Cardiovascular digital twins (CDTs) have the potential to transform precision medicine by enabling tailored insights, continuous monitoring, and personalized simulations of cardiovascular dynamics through virtual representations of the cardiovascular system. Accurately building these representations requires precise estimation of personalized parameters such as arterial compliance and peripheral resistance. However, current methods are often burdensome, rely on invasive procedures, or require large datasets. To address these limitations, we present Windkessel physics-informed neural networks (WPINNs), a framework combining Windkessel models with physics-informed neural networks (PINNs) to estimate personalized cardiovascular parameters and predict blood pressure (BP) waveforms from noninvasive bioimpedance (Bio-Z) wearables. WPINNs embed the governing differential equations of Windkessel models into the training process, enabling interpretable and accurate BP predictions with minimal ground truth data. We validate WPINNs using Bio-Z datasets from healthy and hypertensive individuals, achieving a 12%–25% reduction in error compared to traditional data-driven deep learning models. Additionally, WPINNs estimate arterial compliance and peripheral resistance with high accuracy, resulting in errors from 0.77% to 6.07% utilizing a synthetic cardiovascular waveform dataset. This work establishes WPINNs as a strong foundation for noninvasive and interpretable CDT frameworks.
Osman et al. (2026) studied this question.