Artificial intelligence age prediction using electrocardiogram data: Exploring biological age differences

Abstract

Background: Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events.

Objective: We developed an AI model to predict age from an ECG and compared baseline characteristics to identify determinants of advanced biological age.

Methods: An AI model was trained on ECGs from cardiology inpatients aged 20-90 years. AI analysis used a convolutional neural network with data divided in an 80:20 ratio (development/internal validation), with external validation undertaken using data from the UK Biobank. Performance and subgroup comparison measures included correlation, difference, and mean absolute difference.

Results: A total of 63,246 patients with 353,704 total ECGs were included. In internal validation, the correlation coefficient was 0.72, with a mean absolute difference between chronological age and AI-predicted age of 9.1 years. The same model performed similarly in external validation. In patients aged 20-29 years, AI-ECG-predicted biological age was greater than chronological age by a mean of 14.3 ± 0.2 years. In patients aged 80-89 years, biological age was lower by a mean of 10.5 ± 0.1 years. Women were biologically younger than men by a mean of 10.7 months (P = .023), and patients with a single ECG were biologically 1.0 years younger than those with multiple ECGs (P < .0001).

Conclusion: There are significant between-group differences in AI-ECG-predicted biological age for patient subgroups. Biological age was greater than chronological age in young hospitalized patients and lower than chronological age in older hospitalized patients. Women and patients with a single ECG recorded were biologically younger than men and patients with multiple recorded ECGs.

Keywords: Cardiology; Convolutional neural network; Machine learning; Prognostication; deep learning.