Artificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease.

ABSTRACT: An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance. The AI-enhanced ECG algorithm demonstrated high sensitivity (0.82-0.84) when detecting CAD in patients with normal ECGs and gave remarkably high prediction rates among those with abnormal ECGs, both with and without ischemia (92 %-

SUBMITTER: Yeh CH 

PROVIDER: S-EPMC11774641 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature