ArticleComputer methods in applied mechanics and engineering2022
Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact.
Article in Computer methods in applied mechanics and engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
- Surface-based versus voxel-based finite element head models: comparative analyses of strain responses.Biomechanics and modeling in mechanobiology · 2025Article
- Learning soft tissue deformation from incremental simulations.Medical physics · 2025Article
- Peaks and Distributions of White Matter Tract-related Strains in Bicycle Helmeted Impacts: Implication for Helmet Ranking and Optimization.Annals of biomedical engineering · 2025Article
- Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025Review
- Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning.Annals of biomedical engineering · 2024Article
- Potential of Soft-Shelled Rugby Headgear to Lower Regional Brain Strain Metrics During Standard Drop Tests.Sports medicine - open · 2024Article
- Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.IEEE sensors journal · 2024Article
- Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.Annals of biomedical engineering · 2022Review
- American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.Annals of biomedical engineering · 2022Article
- Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts.Annals of biomedical engineering · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain-skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.