Deep survival analysis from adult and pediatric electrocardiograms: a multi-center benchmark study

These findings highlight several key factors for robust AI-ECG deployment. Deep survival methods consistently provided advantages over horizon-based classifiers, while inclusion of demographic covariates such as age and sex improved predictive performance across sites. The sensitivity of classifier-based models to horizon selection underscores the need for site-specific calibration. The multi-site experiment reveals that cross-cohort training, even between adult and pediatric cohorts, can substantially improve performance on those cohorts compared to cohort-specific training. Together, these results emphasize the importance of model type, demographic features, and training data diversity in developing AI-ECG models that can be reliably applied across populations.