An artificial intelligence algorithm paired with single-lead electrocardiogram sensors on smartwatches accurately diagnosed structural heart diseases including weakened pumping ability, damaged valves, or thickened heart muscle, according to research to be presented at the American Heart Association's Scientific Sessions 2025. This represents the first prospective study demonstrating that AI can detect multiple structural heart diseases using measurements from the single-lead ECG sensors found on consumer smartwatches.
While millions of people wear smartwatches that currently detect heart rhythm problems like atrial fibrillation, structural heart diseases have traditionally required echocardiograms - advanced ultrasound imaging tests needing specialized equipment not widely available for routine screening. The study investigated whether everyday smartwatches could help identify these hidden structural conditions earlier, before they progress to serious complications or cardiac events.
Researchers developed the AI algorithm using more than 266,000 12-lead ECG recordings from over 110,000 adults. 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 team isolated only one of the 12 leads of the ECG to resemble the single-lead configuration on smartwatches and accounted for random interference or noise that could occur during real-world smartwatch recordings.
The AI model underwent external validation using data from community hospital patients and the population-based ELSA-Brasil study. Researchers then prospectively recruited 600 participants who underwent 30-second, single-lead ECGs using a smartwatch to evaluate the algorithm's accuracy in real-world settings. The analysis revealed that using single-lead ECGs obtained from hospital equipment, the AI model was highly 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 smartwatches, the AI model maintained strong performance at 88% for detecting structural heart disease. The algorithm accurately identified most people with heart disease, showing 86% sensitivity, and was highly accurate in ruling out heart disease with 99% negative predictive value. Additional information about the study is available at https://www.heart.org.
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 received heart ultrasounds. The median age of participants was 62 years, with approximately half being women and diverse racial and ethnic representation. About 5% of participants were found to have structural heart disease on the heart ultrasound. Study limitations include a small number of patients with actual disease in the prospective study and some false positive results.
While a single-lead ECG alone is limited and cannot replace the 12-lead ECG tests available in healthcare settings, the integration with AI makes it powerful enough to screen for important heart conditions. This advancement could enable early screening for structural heart disease on a large scale using devices many people already own. Researchers plan to evaluate the AI tool in broader settings and explore integration into community-based heart disease screening programs to assess its potential impact on improving preventive care. More details about the research can be found in the abstract available through the American Heart Association's Scientific Sessions 2025 Online Program Planner at https://professional.heart.org.
