AI Tool Uses Smartwatch ECG to Detect Structural Heart Disease

An artificial intelligence algorithm paired with the single-lead electrocardiogram sensors on a smartwatch accurately diagnosed structural heart diseases, according to a preliminary study to be presented at the American Heart Association's Scientific Sessions 2025. The research represents the first prospective study demonstrating that an AI algorithm can detect multiple structural heart diseases based on measurements taken from a single-lead ECG sensor on the back and digital crown of a smartwatch.

Researchers developed the AI algorithm using more than 266,000 12-lead ECG recordings from more than 110,000 adults who received testing and treatment at Yale New Haven Hospital between 2015 and 2023. Based on this extensive data library, they created an algorithm to identify structural heart disease from a single-lead ECG that can be obtained using smartwatch sensors. The algorithm was matched to heart ultrasound scans to determine whether patients had structural heart disease or not.

The AI model was externally validated using data from 44,591 adults seeking care at four community hospitals and 3,014 participants from the population-based ELSA-Brasil study. The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) gathers important information about how chronic diseases develop and progress, focusing mainly on cardiovascular diseases and diabetes. To prepare the AI model for interpreting signals from real-world, single-lead ECGs, researchers added simulated noise during model training, making the algorithm more resilient and reliable when dealing with less-than-perfect signals.

During the real-world prospective study, 600 patients wore the same type of smartwatch with a single-lead ECG sensor for 30 seconds on the same day they were getting a heart ultrasound. The analysis found that using single-lead ECGs obtained from hospital equipment, the AI model was very effective at distinguishing people with and without structural heart disease, scoring 92% on a standard performance scale. Among the 600 participants with single-lead ECGs obtained from a smartwatch, the AI model maintained high performance at 88% for detecting structural heart disease.

The AI algorithm accurately identified most people with heart disease, demonstrating 86% sensitivity, and was highly accurate in ruling out heart disease with 99% negative predictive value. Study author Arya Aminorroaya, M.D., M.P.H., an internal medicine resident at Yale New Haven Hospital, explained that millions of people wear smartwatches currently used mainly to detect heart rhythm problems such as atrial fibrillation, while structural heart diseases are usually found with echocardiograms requiring special equipment not widely available for routine screening.

Rohan Khera, M.D., M.S., the senior author of the study and director of the Cardiovascular Data Science Lab, noted that while a single-lead ECG alone is limited and cannot replace a 12-lead ECG test available in healthcare settings, when combined with AI it becomes powerful enough to screen for important heart conditions. This approach could make early screening for structural heart disease possible on a large scale using devices many people already own. The study limitations include a small number of patients with actual disease in the prospective study and the number of false positive results. Researchers plan to evaluate the AI tool in broader settings and explore integration into community-based heart disease screening programs.