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Poor outcome following traumatic acute subdural hematoma (ASDH) is associated with the severity of the primary injury and secondary injury including cerebral edema and ischemia. However, the underlying secondary injury mechanism contributing to elevated intracranial pressure (ICP) and high mortality rate remains unclear. Cerebral edema occurs in response to the exposure of the intracellular fixed charge density (FCD) after cell death, causing ICP to increase. The increased ICP from swollen tissue compresses blood vessels in adjacent tissue, restricting blood flow and leading to ischemic damage. We hypothesize that the mass occupying effect of ASDH exacerbates the ischemic injury, leading to ICP elevation, which is an indicator of high mortality rate in the clinic. Using FEBio (febio.org) and triphasic swelling biomechanics, this study modeled clinically relevant ASDHs and simulated post-traumatic brain swelling and ischemia to predict ICP. Results showed that common convexity ASDH significantly increased ICP by exacerbating ischemic injury, and surgical removal of the convexity ASDH may control ICP by preventing ischemia progression. However, in cases where the primary injury is very severe, surgical intervention alone may not effectively decrease ICP, as the contribution of the hematoma to the elevated ICP is insignificant. In addition, interhemispheric ASDH, located between the cerebral hemispheres, does not significantly exacerbate ischemia, supporting the conservative surgical management generally recommended for interhemispheric ASDH. The joint effect of the mass occupying effect of the blood clot and resulting ischemia contributes to elevated ICP which may increase mortality. Our novel approach may improve the fidelity of predicting patient outcome after motor vehicle crashes and traumatic brain injuries due to other causes., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

The large amount of training samples required to develop a deep learning brain injury model demands enormous computational resources. Here, we study how a transformer neural network (TNN) of high accuracy can be used to efficiently generate pretraining samples for a convolutional neural network (CNN) brain injury model to reduce computational cost. The samples use synthetic impacts emulating real-world events or augmented impacts generated from limited measured impacts. First, we verify that the TNN remains highly accurate for the two impact types (N = 100 each; R 2 of 0.948-0.967 with root mean squared error, RMSE, ~ 0.01, for voxelized peak strains). The TNN-estimated samples (1000-5000 for each data type) are then used to pretrain a CNN, which is further finetuned using directly simulated training samples (250-5000). An independent measured impact dataset considered of complete capture of impact event is used to assess estimation accuracy (N = 191). We find that pretraining can significantly improve CNN accuracy via transfer learning compared to a baseline CNN without pretraining. It is most effective when the finetuning dataset is relatively small (e.g., 2000-4000 pretraining synthetic or augmented samples improves success rate from 0.72 to 0.81 with 500 finetuning samples). When finetuning samples reach 3000 or more, no obvious improvement occurs from pretraining. These results support using the TNN to rapidly generate pretraining samples to facilitate a more efficient training strategy for future deep learning brain models, by limiting the number of costly direct simulations from an alternative baseline model. This study could contribute to a wider adoption of deep learning brain injury models for large-scale predictive modeling and ultimately, enhancing safety protocols and protective equipment., (© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.)

This study aims to facilitate intracranial simulation of traumatic events by determining the mechanical properties of different anatomical structures of the brain. Our experimental indentation paradigm used fresh, post-operative human tissue, which is highly advantageous in determining mechanical properties without being affected by postmortem time. This study employed an inverse finite element approach coupled with experimental indentation data to characterize mechanical properties of the human hippocampus (CA1, CA3, dentate gyrus), cortex white matter, and cortex grey matter. We determined that an uncoupled viscoelastic Ogden constitutive formulation was most appropriate to represent the mechanical behavior of these different regions of brain. Anatomical regions were significantly different in their mechanical properties. The cortex white matter was stiffer than cortex grey matter, and the CA1 and dentate gyrus were both stiffer than cortex grey matter. Although no sex dependency was observed, there were trends indicating that male brain regions were generally stiffer than corresponding female regions. In addition, there were no statistically significant age dependent differences. This study provides a structure-specific description of fresh human brain tissue mechanical properties, which will be an important step toward explicitly modeling the heterogeneity of brain tissue deformation during TBI through finite element modeling., (© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.)

This study characterizes the mechanical properties of human brain tissue resected during the course of surgery under multistep indentation loading up to 30% strain. The experimental characterization using fresh, post-operative, human brain tissue is highly advantageous since postmortem times can affect its biomechanical behavior. Although the quasilinear theory of viscoelasticity (QLV) approach has been widely used to model brain tissue mechanical properties, our analysis concluded that the linear viscoelastic approach provided a better fit to the experimental data overall. The only statistically significant regional difference in observed stiffness was between the cortex gray and dentate gyrus. There were no statistically significant age or sex dependent differences, although the data suggested that the cortex white matter in males was stiffer than that in females. Our results can help improve the accuracy of finite element models of brain tissue deformation to predict its response to traumatic brain injury., (© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Helmet manufacturers recommend replacing a bicycle helmet after an impact or after anywhere from 2 to 10 years of use. The goal of this study was to quantify the effect of helmet age on peak headform acceleration during impact attenuation testing of field-used bicycle helmets. Helmets were acquired by donation from consumers and retail stores, and were included in the study if they were free of impact-related damage, had a legible manufacture date label, and were certified to at least one helmet standard. Helmets ( n = 770) spanning 0-26 years old were drop tested to measure peak linear headform acceleration during impacts to the right and left front regions of the helmets at two impact speeds (3.0 and 6.2 m/s). General linear mixed models were used to assess the effect of age and three covariates (helmet style, size and certification impact speed) on peak acceleration. Overall, age was related to either no difference or a statistically significant but small increase (≤0.76 g/year of helmet age) in peak headform acceleration. Extrapolated across 20 years, age-related differences were less than both style- (traditional vs. BMX) and size-related differences. The age-related differences were also less than the variability observed between different helmets after accounting for style, size and certification effects. These findings mean that bicycle helmets (up to 26-year-old traditional helmets and 13-year-old BMX helmets) do not lose their ability to attenuate impacts with age; however, other helmet features that may change with age were not evaluated in this study. [ABSTRACT FROM AUTHOR]

In the human-vehicle-road system of collisions, the human is the most important factor, and digital human models (DHMs) are developed with the aim of preventing or at least reducing human injury. Because most of the relevant literature is focused mainly on collisions in traffic accidents (TAs), only some of the literature reviewed in this paper involves research results on other aspects of collisions. In this review, based on the background of DHMs and the application of DHMs regarding human injury biomechanics in collisions field, research results regarding the development of DHMs are described, the methods for verifying such models are introduced, and the application of the research results is discussed based on the aspect of human injury biomechanics. From the research literature, the development and validation of DHMs and their application in human injury biomechanics are summarized, and future research trends are proposed and discussed., (© 2021. Biomedical Engineering Society.)

In ice hockey, concussions can occur as a result of many different types of impact events, however hockey helmets are certified using a single injury scenario, involving drop tests to a rigid surface. The purpose of this study is to measure the protective capacity of ice hockey helmets for different impact events in ice hockey. A helmeted and unhelmeted Hybrid III headform were impacted simulating falls, elbow, shoulder and puck impacts in ice hockey. Linear and rotational acceleration and maximum principal strain (MPS) were measured. A comparison of helmeted and unhelmeted impacts found significant differences existed in most conditions ( p < 0.05), however some shoulder and puck impacts showed no significant difference ( p > 0.05). Impacts to the ice hockey helmet tested resulted in acceleration levels below reported ranges of concussion and TBI for falls up to 5 m/s, elbow collisions, and low velocity puck impacts but not for shoulder collisions or high velocity puck impacts and falls. The helmet tested reduced MPS below reported ranges of concussion and TBI for falls up to 5 m/s but not for the other impact events across all velocities and locations. This suggests that the ice hockey helmet tested is unable to reduce engineering parameters below reported ranges of concussion and TBI for impact conditions which do not represent a drop against a rigid surface. [ABSTRACT FROM AUTHOR]