❤️ AI Predicts Heart Age from Echo Videos with High Accuracy

A Cedars-Sinai study used deep learning to estimate biological age from echocardiogram videos, analyzing data from over 90,000 patients. The model accurately predicted age and linked its predictions to cardiovascular risk and post-transplant changes.

Why It Matters To Your Practice

▪ AI-driven age prediction could flag patients at higher cardiovascular risk, beyond chronological age.

▪ Automated analysis may streamline risk stratification in busy echo labs.

▪ Early identification of accelerated cardiac aging can inform preventive strategies.

▪ Integration into existing workflows could enhance patient management.

Clinical Implications

▪ Model performance (MAE 6.76 years, R2 0.732) was consistent across five cohorts.

▪ Predicted age correlated with risk of CAD, heart failure, and stroke.

▪ Discontinuities in predicted age after heart transplant suggest sensitivity to structural cardiac change.

▪ Model may help refine risk assessment for at-risk populations.

Insights

▪ Deep learning focused on mitral valve, apparatus, and basal inferior wall regions.

▪ AI may reveal subclinical cardiac aging not visible to the human eye.

▪ Large, diverse dataset supports generalizability of findings.

▪ Potential to inform future AI tools for personalized cardiac care.

The Bottom Line

▪ AI models using echo videos can estimate biological heart age, correlate with disease risk, and may soon enhance cardiovascular risk assessment in clinical practice.