VAM: A Parallel Cross-Modal Hybrid Network for Accurate and Interpretable Vascular Age Estimation from PPG

Vascular health, indicated by vascular age, is a key clinical marker. Traditional methods like PWV are complex, while PPG-based methods are more convenient but face challenges in generalization and adaptability. In response, this study proposes a Vascular Age Estimation Model (VAM), a parallel cross-modal hybrid network based on multi-source datasets, which integrates CNN and Transformer architectures to optimize the extraction and fusion of both local and global features. Experimental results on the VitalDB dataset demonstrate that the model achieves an average absolute error (MAE) of 7.66±0.39 years, root mean square error (RMSE) of 9.98±0.45 years, and coefficient of determination (R²) of 0.55±0.04. Compared to the baseline model, VAM reduces prediction errors by at least 20%. Furthermore, on an external test set, the model performs particularly well in the younger population (MAE=5.85 years, R²=0.63), with minimal impact of gender and BMI on estimation results (ΔMAE<0.5 years). Through interpretability analysis, this study confirms the crucial contribution of the diastolic peak in vascular age estimation. This research provides an efficient and interpretable solution for vascular age assessment, with significant clinical application potential.