Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Objective The biological age progression of the heart varies from person to person. We
developed a deep learning model (DLM) to predict the biological age via ECG to explore its …

Abstract Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been
estimated by both Bayesian and artificial intelligence approaches. We hypothesised that …

Background There is a paucity of data on artificial intelligence-estimated biological
electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular …

Background: 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 …

Background We have demonstrated that a neural network is able to predict a person's age
from the electrocardiogram (ECG)[artificial intelligence (AI) ECG age]. However, some …

Background There is increasing evidence that 12-lead electrocardiograms (ECG) can be
used to predict biological age, which is associated with cardiovascular events. However, the …

Biological age may be a more valuable predictor of morbidity and mortality than a person's
chronological age. Mathematical models have been used for decades to predict biological …

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of
cardiovascular diseases. Here we propose that the age predicted by artificial intelligence …

Objective The aim of the present study was to develop a neural network to characterize the
effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG …