Why the study?
Distinguishing biological from chronological age may help identify individuals with accelerated versus delayed cardiovascular aging and increased risk for adverse outcomes.
Does an AI-predicted biological age from echocardiography improve risk stratification for cardiovascular outcomes compared to chronological age?
Population
90,738 unique patients with 166,508 echocardiogram studies and 2,610,266 videos
Comparison
AI-predicted age from echocardiogram videos vs chronological age
Design
Deep learning model development and validation study
Key result
An artificial intelligence model predicted biological age from echocardiogram videos with a mean absolute error of 6.76 years, and a 10-year increase in predicted age was associated with higher all-cause mortality (HR 1.42).
Authors
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May aid mortality risk stratification via AI echo age; hypothesis-generating and requires prospective validation.
Observational (n=90,738)
Yes
Does an AI-predicted biological age from echocardiography improve risk stratification for cardiovascular outcomes compared to chronological age?
Effect estimate: HR 1.42 (95% CI 1.37-1.48)
p-value: p=<0.005
AI-derived biological age from echocardiograms outperforms chronological age in predicting long-term cardiovascular outcomes and mortality.
Rawlani et al. (2025) conducted an observational in Cardiovascular Disease (n=90,738). Deep learning artificial intelligence age prediction from echocardiogram videos vs. Chronological age was evaluated on All-cause mortality (per 10-year increase in predicted age) (HR 1.42, 95% CI 1.37-1.48, p=<0.005). An artificial intelligence model predicted biological age from echocardiogram videos with a mean absolute error of 6.76 years, and a 10-year increase in predicted age was associated with higher all-cause mortality (HR 1.42).
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