ArticlePloS one2018
Concussion classification via deep learning using whole-brain white matter fiber strains.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Mechanical Stretch Disrupts Calcium Dynamics and Redistributes Piezo1 in Human Astrocytes.Annals of biomedical engineering · 2026Article
- A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations.Communications engineering · 2025Article
- Article
- Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.International journal of computer assisted radiology and surgery · 2023Article
- Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.Emergency radiology · 2023Article
- Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome.Neuropsychology review · 2023Review
- Article
- Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.Annals of biomedical engineering · 2022Review
- Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea).Scientific reports · 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
- Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.Sensors (Basel, Switzerland) · 2021Article
- Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.Scientific reports · 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
- A network-based response feature matrix as a brain injury metric.Biomechanics and modeling in mechanobiology · 2020Article
- Displacement- and Strain-Based Discrimination of Head Injury Models across a Wide Range of Blunt Conditions.Annals of biomedical engineering · 2020Article
- Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.Frontiers in bioengineering and biotechnology · 2020Article
- Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.Frontiers in bioengineering and biotechnology · 2020Article
- Mesh Convergence Behavior and the Effect of Element Integration of a Human Head Injury Model.Annals of biomedical engineering · 2019Article
- Current trends in biomarker discovery and analysis tools for traumatic brain injury.Journal of biological engineering · 2019Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828-0.862 vs. 0.690-0.776, and .632+ error of 0.148-0.176 vs. 0.207-0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.
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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.