ArticleFrontiers in bioengineering and biotechnology2021
A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI.
Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Use of artificial intelligence in diagnosis and prognosis of traumatic brain injury: a scoping review.International journal of emergency medicine · 2025Review
- Traumatic Brain Injury: Advances in Diagnostic Techniques and Treatment Modalities.Journal of clinical medicine · 2025Review
- A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations.Communications engineering · 2025Article
- Advancements in the application of multimodal monitoring and machine learning for the development of personalized therapeutic strategies in traumatic brain injury.Frontiers in human neuroscience · 2025Review
- Proteomics reveals molecular subtypes in traumatic brain injury patients: new perspectives for precision treatment strategies.Frontiers in immunology · 2025Article
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- A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.Frontiers in bioengineering and biotechnology · 2021Article
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Authors and funding
7 authors.
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Abstract
Resting state functional magnetic resonance imaging (rsfMRI), and the underlying brain networks identified with it, have recently appeared as a promising avenue for the evaluation of functional deficits without the need for active patient participation. We hypothesize here that such alteration can be inferred from tissue damage within the network. From an engineering perspective, the numerical prediction of tissue mechanical damage following an impact remains computationally expensive. To this end, we propose a numerical framework aimed at predicting resting state network disruption for an arbitrary head impact, as described by the head velocity, location and angle of impact, and impactor shape. The proposed method uses a library of precalculated cases leveraged by a machine learning layer for efficient and quick prediction. The accuracy of the machine learning layer is illustrated with a dummy fall case, where the machine learning prediction is shown to closely match the full simulation results. The resulting framework is finally tested against the rsfMRI data of nine TBI patients scanned within 24 h of injury, for which paramedical information was used to reconstruct
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