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.

Head injuries remain one of the major health concerns in contact sports such as boxing. Despite the widespread use of protective gloves and helmets, their biomechanical effectiveness in mitigating head acceleration and reducing brain injury risk remains uncertain. This study aims to biomechanically assess available boxing equipment solutions and identify the brain–skull system’s response to physical forces from a boxing punch. A dedicated experimental setup was developed using mini triaxial accelerometers and a high-speed camera to measure head accelerations in a Primus unbreakable dummy. Tests were performed using gloves of different masses (0 oz, 10 oz, and 16 oz) and three head protection configurations: no helmet, rugby helmet, and boxing helmet. The resultant accelerations were analyzed and compared across test conditions. Peak wrist accelerations ranged from 195.00 to 271.77 m/s², while head accelerations did not exceed biomechanical injury thresholds. The boxing helmet, composed of multilayer polyurethane foam, did not consistently decrease acceleration; in some cases, it produced higher overloads due to increased head mass and moment of inertia. A rugby helmet made of open-cell EVA (ethylene vinyl acetate) foam with lower density exhibited more favorable energy-dissipation characteristics under low-impact conditions. Glove mass also influenced acceleration differently between male and female participants, likely due to variations in punch velocity and force generation. This work is a pilot study using two trained adult volunteers to validate the combined IMU–video measurement framework. The results serve as hypothesis-generating mechanistic observations rather than population-level effect estimates. Protective effectiveness in boxing depends on a complex interaction between material properties, geometry, and user biomechanics. Optimal equipment design should balance energy absorption and mass to minimize both linear and rotational accelerations. Future studies should integrate advanced material modeling and finite element simulations to support the development of adaptive, lightweight protective systems.