American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.

Concussion Convolutional neural network Helmet STAR Traumatic brain injury Worcester Head Injury Model

Journal

Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512

Informations de publication

Date de publication:
Nov 2022
Historique:
received: 09 02 2022
accepted: 30 06 2022
pubmed: 12 7 2022
medline: 16 11 2022
entrez: 11 7 2022
Statut: ppublish

Résumé

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Identifiants

pubmed: 35816264
doi: 10.1007/s10439-022-03005-z
pii: 10.1007/s10439-022-03005-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1498-1509

Informations de copyright

© 2022. The Author(s) under exclusive licence to Biomedical Engineering Society.

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Auteurs

Kianoosh Ghazi (K)

Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA.

Mark Begonia (M)

Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA.

Steven Rowson (S)

Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA.

Songbai Ji (S)

Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA, 01605, USA. sji@wpi.edu.
Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA. sji@wpi.edu.

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