While Everybody’s Heart has an absolute chronological age (as old as that person is), Hearts also have a a theoretical “biological” biological “biological” age that is based on how the heart fans. So, someone who is 50 but has poor heart health count has a biological heart age of 60, while someone aged 50 with optimal heart health count from a biological heart age of 40.

Researchers Presenting a Study at Ehra 2025A scientific Congress of the European Society of Cardiology (ESC), Demonstrated that by Using Artificial Intelligence (AI) to Analyze Standard 12 -LECRCARDIGRAD (ECG) DATAN FROM Almost Half a Million cases, they were able to create an algorithm to predict the biological age of the heart. This algorithm could be used to identify that most at risk of Cardiovascular Events and Mortality.

“Our research showed that when the biological age of the heart Exced Its Chronological Age by Seven Years, The Risk of All-Cause Mortality and Major Adverse Cardiovascular Events Incrained Sharply,” Explains Associate Professor Yong-Soo Baek, Inha University Hospital, In South Korea.

“Convercely, if the algorithm estimated the biological heart as serven years younger than the chronological age, that reduced the risk of death and major adverse cardiovascular events.”

The Integration of Artificial Intelligence (AI) INTO Clinical Diagnostics Presents Novel Oportunities for Enhancing Predictive Accuracy in Cardiology.

“Using ai to develop algorithms in this way introduces a potential paradigm shift in cardiovascular risk assessment,” Says Associate Professor Baek.

Their study evaluated the prognostic capability of a deep-greed-based algorithm that calculates biological ecg heart age (AI ECG-Heart Age) from 12-grades, Consuming Against Traditional Chronological Age (CA) For Mortality and Cardiovascular Outcomes.

A Deep Neural Network was developed and trained on a substantial dataset of 425,051 12-LAD ECGS Collected Over Fifteen Years, With Subsequent Validation and Testing on an independent cohort of 97,058 Ecgs. Comparative Analyses Were Conducted Among Age and Sex-Matched Patients Differentiated by Ejection Fraction (EF).

In statistical models, an AI ECG-Heart Age Excesting The Heart’s Chronological Age by Seven Years was Associateed With An Incresed Risk of All-Cause Mortality by 62% and MACE by 92%. Ingtrast, an AI ECG Heart Age That Was Seven Years Younger Than Its Chronological Age Reduced The Risk of All-Cause Mortality by 14% and Mace By 27%.

Additional, subjects with reduced ejection Fraction Consistently Exhibited Increased AI ECG Heart Ages, Along with Prollonged Qrs Durations (The TAME TAME TAKEN For The Heart for the Heart for J Through the Ventricles, Causing Contraction) and Corrected Qt Intervals (The Total Time Needed for the Heart’s Electrical System to Complete One Cycle of Contraction and Relaxation.

The Author’s explain that the significance of the observed correlation between reduced ejection Fraction and Increased AI ECG Heart Ages, AlongSide ProLonged Qrs Durations Durations, Sugs ECG Heart Age Effectively Reflects Various Cardiac Depolarization and Repolarization Processes.

These indicators of electrical remodeling with the heart may significry underling cardiac health conditions and their association with ejection fraction (EF).

However, Associate Professor Baek explains, “It is crucial to obtain a statistically sufficient sample size in future studies to substANTIETETIETIETIETIETIETIETIETIETIETIETIETIETIETIETIETIE The Robustness and Applicability of Ai Ecg in Clinical Assessments of Cardiac Function and Health. “

He concludes, “Biological Heart Age Estimated by Artificial Intelligence from 12-Lad Electrocardograms is Strongly Associated with Increased Mortality and Cartevascular Events, Understand Its Utility in Enhancing Early Detection and Preventive Strategies in Cardiovascular Health Care. Outcomes. “