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Developing an accurate and reliable injury predictor is central to thebiomechanical studies of traumatic brain injury. State-of-the-art effortscontinue to rely on empirical, scalar metrics based on kinematics ormodel-estimated tissue responses explicitly pre-defined in a specific brainregion of interest. They could suffer from loss of information. A singletraining dataset has also been used to evaluate performance but withoutcross-validation. In this study, we developed a deep learning approach forconcussion classification using implicit features of the entire voxel-wisewhite matter fiber strains. Using reconstructed American National FootballLeague (NFL) injury cases, leave-one-out cross-validation was employed toobjectively compare injury prediction performances against two baseline machinelearning classifiers (support vector machine (SVM) and random forest (RF)) andfour scalar metrics via univariate logistic regression (Brain Injury Criterion(BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and thecorpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based deeplearning and machine learning classifiers consistently outperformed all scalarinjury metrics across all performance categories in cross-validation (e.g.,average accuracy of 0.844 vs. 0.746, and average area under the receiveroperating curve (AUC) of 0.873 vs. 0.769, respectively, based on the testingdataset). Nevertheless, deep learning achieved the best cross-validationaccuracy, sensitivity, and AUC (e.g., accuracy of 0.862 vs. 0.828 and 0.842 forSVM and RF, respectively). These findings demonstrate the superior performancesof deep learning in concussion prediction, and suggest its promise for futureapplications in biomechanical investigations of traumatic brain injury.

Concussion classification via deep learning using whole-brain white matter fiber strains