Why the study?
Can a deep learning model accurately predict age and sex from reduced-lead ECGs and assess mortality risk?
Observational (n=233,770)
Yes
Can a deep learning model accurately predict age and sex from reduced-lead ECGs and assess mortality risk?
Answers are grounded in this paper and cite their sources.
Deep learning models can predict age and sex from reduced-lead ECGs, and discrepancies between predicted and actual values are associated with mortality risk.
Artificial intelligence is increasingly used to extract health insights from 12-lead (12L) electrocardiograms (ECG). Here, we propose a deeplearning model to predict sex and age from 12L and reduced-lead ECGs (6L–1L) and assess their impact on mortality risk. Using a ResNeXt-based model trained on the CODE15 dataset, our best models achieved an F1-score of 0.800 for sex classification (12L) and a mean absolute error of 8.961 for age estimation (4L). We found that overestimated age predictions and incorrect sex classifications were associated with higher mortality risk, whereas underestimated age predictions correlated with lower risk. These findings highlight the potential of reduced-lead ECGs for risk assessment, expanding their clinical utility.
No takes yet. Share an insight, caveat, or question.
Dias et al. (2025) conducted an observational in General population undergoing ECG (n=233,770). AI-estimated ECG age greater than chronological age vs. Accurate age prediction was evaluated on Mortality risk when predicted age exceeds actual age by more than the mean absolute error (12-lead model) (HR 2.49, 95% CI 2.17-2.87, p=<0.005).
Synapse has enriched 8 closely related papers on similar clinical questions. Consider them for comparative context: