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<p>Despite significant advances in traumatic brain injury (TBI) research, current computational models remain largely confined to research environments with limited clinical translation. While previous reviews have focused on individual modelling components, a comprehensive synthesis examining the integrative progress across finite element and fluid-structure interaction approaches over the past decade has been lacking. This comprehensive review synthesizes key methodological advancements in computational head injury biomechanics from 2015 to 2025, focusing on the integration of cerebrospinal fluid (CSF) dynamics, anatomically precise modelling, and coupled fluid-structure simulations. Following a thematic synthesis approach, approximately 250 peer-reviewed studies from major databases were reviewed. Quantitative analysis reveals remarkable progress. Finite element counts have increased from ~100,000 to &gt; 2.5 million elements, and computational efficiency has advanced 3-fold through machine learning surrogates. Key advances include: (1) region-specific, strain-rate sensitive constitutive models capturing brain tissue’s visco-hyperelastic properties, (2) enhanced fluid-structure interaction frameworks, enabling physiologically relevant CSF flow modelling, and (3) integration of real-time simulation capabilities. The most promising advancement identified is the multiple-network poroelastic theory (MPET) for integrated CSF-tissue modelling, while machine learning integration represents the most significant computational breakthrough. Critical challenges persist, particularly parameter uncertainty and limited validation data availability. DTI-informed anisotropic white matter representation has been integrated into models, though systematic analysis reveals substantial implementation variability (up to 25% strain prediction differences) and validation challenges that complicate assessment of its impact on predictive accuracy. Future convergence toward hybrid physics-informed models may facilitate the transition of finite element head models from research prototypes to clinical tools for decision support, injury prevention, and regulatory assessment. This review provides the first comprehensive decade-spanning synthesis of computational TBI modelling.</p>

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Advances in Computational Modelling of Head Injury Biomechanics – a Comprehensive Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月5日

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  • Claudia Sbriglio,
  • Mariusz Ptak,
  • Artur Kwiatkowski

摘要

Despite significant advances in traumatic brain injury (TBI) research, current computational models remain largely confined to research environments with limited clinical translation. While previous reviews have focused on individual modelling components, a comprehensive synthesis examining the integrative progress across finite element and fluid-structure interaction approaches over the past decade has been lacking. This comprehensive review synthesizes key methodological advancements in computational head injury biomechanics from 2015 to 2025, focusing on the integration of cerebrospinal fluid (CSF) dynamics, anatomically precise modelling, and coupled fluid-structure simulations. Following a thematic synthesis approach, approximately 250 peer-reviewed studies from major databases were reviewed. Quantitative analysis reveals remarkable progress. Finite element counts have increased from ~100,000 to > 2.5 million elements, and computational efficiency has advanced 3-fold through machine learning surrogates. Key advances include: (1) region-specific, strain-rate sensitive constitutive models capturing brain tissue’s visco-hyperelastic properties, (2) enhanced fluid-structure interaction frameworks, enabling physiologically relevant CSF flow modelling, and (3) integration of real-time simulation capabilities. The most promising advancement identified is the multiple-network poroelastic theory (MPET) for integrated CSF-tissue modelling, while machine learning integration represents the most significant computational breakthrough. Critical challenges persist, particularly parameter uncertainty and limited validation data availability. DTI-informed anisotropic white matter representation has been integrated into models, though systematic analysis reveals substantial implementation variability (up to 25% strain prediction differences) and validation challenges that complicate assessment of its impact on predictive accuracy. Future convergence toward hybrid physics-informed models may facilitate the transition of finite element head models from research prototypes to clinical tools for decision support, injury prevention, and regulatory assessment. This review provides the first comprehensive decade-spanning synthesis of computational TBI modelling.