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Artificial intelligence to predict heart attack

Investigators from Cedars-Sinai have created an artificial intelligence-enabled tool that may make it easier to predict if a person will have a heart attack.

Based on the volume and type of plaque in arteries that carry blood to the heart, the tool accurately identified which individuals will have a heart attack in five years, according to a study published in The Lancet Digital Health.

Plaque buildup can restrict arteries, making it difficult for blood to reach the heart and raising the risk of a heart attack. A coronary Computed Tomography Angiography (CTA) is a diagnostic procedure that takes 3D images of the heart and arteries and can tell doctors how much a patient’s arteries have narrowed. There hasn’t been a straightforward, automated, or quick technique to quantify the plaque shown in CTA photos until now.

“Coronary plaque is often not measured because there is not a fully automated way to do it,” said Damini Dey, PhD of Cedars-Sinai and senior author of the study. “When it is measured, it takes an expert at least 25 to 30 minutes, but now we can use this program to quantify plaque from CTA images in five to six seconds.”

Dey and colleagues looked at CTA photos from 1,196 persons who had a coronary CTA at 11 different locations in Australia, Germany, Japan, Scotland, and the US. The researchers taught the AI programme how to measure plaque by teaching it from 921 coronary CTA pictures that had already been evaluated by expert doctors.

The method works by first creating 3D images of the coronary arteries and then recognizing the blood and plaque deposits within them. The tool’s measurements matched the plaque amounts seen in Coronary CTA’s, according to the researchers. They also compared pictures from Intravascular Ultrasonography and catheter-based Coronary Angiography, two invasive techniques that are believed to be very accurate in detecting coronary artery plaque and constriction.

Ultimately, the studies found that parameters measured from CTA pictures by an AI algorithm accurately predicted heart attack risk within five years for 1,611 patients who took part in the SCOT-HEART experiment, a multicenter trial.

“More studies are needed, but it’s possible we may be able to predict if and how soon a person is likely to have a heart attack based on the amount and composition of the plaque imaged with this standard test,” said Dey.

Dey and his teammates are still investigating how well their AI algorithm measures plaque deposits in coronary CTA patients.

Artificial intelligence to predict heart attack

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