Cedars-Sinai research shows deep learning model could improve AFib detection

A new artificial intelligence approach developed by investigators in Cedars-Sinai’s Los Angeles-based Smidt Heart Institute has been shown to detect abnormal heart rhythms associated with atrial fibrillation that might otherwise be unnoticed by physicians.

WHY IT MATTERS
Researchers at Smidt Heart Institute say the findings point to the potential for artificial intelligence to be used more widely in cardiac care.

In a recent study, published in npj Digital Medicine, Cedars-Sinai clinicians show how the deep learning model was developed to analyze images from echocardiogram imaging, in which sound waves show the heart’s rhythm.

Researchers trained a program to study more than 100,000 echocardiogram videos from patients with atrial fibrillation, they explain. The model distinguished between echocardiograms showing a heart in sinus rhythm – normal heartbeats – and those showing a heart in an irregular heart rhythm.

The program was able to predict which patients in sinus rhythm had experienced – or would develop – atrial fibrillation within 90 days, they said, noting that the AI model evaluating the images performed better than estimating risk based on known risk factors.

“We were able to show that a deep learning algorithm we developed could be applied to echocardiograms to identify patients with a hidden abnormal heart rhythm disorder called atrial fibrillation,” explained Dr. Neal Yuan, a staff scientist with the Smidt Heart Institute.

“Atrial fibrillation can come and go,” he added, “so it might not be present at a doctor’s appointment. This AI algorithm identifies patients who might have atrial fibrillation even when it is not present during their echocardiogram study.”

THE LARGER TREND
The Smidt Heart Institute is the biggest cardiothoracic transplant center in California and the third-largest in the United States.

An estimated 12.1 million people in the United States will have atrial fibrillation in 2030, according to the CDC. During AFib the heart’s upper chambers sometimes beat in sync with the lower chamber and sometimes they do not – making the arrhythmia often difficult for clinicians to detect. In some patients, the condition causes no symptoms at all.

Researchers say a machine learning model trained to analyze echo imaging could help clinicians detect early and subtle changes in the hearts of patients with undiagnosed arrhythmias.

Indeed, AI has long shown big promise for early detection of AFib, as evidenced by similar studies at health systems such as Geisinger and Mayo Clinic.

ON THE RECORD
“We’re encouraged that this technology might pick up a dangerous condition that the human eye would not while looking at echocardiograms,” said Dr. David Ouyang, a cardiologist and AI researcher in the Smidt Heart Institute. “It might be used for patients at risk for atrial fibrillation or who are experiencing symptoms associated with the condition.”

“The fact that this program predicted which patients had active or hidden atrial fibrillation could have immense clinical applications,” added Dr. Christine M. Albert, chair of the Department of Cardiology at the Smidt Heart Institute. “Being able to identify patients with hidden atrial fibrillation could allow us to treat them before they experience a serious cardiovascular event.”

Mike Miliard is executive editor of Healthcare IT News
Email the writer: mike.miliard@himssmedia.com
Healthcare IT News is a HIMSS publication.

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