Scientists have developed an artificial intelligence (AI) based system that can better predict heart attacks and other cardiac events as compared to conventional risk models. Risk determination is an imperfect science, and popular existing models like the Framingham Risk Score have limitations, as they do not directly consider the condition of the coronary arteries.
Coronary computed tomography arteriography (CCTA), that gives highly detailed images of the heart vessels, is a promising tool for refining risk assessment, according to a study published in the journal Radiology. The decision-making tool, known as the coronary artery disease reporting and data system (CAD-RADS), emphasises stenoses, or blockages and narrowing in the coronary arteries.
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 Kevin M Johnson, associate professor at the Yale School of Medicine in the US.
Noting that CCTA shows more than just stenoses, Johnson investigated machine learning (ML) system capable of mining the myriad details in these images for a more comprehensive prognostic picture. "Starting from the ground up, I took imaging features from the coronary CT," Johnson said.
"Each patient had 64 of these features and I fed them into a machine learning algorithm. The 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," he said.
The researchers compared the ML approach with CAD-RADS and other vessel scoring systems in 6,892 patients. They followed the patients for an average of nine years after CCTA.
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