A new study led by researchers at Wake Forest University School of Medicine has found that artificial intelligence (AI) can help clinicians identify signs of heart failure, including a type that is 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 using data collected from wearables, the finding suggests it could eventually be adapted to support 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 often requires an echocardiogram, a specialized imaging test that may not be readily available in every care setting. 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 a novel AI tool that analyzes data from a standard electrocardiogram (ECG) to help clinicians identify three types of heart dysfunction:
- Reduced ejection fraction, meaning the heart’s main pumping chamber is pumping substantially less blood than normal (rEF)
- Mildly reduced ejection fraction (mEF)
- Heart failure with preserved ejection fraction, or HFpEF, in which the heart pumps out a normal proportion of blood but does not fill or function normally
Ejection fraction measures the percentage of blood the heart’s main pumping chamber pushes out with each beat. HFpEF is especially challenging to detect in its early stages 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.”
“Some of these conditions can progress without noticeable symptoms and may not be found until they become more severe,” said Akbilgic. “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 the model using more than 1 million ECGs from Atrium Health Wake Forest Baptist. They then tested it using a separate set of more than 72,000 ECGs from the University of Tennessee Health Science Center to determine how well it performed in another patient population. The model classified ECGs into four categories: rEF, mEF, HFpEF or no dysfunction.
Researchers tested two versions: one model using 12-lead ECGs and one using a single lead ECG, similar to what wearable devices can collect.
The research team noted the following key findings:
- Both models performed similarly. The 12-lead model was particularly effective at distinguishing patients with reduced ejection fraction from those without it. Its performance was somewhat lower, but still potentially useful, for the other two forms of heart dysfunction.
- The single-lead model performed nearly as well as the 12-lead model, suggesting the technology could eventually be adapted for wearable devices.
- In pediatric patients, the model demonstrated a strong ability to detect reduced ejection fraction, performing as well as or better than previously studied models. Researchers said the results were encouraging, although the pediatric group was relatively small.
- The model generalized well across different demographic populations.
The research team is now piloting the model in a family medicine clinic at Atrium Health Wake Forest Baptist to study how it performs when incorporated into clinical care.
“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.
The study was partially funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health.
