AI-Enabled ECG Analysis Predicts Heart Attack Risk Nearly as well as CT Scans

Increased coronary artery calcium is a marker of coronary artery disease that can lead to a heart attack. Traditionally, CT scans are used to diagnose buildup of coronary artery calcium, although CT scanners are not available at all centers, are expensive, and expose patients to radiation. On the other hand, heart ultrasounds - also called echocardiograms - can be performed in a clinic or doctor’s office, do not produce radiation, and are much less expensive. Now, a coronary artery calcium study has shown for the first time that an artificial intelligence (AI) algorithm can read ultrasound images of the heart to accurately identify whether a patient has a large amount of buildup in their coronary arteries.

Using a dataset of 2,881 echocardiogram images, investigators at Cedars-Sinai (Los Angeles, CA, USA) trained a video-based AI tool to predict coronary artery calcium scores. Scores ranged from zero - representing a “perfect” score with no indication of coronary artery calcium buildup - to over 2,000, indicating poor prognosis for individuals, representing a high risk of heart attack and coronary artery disease. The video-based deep learning model was successful in predicting the scores of zero in patients with good health as well as high coronary calcium scores, possibly foreshadowing a worse future prognosis. The researchers hope that the efficient technology - inclusive of a coronary artery calcium score for each patient – can be used in all echocardiogram laboratories.

“We show that echocardiograms, when interpreted with our AI software, can predict coronary artery calcium and predict heart attack risk nearly as well as CT scans,” said senior author David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute and a researcher in the Division of AIM. “This proved true even in cases where the naked eye of an expert reader sees the ultrasound image of the heart as appearing fairly normal.”

This type of resource, Ouyang says, “will allow for faster, potentially more frequent, and generally more cost-effective imaging that provides clinically valuable, predictive information.”

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