  Objective: Many recent studies have suggested that brain deformation
resulting from a head impact is linked to the corresponding clinical outcome,
such as mild traumatic brain injury (mTBI). Even though 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 process of 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 1803 total 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 for every element in the entire brain in
less than 0.001s (with an average root mean squared error of 0.025, and with a
standard deviation of 0.002 over twenty repeats with random data partition and
model initialization). The contributions of various features to the predictive
power of the model were investigated, and it was noted that the features based
on angular acceleration were found to be more predictive than the features
based on angular velocity. Conclusion: Trained using the dataset of 1803 head
impacts, this model can be applied to various sports in the calculation of
brain strain with accuracy, and its applicability can even further be extended
by incorporating data from other types of head impacts. Significance: In
addition to the potential clinical application in real-time brain deformation
monitoring, this model will help researchers estimate the brain strain from a
large number of head impacts more efficiently than using FE models.
