Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types.

Journal: IEEE transactions on bio-medical engineering
PMID: 38224520

Abstract

OBJECTIVE: The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM.

Authors

  • Xianghao Zhan
    Department of Bioengineering, Stanford University, CA, 94305, USA.
  • Yuzhe Liu
    School of Biological Science and Medical Engineering, BeiHang University, Beijing, 10019, China.
  • Nicholas J Cecchi
    Department of Bioengineering, Stanford University, CA, 94305, USA.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.
  • Michael M Zeineh
    Department of Radiology, Stanford University, CA, 94305, USA.
  • Gerald A Grant
    Departments of1Neurosurgery and.
  • David B Camarillo
    Department of Bioengineering, Stanford University, CA, 94305, USA.

Keywords

External Resources

Popular Topics
Recent Journals