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, and that discrepancies between CNN-predicted age and chronological age may serve as a physiological measure of health. Upon training CNNs using 10-second samples of 499727 12-lead ECGs, we found that the model was 90.4% accurate in sex classification and age was estimated as a continuous variable with an average error of 6.9+/-5.6 years. The study found that the major factors seen among patients with a CNN-predicted age that exceeded chronological age by >7 years included: low ejection fraction, hypertension, and coronary disease. We found that applying AI to the ECG allows prediction of patient sex, and estimation of age. The ability of an AI algorithm to determine psychological age, with further validation, may serve as a measure of overall health.
Anumana Appoints Kevin Ballinger and Jean-Luc Butel to Board of Directors
