AI Algorithm Uses Smartwatch ECG Sensors to Detect Structural Heart Diseases

An artificial intelligence algorithm paired with the single-lead electrocardiogram sensors on smartwatches accurately diagnosed structural heart diseases such as weakened pumping ability, damaged valves, or thickened heart muscle, according to a preliminary study to be presented at the American Heart Association's Scientific Sessions 2025. This represents the first prospective study showing that an AI algorithm can detect multiple structural heart diseases based on measures taken from a single-lead ECG sensor on smartwatches. Millions of people wear smartwatches, which are currently mainly used to detect heart rhythm problems such as atrial fibrillation. Structural heart diseases, however, are typically found with echocardiograms, advanced ultrasound imaging tests that require special equipment and aren't widely available for routine screening.

The study explored whether everyday smartwatches could help identify these hidden structural heart diseases 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 more than 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. For this purpose, researchers isolated only one of the 12 leads of the ECG, which resembles the single-lead ECG on smartwatches. They also accounted for random interference in ECG signaling or noise that could arise during real-world smartwatch recordings.

The AI model was externally validated using data from people seeking care at community hospitals, as well as data from the population-based ELSA-Brasil study. Researchers then prospectively recruited 600 participants who underwent 30-second, single-lead ECGs using a smartwatch to gauge the algorithm's accuracy in real-world settings. The analysis revealed 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 smartwatches, the AI model maintained high performance at 88% for detecting structural heart disease.

The AI 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. On its own, a single-lead ECG is limited and cannot replace a 12-lead ECG test available in healthcare settings. However, with AI, it becomes powerful enough to screen for important heart conditions. This could make early screening for structural heart disease possible on a large scale using devices many people already own. 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 were getting a heart ultrasound. The median age of participants was 62 years, and about half were women, with diverse racial and ethnic representation. Approximately 5% of participants were found to have structural heart disease on the heart ultrasound. Study limitations include a small number of patients with the 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 how it could be integrated into community-based heart disease screening programs to assess its potential impact on improving preventive care. The findings are considered preliminary until published as a full manuscript in a peer-reviewed scientific journal. 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.