Artificial intelligence prediction of age from echocardiography as a marker for cardiovascular disease.

ABSTRACT: While chronological age is a universal risk predictor across most populations and diseases, distinguishing between biologically older from younger individuals may identify individuals with accelerated or delayed cardiovascular aging. This study presents a deep learning model to predict age from echocardiogram videos, leveraging 2,610,266 videos from 166,508 studies from 90,738 patients. Leveraging multi-view echocardiography, our model achieved a mean absolute error (MAE) of 6.76 (6.65-6.87) years and a coefficient of determination (R²) of 0.732 (0.72-0.74) on Cedars-Sinai Medical Center test set. Consistent performance was observed across four external validation cohorts. Predictions were associated with increased risk of coronary artery disease, heart failure, and stroke, and

SUBMITTER: Rawlani M 

PROVIDER: S-EPMC12627499 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature