NPJ Digit Med. 2026 Jul 11. doi: 10.1038/s41746-026-02646-x. Online ahead of print.
ABSTRACT
Portable, scalable, and accessible artificial intelligence (AI)-enabled smartwatch technology shows promise as a cardiovascular risk stratification strategy in the general adult population. The challenges of lifelong care in congenital heart disease (CHD)-inclusive of regional and socioeconomic disparities worldwide-underscore the need for similar solutions tailored to this population. Herein, we present the first noise-adapted single-lead ECG model for predicting left ventricular systolic dysfunction (left ventricular ejection fraction [LVEF] ≤ 40%) in pediatric and CHD patients. The internal cohort was comprised of 70,226 patients. External test groups included Children's Hospital of Philadelphia (CHOP; 42,984 patients) and Toronto General Hospital (TGH; 284 repaired tetralogy of Fallot patients). Our model had strong performance across a broad range of CHD lesions, races, ages, and healthcare systems. Our findings support the potential of AI-enabled wearables to expand global access to CHD care. Prospective studies utilizing wearable ECG devices in pediatric and CHD patients are warranted.
PMID:42436248 | DOI:10.1038/s41746-026-02646-x