Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion

Abstract

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.

arXiv comment12 pages, 6 figures, IEEE journal

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Scikit-learn: Machine Learning in Python
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts
• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
• Theoretical and numerical analysis for angular acceleration being determinant of brain strain in mTBI

Cited by in corpus (11)

• Relationship between brain injury criteria and brain strain across different types of head impacts can be different
• Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
• Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
• Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
• Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
• Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
• Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
• Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
• A finite rotation, small strain 2D elastic head model, with applications in mild traumatic brain injury
• Identification of head impact locations, speeds, and force based on head kinematics
• A comparison of sports-related head accelerations with and without direct head impacts