AI can PREDICT your heart's 'real' age! And it could save your life
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Synopsis
Researchers have developed an AI-driven ECG algorithm to predict heart age, revealing that a biological heart age exceeding chronological age by seven years significantly increases mortality and cardiovascular risk—offering a breakthrough in early disease detection and prevention.
Your heart might be older—or younger—than you think. While every heart has a chronological age matching its owner’s, its biological age can differ based on overall function and health.
Now, researchers have developed an AI-powered algorithm capable of predicting a heart’s biological age using standard 12-lead electrocardiograms (ECGs)—a breakthrough that could transform cardiovascular risk assessment.
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The study, presented on March 31 at EHRA 2025, a congress of the European Society of Cardiology (ESC), analyzed nearly half a million ECGs to build an advanced deep learning model. Researchers found that when a heart’s biological age exceeded its chronological age by seven years, the risk of all-cause mortality rose by 62%, while the likelihood of major adverse cardiovascular events (MACE) nearly doubled. Conversely, hearts estimated to be seven years younger than their chronological age had a 14% lower mortality risk and 27% lower MACE risk.
“Our research highlights the potential of AI-driven ECG analysis to provide a more precise indicator of cardiovascular risk than traditional assessments,” said Associate Professor Yong-Soo Baek from Inha University Hospital, South Korea.
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The deep learning model was trained on a dataset of 425,051 ECGs spanning 15 years and validated with an independent cohort of 97,058 ECGs. The AI-generated ECG heart age was strongly correlated with key cardiac electrical markers, including ejection fraction (EF), QRS duration, and corrected QT intervals—all of which play critical roles in heart function.
The study’s findings suggest AI ECG heart age could become a powerful tool for early detection and intervention, allowing doctors to identify at-risk individuals and take proactive steps to prevent cardiovascular disease.
Baek emphasized the need for larger studies to refine the model further, stating, “This research confirms AI’s transformative potential in clinical cardiology, enhancing early detection and preventive strategies to improve patient outcomes.”
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