Researchers at Mayo Clinic developed an Artificial Intelligence tool that interprets ECG data to identify plaques lining the walls of blood vessels. The study, which aims to enable earlier detection of coronary heart disease and prevent heart attacks, appeared in the journal eClinicalMedicine.
Atherosclerosis involves fatty deposits that narrow or block arteries, raising the risk of stroke and heart attack. When the heart’s oxygen supply is disrupted, coronary heart disease can develop. Often there are no early symptoms, and the first warning may be a sudden cardiac event or death.
The recent research evaluated an ECG-AI system designed to spot coronary disease at an early stage. The team analyzed health records from more than seven million U.S. patients to train the models. Three algorithms focused on identifying calcium buildup in the coronary arteries, detecting arterial occlusions, and recognizing impaired movement in segments of the left ventricle. Changes in these areas can signal an impending heart attack.
When used together, the three independent ECG-AI models enabled risk stratification by highlighting patients who might harbor hidden coronary artery disease and therefore face a higher chance of future heart attacks. Those individuals could benefit from preventive measures and, in some cases, medical therapy.
An ECG is a readily accessible test that records the heart’s electrical activity and requires minimal time and expense. Early researchers suggest that combining ECG-AI insights with standard prognosis tools could improve survival by guiding more proactive management.
A prior line of inquiry emphasized how lifestyle choices influence risk. A simple approach—regular physical activity and reduced sedentary time—continues to be associated with lower mortality rates, underscoring the value of preventive strategies in heart health.