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The advancement of personalized cardiac modeling, particularly through digital cardiac twins, enables tailored treatments based on the physiology of the individual patient. Traditional physics-based methods for optimizing the parameters of these cardiac models face challenges in clinical adoption due to their computational cost. Recent shifts towards data-driven approaches offer improved efficiency, but struggle with generalization and integration of core electrophysiological principles. The emerging use of physics-informed neural networks (PINNs) has the potential to combine the advantages of these two approaches, although still requiring retraining from scratch for each subject. This paper introduces a novel framework for meta-learning PINNs to overcome these challenges, enabling rapid personalization of a PINN to new subjects’ data via simple feedforward computation. We instantiate this meta-PINN framework using the Eikonal model as the governing physics, demonstrating its efficacy in significantly reducing computational demands while improving the predictive accuracy of personalized cardiac models. Source code available at https://github.com/temporary-repos/MICCAI2025

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Meta-learning Physics-Informed Neural Networks for Personalized Cardiac Modeling

  • 会议论文

  • 发表日期：

    2026年

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  • Maryam Toloubidokhti,
  • Ryan Missel,
  • Shichang Lian,
  • Linwei Wang

摘要

The advancement of personalized cardiac modeling, particularly through digital cardiac twins, enables tailored treatments based on the physiology of the individual patient. Traditional physics-based methods for optimizing the parameters of these cardiac models face challenges in clinical adoption due to their computational cost. Recent shifts towards data-driven approaches offer improved efficiency, but struggle with generalization and integration of core electrophysiological principles. The emerging use of physics-informed neural networks (PINNs) has the potential to combine the advantages of these two approaches, although still requiring retraining from scratch for each subject. This paper introduces a novel framework for meta-learning PINNs to overcome these challenges, enabling rapid personalization of a PINN to new subjects’ data via simple feedforward computation. We instantiate this meta-PINN framework using the Eikonal model as the governing physics, demonstrating its efficacy in significantly reducing computational demands while improving the predictive accuracy of personalized cardiac models. Source code available at https://github.com/temporary-repos/MICCAI2025