Your browser doesn't support javascript.
loading
Concussion classification via deep learning using whole-brain white matter fiber strains.
Cai, Yunliang; Wu, Shaoju; Zhao, Wei; Li, Zhigang; Wu, Zheyang; Ji, Songbai.
Afiliación
  • Cai Y; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America.
  • Wu S; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America.
  • Zhao W; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America.
  • Li Z; Department of Biomedical Data Science, Geisel School of medicine, Dartmouth College, Hanover, NH, United States of America.
  • Wu Z; Department of Mathematical Sciences, Worcester Polytechnic Institute, Worcester, MA, United States of America.
  • Ji S; Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, United States of America.
PLoS One ; 13(5): e0197992, 2018.
Article en En | MEDLINE | ID: mdl-29795640
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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Conmoción Encefálica / Mapeo Encefálico / Árboles de Decisión / Sustancia Blanca / Aprendizaje Automático / Fútbol Americano Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Conmoción Encefálica / Mapeo Encefálico / Árboles de Decisión / Sustancia Blanca / Aprendizaje Automático / Fútbol Americano Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos