Researchers at Wake Forest University School of Medicine announced on Aug. 6 that artificial intelligence can help clinicians identify signs of heart failure, including a type often missed in routine care. The AI model also performed well using data from a single ECG lead, similar to the measurement captured by some wearable devices. Although the model was not tested with data collected from wearables, researchers said this finding suggests it could eventually be adapted for more accessible screening.
Heart failure affects more than 6 million Americans and is a leading cause of hospitalization and death. Early detection is important, but evaluating heart function typically requires an echocardiogram—a specialized imaging test that may not be available in every care setting. Researchers said an AI-assisted ECG could eventually help clinicians identify patients who may benefit from further evaluation.
The study, published in the Journal of the American Heart Association, introduces an AI tool that analyzes data from standard electrocardiograms to help clinicians identify three types of heart dysfunction. Ejection fraction measures the percentage of blood the heart's main pumping chamber pushes out with each beat; one type called HFpEF is especially challenging to detect early and is often overlooked during routine clinical evaluations.
"This is a major step forward in how we can use everyday clinical tools to catch heart failure earlier," said Oguz Akbilgic, Ph.D., corresponding author and professor of artificial intelligence in the Department of Cardiovascular Medicine at Wake Forest University School of Medicine. "Our AI model can detect various types of heart dysfunction from a simple, single-lead ECG alone—the same lead configuration captured by many smartwatches and wearable ECG devices—suggesting the model could eventually be adapted for wearable-based screening." Akbilgic added, "Some of these conditions can progress without noticeable symptoms and may not be found until they become more severe. Our model helps fill that gap by identifying electrical patterns in the heart that humans can't easily see so clinicians can decide when additional heart failure evaluation is needed."
Researchers developed their model using over 1 million ECGs from Atrium Health Wake Forest Baptist and tested it on more than 72,000 ECGs from the University of Tennessee Health Science Center to determine its performance with another patient population. The model classified ECGs into four categories: rEF, mEF, HFpEF or no dysfunction; two versions were tested—one using 12-lead ECGs and one using single-lead data similar to what wearable devices collect.
The research team is now piloting their AI tool in a family medicine clinic at Atrium Health Wake Forest Baptist to observe its impact when incorporated into clinical care settings. "We're testing the tool in a real-world health care setting to determine whether it can help clinicians identify patients who need additional evaluation and how it might affect care and resource use," Akbilgic said.
