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Rapid Estimation of Entire Brain Strain Using Deep Learning Models by Xianghao Zhan; Yuzhe Liu; Samuel Raymond; Hossein Vahid Alizadeh; August Domel; Olivier Gevaert; Michael Zeineh; Gerald Grant; David Camarillo is a Engineering article available to read on EtoBox.

What is Rapid Estimation of Entire Brain Strain Using Deep Learning Models about?

## Objective: Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mtbi). even if several finite element (fe) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these fe head models is limited due to the time-consuming nature of fe simulations. this work aims to accelerate the brain deformation calculation and thus improve the potential for clinical applications. ## Methods: We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. ## Results: The proposed deep learning head model can calculate the maximum principal strain (green lagrange) for every element in the entire brain in less than 0.001 s with an average root mean squared error of 0.022 and a standard deviation of 0.001 over twenty repeats with random data partition and model initialization. ## Conclusion: Trained and tested using

Who reads Rapid Estimation of Entire Brain Strain Using Deep Learning Models?

Author

Xianghao Zhan; Yuzhe Liu; Samuel Raymond; Hossein Vahid Alizadeh; August Domel; Olivier Gevaert; Michael Zeineh; Gerald Grant; David Camarillo

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)