Why the study?
Heart disease is the primary cause of death after age 65, and its prevalence is expected to starkly increase with global population aging.
Can deep learning accurately predict heart age using cardiac magnetic resonance videos and electrocardiograms in a large biobank cohort?
Population
45,000 individuals aged 45-81 years from the UK Biobank cohort
Comparison
MRI-based anatomical features vs ECG-based electro-physiological features for deep learning heart age prediction
Design
Cohort study
Key result
A deep learning model trained on heart MRI videos, ECGs, and biobank data predicted chronological age with a root mean squared error of 2.81±0.02 years and an R-squared of 85.6±0.2%.
Authors
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Supports DL-based cardiac age estimation in research; leaves open validation for clinical risk stratification or outcomes.
Observational (n=45,000)
Can deep learning accurately predict heart age using cardiac magnetic resonance videos and electrocardiograms in a large biobank cohort?
Effect estimate: RMSE 2.81±0.02 years
Deep learning can accurately predict heart age from cardiac MRI and ECGs, demonstrating that accelerated heart aging is highly heritable and primarily driven by anatomical features such as the aorta, mitral valve, and interventricular septum.
Goallec et al. (2021) conducted an observational in General population (n=45,000). Deep learning model using heart MRI videos, ECGs, and scalar biomarkers was evaluated on Prediction of chronological age (RMSE 2.81±0.02 years). A deep learning model trained on heart MRI videos, ECGs, and biobank data predicted chronological age with a root mean squared error of 2.81±0.02 years and an R-squared of 85.6±0.2%.
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