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

• Published in: PloS one Vol. 13; no. 5; p. e0197992
• Main Authors: Cai, Yunliang, Wu, Shaoju, Zhao, Wei, Li, Zhigang, Wu, Zheyang, Ji, Songbai
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 24.05.2018; Public Library of Science (PLoS)
• Subjects: Artificial intelligence; Biology and Life Sciences; Biomechanics; Biomedical engineering; Brain; Brain Concussion - classification; Brain Concussion - pathology; Brain damage; Brain mapping; Brain Mapping - methods; Brain research; Care and treatment; Classification; Classifiers; Computer and Information Sciences; Concussion; Corpus callosum; Data mining; Datasets; Decision Trees; Deep learning; Development and progression; Engineering; Error detection; Football; Head injuries; Humans; International conferences; Kinematics; Learning algorithms; Machine Learning; Medical imaging; Medicine and Health Sciences; Neural networks; Neuroimaging; Physical Sciences; Research and Analysis Methods; ROC Curve; Student athletes; Substantia alba; Support vector machines; Traumatic brain injury; Variables; White Matter - pathology; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0197992

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 baseline machine learning classifiers (support vector machine (SVM) and random forest (RF)) and four scalar metrics via univariate logistic regression (Brain Injury Criterion (BrIC), cumulative strain damage measure of the whole brain (CSDM-WB) and the corpus callosum (CSDM-CC), and peak fiber strain in the CC). Feature-based machine learning classifiers including deep learning, SVM, and RF consistently outperformed all scalar injury metrics across all performance categories (e.g., leave-one-out accuracy of 0.828-0.862 vs. 0.690-0.776, and .632+ error of 0.148-0.176 vs. 0.207-0.292). Further, deep learning achieved the best cross-validation accuracy, sensitivity, AUC, and .632+ error. These findings demonstrate the superior performances of deep learning in concussion prediction and suggest its promise for future applications in biomechanical investigations of traumatic brain injury.