In a groundbreaking development presented at EHRA 2025, a scientific congress of the European Society of Cardiology, researchers have unveiled a new algorithm leveraging artificial intelligence to estimate the biological age of the heart based on standard 12-lead electrocardiogram (ECG) data. This innovative approach aims to enhance cardiovascular risk prediction and improve patient outcomes. By analyzing the heart’s electrical activity through nearly half a million ECG recordings, the study illuminates the disparity between chronological and biological heart age, leading to critical insights regarding cardiovascular health.
The concept of biological age, often defined as the functional age of an individual’s organs relative to their chronological age, places significant emphasis on the health status of the heart as it can vary widely among individuals of the same age. For instance, a 50-year-old with optimal heart health may exhibit a biological heart age of 40, while another individual of the same age with significant cardiovascular issues could have a biological heart age of 60. This distinction is instrumental in understanding individual predispositions to cardiovascular events and mortality risk.
Researchers utilized advanced machine learning techniques to develop an algorithm capable of predicting biological heart age. Associate Professor Yong-Soo Baek of Inha University Hospital in South Korea highlighted that their study’s findings indicate that when biological heart age exceeds chronological age by seven years, the risk of all-cause mortality and major adverse cardiovascular events (MACE) escalates significantly. Notably, the algorithm also revealed that a biological heart age that is seven years younger than chronological age is associated with a decreased risk of these adverse outcomes, underscoring the importance of heart health beyond mere chronological markers.
The study analyzed a vast dataset of 425,051 standard 12-lead ECGs collected over a period of fifteen years. A deep learning model was trained to assess ECG features that inform biological heart age, comparing these findings against traditional assessments determined by chronological age. The subsequent results were validated against a separate cohort of 97,058 ECGs, demonstrating the robustness of the algorithm and its potential to predict mortality and cardiovascular health risks accurately.
Statistical analyses revealed alarming correlations that could reshape cardiovascular risk assessments. An AI-derived biological heart age exceeding the individual’s chronological age by seven years was linked to a striking 62% increase in the risk of all-cause mortality and a staggering 92% rise in the risk of MACE. On the flip side, an AI biological heart age seven years younger than chronological age corresponded with a 14% reduction in all-cause mortality and a 27% decrease in MACE risk.
An important aspect of the study is its findings concerning the ejection fraction, a key measure of heart function that indicates how well the heart pumps blood. The results consistently indicated that subjects with reduced ejection fractions exhibited higher AI biological heart ages in conjunction with prolonged QRS durations and corrected QT intervals. These metrics, indicative of the heart’s electrical signaling and its overall health, suggest deep underlying cardiac conditions that the AI algorithm may effectively monitor.
The implications of these findings extend far beyond academic research. The integration of AI in cardiovascular assessment signifies a transformative shift in how clinicians may approach patient evaluations. The ability to utilize AI-driven insights to refine cardiac health assessments has the potential to streamline patient management strategies in clinical settings, allowing healthcare providers to identify high-risk patients for early intervention.
Furthermore, the correlation established between AI biological heart age and other cardiac parameters reflects the need for continued exploration into the intricacies of heart health. The researchers emphasize that obtaining larger and more statistically significant samples in future studies will be critical for validating these findings further, enhancing the applicability of their algorithm in real-world clinical practice.
The value of an AI-driven approach in predicting heart age and related risks shines a light on the future of personalized medicine. Tailoring cardiovascular risk assessments to the biological age of the heart rather than solely relying on chronological age can usher in a new era of preventative cardiovascular care, potentially saving countless lives by prioritizing early detection and intervention strategies for those at risk.
In conclusion, the revolutionary potential of this AI-based algorithm indicates a significant step forward in cardiovascular health assessment. As healthcare continues to evolve, the insights derived from this technology could refine how clinicians assess heart health, ensuring that individuals receive appropriate care based on their unique physiological status rather than merely their age. This study heralds a promising future for integrating artificial intelligence into healthcare to not only understand but also improve cardiovascular health outcomes on a population scale.
Subject of Research: AI-Based Algorithm for Predicting Biological Heart Age
Article Title: Novel AI Algorithm Predicts Biological Heart Age, Enhancing Cardiovascular Risk Assessment
News Publication Date: 31 March 2025
Web References: ESC Press Office
References: Not applicable
Image Credits: Not applicable
Keywords: AI, biological heart age, cardiovascular health, ECG, predictive analytics, ejection fraction, machine learning, cardiovascular risk assessment
