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Related Experiment Video

Updated: Mar 29, 2026

Building Finite Element Models to Investigate Zebrafish Jaw Biomechanics
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Integrated Workflow Finite Element Modeling of the Temporomandibular Joint: Toward a Methodical and Reproducible

Lilian Baugnon1, Romain Nicot2,3, Nicolas Bethune1

  • 1Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube Laboratoire de Mécanique, Multiphysique, Multiéchelle, F-59000 Lille, France.

Journal of Biomechanical Engineering
|March 27, 2026
PubMed
Summary

This study introduces a patient-specific temporomandibular joint model for analyzing mandibular stress. The AI-assisted workflow streamlines biomechanical modeling for personalized surgical planning and treatment strategies.

Keywords:
biomechanicsfinite element (FE)patient-specific modelsemi-automated workflowtemporomandibular joint (TMJ)

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The temporomandibular joint (TMJ) is complex, requiring patient-specific models for accurate biomechanical analysis.
  • Existing methods for TMJ modeling can be time-consuming and lack patient specificity.
  • Understanding mandibular stress distribution is crucial for diagnosing and treating TMJ disorders.

Purpose of the Study:

  • To develop and validate a semi-automated, patient-specific parametric model of the temporomandibular joint.
  • To evaluate mandibular stress distribution under various loading conditions using the developed model.
  • To demonstrate a streamlined workflow for creating accurate, personalized biomechanical models.

Main Methods:

  • A finite element model was constructed from computed tomography (CT) data, including skull, mandible, teeth, and articular discs.
  • AI-assisted segmentation was used for bone and muscle structures, integrating patient-specific anatomical features.
  • Material properties were assigned based on Hounsfield units (HU) to differentiate bone and dental tissues.
  • Sensitivity analysis of key parameters (mesh density, material properties, friction, muscle forces) was performed.

Main Results:

  • The Hounsfield-Unit-driven material assignment resulted in a Young's modulus distribution consistent with literature values.
  • AI-based muscle reconstruction showed that stress fields stabilize with refined directional vector inputs.
  • The patient-specific model accurately reproduced mandibular stress distribution under tested conditions.
  • The workflow proved to be integrated, fast, and streamlined for generating tailored biomechanical models.

Conclusions:

  • The developed patient-specific parametric model is a feasible and reproducible tool for TMJ biomechanical analysis.
  • The AI-assisted workflow enhances accuracy and efficiency in creating personalized models.
  • This approach offers a promising tool for surgical planning, pathology simulation, and personalized treatment evaluation in TMJ disorders.