ArticleScientific reports2019
Convolutional neural network for efficient estimation of regional brain strains.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Peaks and Distributions of White Matter Tract-related Strains in Bicycle Helmeted Impacts: Implication for Helmet Ranking and Optimization.Annals of biomedical engineering · 2025Article
- 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
- The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model.Annals of biomedical engineering · 2024Article
- Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.IEEE sensors journal · 2024Article
- Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts.Brain multiphysics · 2023Article
- American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.Annals of biomedical engineering · 2022Article
- Drop Test Kinematics Using Varied Impact Surfaces and Head/Neck Configurations for Rugby Headgear Testing.Annals of biomedical engineering · 2022Article
- Predictive Helmet Optimization Framework Based on Reduced-Order Modeling of the Brain Dynamics.Annals of biomedical engineering · 2022Article
- Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.Annals of biomedical engineering · 2022Review
- Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
- Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact.Computer methods in applied mechanics and engineering · 2022Article
- [Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion.Frontiers in sports and active living · 2022Article
- The relationship between brain injury criteria and brain strain across different types of head impacts can be different.Journal of the Royal Society, Interface · 2021Article
- Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations.Journal of neurotrauma · 2021Article
- Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact.Journal of neurotrauma · 2021Article
- A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.Frontiers in bioengineering and biotechnology · 2021Article
- Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts.Annals of biomedical engineering · 2020Article
- Displacement- and Strain-Based Discrimination of Head Injury Models across a Wide Range of Blunt Conditions.Annals of biomedical engineering · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Head injury models are important tools to study concussion biomechanics but are impractical for real-world use because they are too slow. Here, we develop a convolutional neural network (CNN) to estimate regional brain strains instantly and accurately by conceptualizing head rotational velocity profiles as two-dimensional images for input. We use two impact datasets with augmentation to investigate the CNN prediction performances with a variety of training-testing configurations. Three strain measures are considered, including maximum principal strain (MPS) of the whole brain, MPS of the corpus callosum, and fiber strain of the corpus callosum. The CNN is further tested using an independent impact dataset (N = 314) measured in American football. Based on 2592 training samples, it achieves a testing R
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