Relationship between brain injury criteria and brain strain across different types of head impacts can be different
December 18, 2020 ยท Declared Dead ยท ๐ Journal of the Royal Society Interface
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel, Hossein Vahid Alizadeh, Samuel J. Raymond, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael Zeineh, Gerald Grant, David B. Camarillo
arXiv ID
2012.10006
Category
q-bio.TO
Cross-listed
cs.LG,
physics.data-an,
q-bio.QM,
stat.AP
Citations
42
Venue
Journal of the Royal Society Interface
Last Checked
2 months ago
Abstract
Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different types of head impacts (e.g., sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across different types of head impacts has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum, and cumulative strain damage (15%) on each of 18 BIC respectively. The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fit on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ q-bio.TO
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Early Cancer Detection in Blood Vessels Using Mobile Nanosensors
R.I.P.
๐ป
Ghosted
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study
R.I.P.
๐ป
Ghosted
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
R.I.P.
๐ป
Ghosted
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
R.I.P.
๐ป
Ghosted
SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted