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

ABSTRACT: Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts continue to rely on empirical, scalar metrics based on kinematics or model-estimated tissue responses explicitly pre-defined in a specific brain region of interest. They could suffer from loss of information. A single training dataset has also been used to evaluate performance but without cross-validation. In this study, we developed a deep learning approach for concussion classification using implicit features of the entire voxel-wise white matter fiber strains. Using reconstructed American National Football League (NFL) injury cases, leave-one-out cross-validation was employed to objectively compare injury prediction performances against two bas

SUBMITTER: Cai Y 

PROVIDER: S-EPMC5967816 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature