In Vienna, researchers from Inha University Hospital in South Korea presented a study at EHRA 2025 demonstrating an AI algorithm that calculates biological heart age using ECG data. The algorithm analyzed nearly half a million cases to predict cardiovascular risks, revealing that a seven-year discrepancy between biological and chronological age significantly impacts mortality and major adverse cardiovascular events (MACE). When the biological age exceeded chronological age by seven years, all-cause mortality risk increased by 62%, while MACE risk rose by 92%. Conversely, a younger biological age reduced these risks. The study highlights AI’s potential to enhance early detection and preventive strategies in cardiology.
Introduction to Biological Heart Age
Biological heart age refers to how old a person’s heart appears based on its functional state, rather than their chronological age. This concept is crucial because it reflects the actual health of the heart, which can differ significantly from one’s chronological age. For instance, someone in their 30s might have a biological heart age closer to 50 due to factors like high blood pressure or poor lifestyle choices.
The study employs artificial intelligence (AI) to analyze electrocardiogram (ECG) data, specifically using a method called AI ECG-heart age. This approach involves training an algorithm on nearly half a million ECG readings to estimate biological heart age and identify patterns that may indicate underlying cardiovascular issues. By analyzing electrical signals from the heart, the algorithm can provide insights into heart function beyond traditional metrics.
The methodology centers on leveraging AI to analyze ECG data for estimating biological heart age. The algorithm was trained using a dataset of nearly half a million ECG readings, enabling it to identify patterns and correlations that may signal cardiovascular issues. This approach allows for a more nuanced understanding of heart health compared to conventional methods.
Key findings reveal that when the estimated biological heart age exceeds chronological age by seven years or more, there is a notable increase in the risk of cardiovascular events and mortality. This discrepancy highlights potential underlying issues with heart health that may not be immediately apparent through standard assessments. Additionally, the study underscores the importance of QRS duration and QT intervals as critical indicators of heart electrical activity, which can signal subtle abnormalities in heart function.
The implications of this research are significant for cardiovascular care. By leveraging AI to predict risks more accurately, healthcare providers can implement targeted preventive measures and interventions earlier, potentially improving patient outcomes. This approach not only enhances diagnostic precision but also opens avenues for personalized medicine tailored to individual heart health profiles.
Looking ahead, the integration of AI with ECG analysis presents a promising direction for assessing cardiovascular health. Future research could explore how this method can be expanded to include other cardiac metrics and how it might be integrated into routine clinical practice. Additionally, advancements in AI algorithms could further refine the accuracy of biological heart age estimation, offering even more precise insights into heart health.
In conclusion, applying AI to ECG data represents a significant step forward in understanding and managing cardiovascular health. By providing deeper insights into heart function, this approach can revolutionize how we assess and treat cardiac conditions in the future.
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