AI-Based Echocardiographic Age Estimation as a Cardiovascular Health Indicator

Overview

A deep learning model was developed to predict patient age from echocardiogram videos with high accuracy, achieving a mean absolute error of 6.76 years and strong correlation (R² = 0.732). Predicted biological age was associated with increased risks of coronary artery disease, heart failure, and stroke, demonstrating its potential as a marker of cardiovascular aging and risk.

Background

Cardiovascular diseases are the leading cause of mortality worldwide, necessitating improved methods for cardiac health assessment. Echocardiography provides detailed cardiac imaging, and recent advances in deep learning enable extraction of demographic and health-related features from these images. Predicting biological age from echocardiograms may help identify individuals with accelerated cardiovascular aging beyond chronological age, offering enhanced risk stratification. Large datasets and multi-view echocardiographic analysis improve model robustness and clinical relevance.

Data Highlights

Key Findings

• The ensemble deep learning model combining four echocardiographic views achieved a MAE of 6.76 years and R² of 0.732 internally.
• Model performance was consistent across multiple external validation cohorts with varying demographics.
• Predicted biological age correlated with increased risk of coronary artery disease, heart failure, and stroke.
• Model attention focused on mitral valve, mitral apparatus, and basal inferior wall regions, highlighting relevant cardiac structures.
• Survival analysis showed worse outcomes in patients with older predicted biological age compared to chronological age.
• Integration of biomarkers (creatinine, hemoglobin, BNP) with echocardiographic predictions maintained strong predictive accuracy.

Clinical Implications

This AI-based echocardiographic age prediction provides a non-invasive biomarker for cardiovascular biological aging, potentially improving risk stratification beyond chronological age. It may aid clinicians in identifying patients with accelerated cardiovascular aging who could benefit from targeted preventive interventions. The model’s robustness across diverse populations supports its applicability in varied clinical settings.

Conclusion

Deep learning analysis of echocardiographic videos enables accurate estimation of biological age, which is associated with cardiovascular risk and outcomes. This approach offers a promising tool to enhance cardiovascular risk assessment and guide personalized patient management.

References

1. Cedars-Sinai Medical Center Study -- Utilizing Artificial Intelligence to Estimate Age from Echocardiographic Data