Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs

Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person's age and self-reported sex using only 12-lead ECG signals. We further hypothesized that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health. We trained CNNs using 10-second samples of 12-lead ECG signals from 499 727 patients to predict sex and age.