile, chest radiography is one of the most common tests to identify diseases, primarily of the lungs. Even though the heart is also visible in chest radiographs, little was known heretofore about the ability of chest radiographs to detect cardiac function or disease. Chest radiographs, or chest X-Rays, are performed in many hospitals and very little time is required to conduct them, making them highly accessible and reproducible. Accordingly, the research team led by Dr. Daiju Ueda, from the Department of Diagnostic and Interventional Radiology at the Graduate School of Medicine of Osaka Metropolitan University, reckoned that if cardiac function and disease could be determined from chest radiographs, this test could serve as a supplement to echocardiography.

Dr. Ueda’s team successfully developed a model that utilizes AI to accurately classify cardiac functions and valvular heart diseases from chest radiographs. Since AI trained on a single dataset faces potential bias, leading to low accuracy, the team aimed for multi-institutional data. Accordingly, a total of 22,551 chest radiographs associated with 22,551 echocardiograms were collected from 16,946 patients at four facilities between 2013 and 2021. With the chest radiographs set as input data and the echocardiograms set as output data, the AI model was trained to learn features connecting both datasets.

The AI model was able to categorize precisely six selected types of valvular heart disease, with the Area Under the Curve, or AUC, ranging from 0.83 to 0.92. (AUC is a rating index that indicates the capability of an AI model and uses a value range from 0 to 1, with the closer to 1, the better.) The AUC was 0.92 at a 40% cut-off for detecting left ventricular ejection fraction—an important measure for monitoring cardiac function.

“It took us a very long time to get to these results, but I believe this is significant research,” stated Dr. Ueda. “In addition to improving the efficiency of doctors’ diagnoses, the system might also be used in areas where there are no specialists, in night-time emergencies, and for patients who have difficulty undergoing echocardiography.”



About OMU 

Osaka Metropolitan University is the third largest public university in Japan, formed by a merger between Osaka City University and Osaka Prefecture University in 2022. OMU upholds "Convergence of Knowledge" through 11 undergraduate schools, a college, and 15 graduate schools. For more research news, visit or follow us on Twitter: @OsakaMetUniv_en, or Facebook

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.