An AI tool accurately detected structural heart disease from smartwatch single-lead ECGs in a 600-person prospective trial.
Key Details
- 1AI model was trained using 266,000 12-lead ECGs from 110,000 patients and validated on large external cohorts.
- 2Prospective real-world testing was performed with 600 adults using smartwatch-recorded single-lead ECGs.
- 3Algorithm achieved 88% accuracy (AUC) in smartwatch data; sensitivity was 86% and negative predictive value 99%.
- 4Diseases detected included weakened pumping, valve damage, or thickened heart muscle—most commonly found with cardiac ultrasound (echo).
- 5Preliminary results were presented at AHA Scientific Sessions 2025 and have not yet been peer-reviewed.
Why It Matters

Source
EurekAlert
Related News

NIH-Backed AI Model Predicts Cancer Survival Using Single-Cell Data
Researchers have developed scSurvival, a machine learning tool that uses single-cell tumor data to accurately predict cancer patient survival and identify high-risk cell populations.

Deep Learning Pathomics Platform Improves Immunotherapy Prediction in Lung Cancer
A deep learning pathomics platform accurately predicts immunotherapy response in metastatic NSCLC using routine pathology slides.

AI Pathology Model Outperforms PD-L1 in Predicting NSCLC Immunotherapy Response
MD Anderson's Path-IO machine learning platform accurately predicts immunotherapy responses in metastatic non-small cell lung cancer, surpassing current biomarker standards.