AI-Driven Heart Age Assessment Predicts Mortality and Cardiovascular Risk

AI-Driven Heart Age Assessment Predicts Mortality and Cardiovascular Risk

(IN BRIEF) Researchers have developed an AI algorithm that predicts a heart’s “biological” age using standard 12-lead ECG data. Presented at EHRA 2025, the study found that a biological heart age that is seven years older than the actual age increases the risk of all-cause mortality by 62% and doubles the risk of major cardiovascular events. Conversely, a heart appearing seven years younger significantly lowers these risks. Trained on over 425,000 ECGs and validated on nearly 100,000 additional recordings, the algorithm also showed that patients with reduced ejection fraction tend to have higher predicted heart ages and prolonged electrical intervals. The research highlights the potential of AI to revolutionize cardiovascular risk assessments and support early intervention strategies, though further studies with larger cohorts are needed to enhance its clinical application.

(PRESS RELEASE) VIENNA, 1-Apr-2025 — /EuropaWire/ — Researchers have discovered that while everyone’s heart has a chronological age, it also possesses a “biological” age that reflects its functional health. Presenting their findings at EHRA 2025—a congress hosted by the European Society of Cardiology—scientists revealed an AI-powered algorithm capable of predicting the heart’s biological age using data from standard 12-lead electrocardiograms (ECGs). The study, which analyzed nearly half a million ECGs, found that when the predicted biological age exceeds a person’s actual age by seven years, the risk of death from any cause increases by 62%, while the risk of major adverse cardiovascular events (MACE) nearly doubles. Conversely, a heart that appears seven years younger than the chronological age is linked to a lower risk of adverse outcomes.

The innovative approach relies on deep learning and a robust neural network trained on 425,051 ECGs collected over fifteen years, followed by validation on an independent set of 97,058 ECGs. The algorithm outperformed traditional chronological age measurements, particularly when comparing patients with varying ejection fractions. Notably, individuals with reduced ejection fraction showed higher AI-predicted heart ages, as well as longer QRS durations and corrected QT intervals—key indicators of electrical remodelling in the heart.

Associate Professor Yong-Soo Baek from Inha University Hospital in South Korea emphasized the significance of these findings. “Our research demonstrates that an AI-determined heart age exceeding chronological age by seven years is a strong predictor of increased mortality and cardiovascular events,” he explained. He also pointed out that these results highlight the promise of integrating AI into clinical diagnostics to enhance cardiovascular risk assessments and inform early intervention strategies. Professor Baek stressed that further studies with larger sample sizes are essential to validate and expand the applicability of these promising results.

This breakthrough underscores the transformative potential of AI in cardiology, offering a powerful tool for early detection and prevention of heart disease by accurately reflecting the underlying electrical and functional state of the heart.

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References:

1 – Biological age, also known as physiological or functional age, refers to how old a person’s body and its functions appear to be, based on factors like cellular damage and health status, rather than just the number of years he or she has lived.

2 – The standard 12-lead electrocardiogram is a representation of the heart’s electrical activity recorded from 12 electrodes on the body surface. The electrodes are placed in certain positions to provide the most accurate picture of this electrical activity.

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SOURCE: European Society of Cardiology (ESC)