AI predicts heart attacks better than conventional risk models

The ML algorithm is able to pull out the patterns in the data and predict that patients with certain patterns are more likely to have an adverse event like a heart attack than patients with other patterns.

Scientists have developed a machine learning system that that will soon be able to predict the risk of heart attacks and other cardiac events better than existing risk models.

Machine learning (ML) is a type of artificial intelligence.

Coronary computed tomography arteriography (CCTA) is a kind of CT that gives highly detailed images of the heart vessels and is a promising tool for refining risk assessment, according to ew reaserch published in Radiology.

While earlier tools such as the Coronary Artery Disease Reporting and Data System (CAD-RADS) emphasise on stenoses or blockages and narrowing in the coronary arteries, CCTA shows more than just stenoses.

“While CAD-RADS is an important and useful development in the management of cardiac patients, its focus on stenoses may leave out important information about the arteries,” according to study lead author Kevin M. Johnson, associate professor of radiology and biomedical imaging at the Yale School of Medicine.

The ML algorithm is able to pull out the patterns in the data and predict that patients with certain patterns are more likely to have an adverse event like a heart attack than patients with other patterns.

For the study, the research team compared the ML approach with CAD-RADS and other vessel scoring systems in in 6,892 patients. They followed the patients for an average of nine years after CCTA.

The researchers found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not.

“The risk estimate that you get from doing the Machine Learning version of the model is more accurate than the risk estimate you’re going to get if you rely on CAD-RADS,” Johnson said.

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