Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach.

Journal: Military medicine
PMID: 38739497

Abstract

INTRODUCTION: Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models.

Authors

  • Kshitiz Upadhyay
    Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
  • Roshan Jagani
    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
  • Dimitris G Giovanis
    Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
  • Ahmed Alshareef
    Department of Mechanical Engineering, University of South Carolina, Columbia, SC 29208, USA.
  • Andrew K Knutsen
    Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD 20817, USA.
  • Curtis L Johnson
  • Aaron Carass
    Department of Computer Science, The Johns Hopkins University, United States; Department of Electrical and Computer Engineering, The Johns Hopkins University, United States.
  • Philip V Bayly
    Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA.
  • Michael D Shields
    Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
  • K T Ramesh
    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

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