Explainable age prediction using a transformer trained on diverse global electrocardiograms.

AIMS: Artificial intelligence (AI) models trained on electrocardiograms (ECGs) can estimate age, with deviations from chronological age (Δage) serving as markers of accelerated cardiovascular ageing. We evaluated whether transformer-based AI models trained on globally diverse, heterogeneous ECG datasets outperform convolutional neural network (CNN)-based models in generalizability and physiological interpretability.

METHODS AND

RESULTS

Using 11 public datasets (435 203 recordings) from four continents and an independent external cohort (14 462 recordings), we trained and validated transformer and CNN architectures. The transformer model trained on global data achieved strong correlation with chronological age and maintained robust performance in the external cohort (r = 0.90), outperforming both a CNN trained on global data and a model trained exclusively on a homogenous population-based cohort (r = 0.88). Individuals with Δage ≥+8 years exhibited a two-fold increased mortality risk (hazard ratio = 2.08, 95% confidence interval 1.93-2.24). Explainability analysis identified the P-wave, particularly in lead V1, as the dominant contributor to age prediction, consistent with known electrophysiological changes associated with atrial remodelling and ageing. The model's attribution to early atrial conduction features was consistent across age strata, peaking in midlife (30-50 years), and correlated positively with disease burden, including hypertension and smoking.

CONCLUSION

Our findings establish that transformer models trained on globally heterogeneous data generalize well and can provide insights to plausible ageing signatures. Furthermore, Δage may offer insight into underlying physiological ageing processes and associated risk, though further validation is needed to define its clinical utility in risk stratification.