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Updated: Apr 13, 2026

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Cardiovascular digital twins using a Windkessel physics informed neural network.

Deen Osman1, Kaan Sel2, Erica Spatz3

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.

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|April 11, 2026
PubMed
Summary
This summary is machine-generated.

Windkessel physics-informed neural networks (WPINNs) enable accurate, noninvasive blood pressure prediction and cardiovascular parameter estimation using bioimpedance data. This advances personalized cardiovascular digital twins (CDTs) with minimal data requirements.

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Area of Science:

  • Cardiovascular medicine
  • Biomedical engineering
  • Artificial intelligence in healthcare

Background:

  • Cardiovascular digital twins (CDTs) promise personalized cardiovascular medicine but require accurate parameter estimation.
  • Current methods for parameter estimation are often invasive, burdensome, or data-intensive.
  • Noninvasive methods are needed for precise personalization of CDTs.

Purpose of the Study:

  • To develop a novel framework, Windkessel physics-informed neural networks (WPINNs), for estimating cardiovascular parameters and predicting blood pressure (BP) waveforms.
  • To enable accurate personalization of CDTs using noninvasive bioimpedance (Bio-Z) data.
  • To reduce the data requirements and invasiveness associated with current CDT construction methods.

Main Methods:

  • Developed WPINNs by integrating Windkessel models with physics-informed neural networks (PINNs).
  • Embedded Windkessel model differential equations into the PINN training process.
  • Validated WPINNs using Bio-Z datasets from healthy and hypertensive individuals and a synthetic dataset.

Main Results:

  • WPINNs achieved a 12%-25% reduction in BP prediction error compared to traditional deep learning models.
  • Accurate estimation of arterial compliance and peripheral resistance with errors ranging from 0.77% to 6.07%.
  • Demonstrated interpretable and accurate BP predictions with minimal ground truth data.

Conclusions:

  • WPINNs provide a robust foundation for noninvasive and interpretable cardiovascular digital twin frameworks.
  • This approach facilitates personalized cardiovascular medicine through accurate, data-efficient parameter estimation.
  • WPINNs offer a significant advancement over existing methods for building personalized CDTs.