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Updated: May 14, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerating Cardiac Mechanics Modeling.

Lei Shi1, Yurui Chen2, Vijay Vedula2

  • 1Department of Mechanical Engineering, Kennesaw State University, Marietta, GA, 30060, USA.

Journal of Computational Physics
|May 13, 2026
PubMed
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We developed HeartSimSage, an attention-enhanced graph neural network (GNN) model, to rapidly predict cardiac displacements. This computational tool significantly accelerates finite element analysis (FEA) for cardiac biomechanics simulations.

Area of Science:

  • Computational mechanics
  • Biomedical engineering
  • Artificial intelligence in medicine

Background:

  • Finite element analysis (FEA) is crucial for cardiac biomechanics but computationally intensive for creating patient-specific digital twins.
  • Current FEA emulators struggle with diverse geometries, material models, and boundary conditions, limiting clinical applications.

Purpose of the Study:

  • To develop a rapid and accurate FEA emulator for predicting passive biventricular myocardial displacements.
  • To overcome the limitations of existing emulators in handling complex cardiac geometries and material properties.

Main Methods:

  • Developed an attention-enhanced graph neural network (GNN) named HeartSimSage, inspired by Graph Sample and Aggregate (GraphSAGE).
  • Integrated Laplace-Dirichlet solutions for spatial encoding and subset-based training for efficiency.
Keywords:
attention mechanismbiventricular cardiac mechanicscardiac mechanics emulatorfeature engineeringfinite element analysisgraph neural networks

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Last Updated: May 14, 2026

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  • Employed an attention mechanism to adaptively weigh neighbor contributions and filter information flow.

    • Main Results:

    • HeartSimSage accurately predicted biventricular myocardial displacements with a median error of 0.280 mm (IQR [0.167, 0.484] mm) compared to traditional FEA.
    • Achieved significant computational speedups: ~13000X on GPU and ~190X on CPU.
    • Demonstrated robustness across diverse 3D biventricular geometries, mesh types, and material models.

    Conclusions:

    • HeartSimSage offers a computationally efficient and accurate alternative to traditional FEA for cardiac biomechanics.
    • The model's ability to handle complex inputs and its speedup potential pave the way for clinical applications of cardiac digital twins.