Artificial intelligence prediction of age from echocardiography as a marker for cardiovascular disease
AI-Powered Cardiovascular Age Prediction
This study leveraged deep learning on a massive dataset of echocardiogram videos (2.6M videos from 166k studies) to predict patient age, serving as a marker for cardiovascular aging. The AI model achieved a mean absolute error (MAE) of 6.76 years and an R² of 0.732 on an internal test set, maintaining consistent performance across four external cohorts. Predicted age was strongly associated with increased risk of coronary artery disease, heart failure, and stroke, outperforming chronological age as a predictor. Notably, the model captured 'rejuvenation' effects post-heart transplant. Guided backpropagation revealed the model's focus on the mitral valve and basal inferior wall, suggesting these regions are key indicators of cardiovascular aging. The study highlights the potential of computer vision in echocardiography for enhanced cardiovascular risk assessment and understanding biological aging.
Key Performance Indicators
Our AI model demonstrates robust accuracy and clinical relevance in predicting cardiovascular age.
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Mean Absolute Error (MAE) in Age Prediction
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Coefficient of Determination (R²) for Age Prediction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Predictive Modeling
Explores the use of AI to predict chronological and biological age from echocardiographic data, demonstrating strong correlations with cardiovascular outcomes.
Enterprise Process Flow
2.6M Echocardiogram Videos
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Deep Learning Model (ResNet R2+1D)
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Predict Age from Multiple Views
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Ensemble Model (Gradient Boosting)
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Cardiovascular Risk Assessment
Clinical Relevance
Highlights how AI-predicted age can serve as a superior risk predictor for CAD, heart failure, and stroke compared to chronological age, and its utility in identifying 'accelerated' vs. 'delayed' cardiac aging phenotypes.
| Outcome | Chronological Age HR (95% CI) | Predicted Age HR (95% CI) |
|---|---|---|
| All-Cause Mortality | 1.33 (1.28-1.37) | 1.42 (1.37-1.48) |
| Coronary Artery Disease | 1.15 (1.09-1.21) | 1.25 (1.17-1.34) |
| Heart Failure | 1.16 (1.10-1.21) | 1.21 (1.14-1.28) |
| Stroke | 1.07 (1.01-1.15) | 1.16 (1.06-1.26) |
Case Study: AI Detects 'Rejuvenation' Post-Heart Transplant
The model successfully identified a significant reduction in predicted age (from 62.57 to 57.32 years) in patients within one year following a heart transplant. This indicates the AI model captures the morphological and functional changes consistent with a younger, healthy donor heart, serving as a powerful surrogate for 'cardiac age' rather than just chronological age. This provides a unique, quantifiable measure of treatment efficacy and biological impact.
Interpretability & Mechanism
Details the interpretability of the model, showing its focus on key anatomical structures like the mitral valve and annulus, linking AI predictions to known biological aging markers.
Case Study: Mitral Valve & Basal Inferior Wall: Key Aging Markers
Guided backpropagation revealed that the AI model consistently focused on the crux cordis, mitral valve, mitral annulus, and basal inferior wall across multiple echocardiographic views. These areas are known to undergo age-related structural and functional changes, such as mitral annular calcification, which is a marker of biological aging and cardiovascular risk. This interpretability enhances the clinical relevance and biological plausibility of the model's predictions, providing targets for further clinical investigation.
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Estimated Annual Savings
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