Predicting Age and Sex from Reduced Lead Electrocardiograms using Deep Learning - Dias, Felipe M.; Ribeiro, Estela; Soares, Quenaz B.; Krieger, Jose E.; Gutierrez, Marco A. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2025: Anais do XXV Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2025.7705, 2025)
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.