AI-supported scans measuring heart fat could better predict cardiovascular risk

• A new study suggests that AI can measure heart fat from routine coronary artery calcium (CAC) scans without requiring additional tests.
• Higher levels of this heart fat were independently linked to a greater risk of developing cardiovascular disease over long-term follow-up.
• Adding the AI-derived heart fat measurement to existing risk models could significantly improve the accuracy of cardiovascular risk prediction.
• The study indicates this improvement may be especially useful for people at low or intermediate risk, helping better identify those who may benefit from earlier preventive care.

Cardiovascular diseases are the leading cause of death globally, and more than 60% of United States adults have at least one risk factor for these conditions.

Early diagnosis is crucial for managing the condition, preventing irreversible heart damage, and reducing hospitalization. However, early diagnosis can be challenging, as many heart diseases often develop silently without noticeable symptoms until advanced stages.

Coronary artery calcium (CAC) scans are a routine imaging test that measures calcium in the coronary arteries and can detect early signs of heart disease.

It is a quick and noninvasive procedure that can help predict an individual’s cardiovascular disease risk. As technology continues to improve, artificial intelligence (AI) techniques are showing great potential for improving the accuracy, efficiency, and timing of cardiovascular disease diagnosis.

Now, a new study suggests that using AI to measure fat around the heart, known as pericardial fat, using CAC scans could significantly improve the ability to predict a person’s risk of developing cardiovascular disease.

Prior research highlights a strong association between pericardial fat volume and cardiovascular disease.

The findings, presented at the American College of Cardiology Scientific Session 2026 and published in the American Journal of Preventive Cardiology, highlight how AI can extract additional clinically useful information from routine imaging tests.