New AI-enabled tool may help detect heart attacks

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.

There were 380 deaths from all causes, including 70 from coronary artery disease. In addition, 43 patients reported heart attacks.

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

When deciding whether to start statins, the ML score ensured that 93 per cent of patients with events would receive the drug, compared with only 69 per cent if CAD-RADS were relied on.

"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.

"Both methods perform better than just using the Framingham risk estimate. This shows the value of looking at the coronary arteries to better estimate people's risk," he said.

If machine learning can improve vessel scoring, it would enhance the contribution of noninvasive imaging to cardiovascular risk assessment.

The ML-derived vessel scores could be combined with non-imaging risk factors such as age, gender, hypertension and smoking to develop more comprehensive risk models. This would benefit both physicians and patients.

"Once you use a tool like this to help see that someone's at risk, then you can get the person on statins or get their glucose under control, get them off smoking, get their hypertension controlled, because those are the big, modifiable risk factors," he said.