Researchers have developed an Artificial Intelligence-enabled tool which uses Machine Learning (ML) algorithms that will soon play a critical role in predicting heart attacks and other cardiac issues.
The Coronary Computed Tomography Arteriography (CCTA) gives highly detailed images of the heart vessels and is a promising tool for refining risk assessment, said researchers in the study published in the journal Radiology.
While earlier tools like 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," said study lead author Kevin M. Johnson, Associate Professor at the Yale University.
The ML algorithm is able to pull out 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 nearly 7,000 patients. They followed the patients for an average of nine years after CCTA.
It was 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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