ArticleJournal of neurotrauma2021
Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact.
Article in Journal of neurotrauma, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
27 citing papers in PubMed.
- Peak Power: A Severity Measure for Head Acceleration Events Associated with Suspected Concussions.Sports medicine (Auckland, N.Z.) · 2026Article
- Effects of anatomy and head motion on spatial patterns of deformation in the human brain.Annals of biomedical engineering · 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
- Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement.Communications 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
- Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach.Military medicine · 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
- Evolution of Severe Closed Head Injury: Assessing Ventricular Volume and Behavioral Measures at 30 and 90 Days Post-Injury.Journal of clinical medicine · 2024Article
- Review
- Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.Journal of sport and health science · 2023Article
- The biomechanical signature of loss of consciousness: computational modelling of elite athlete head injuries.Brain : a journal of neurology · 2023Review
- Predictive Helmet Optimization Framework Based on Reduced-Order Modeling of the Brain Dynamics.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
- 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
- Drop Test Kinematics Using Varied Impact Surfaces and Head/Neck Configurations for Rugby Headgear Testing.Annals of biomedical engineering · 2022Article
- Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (>36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases
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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.