Identification of injured elements in computational models of spinal cord injury using machine learning

摘要: The purpose of this study was to use machine learning (ML) algorithms to identify tissue damage based on the mechanical outputs of computational models of spinal cord injury (SCI). Three datasets corresponding to gray matter, white matter, and the combination of gray and white matter tissues were used to train the models. These datasets were built from the comparison of histological images taken from SCI experiments in non-human primates and corresponding subject-specific finite element (FE) models. Four ML algorithms were evaluated and compared using cross-validation and the area under the receiver operating characteristic curve (AUC). After hyperparameter tuning, the AUC mean values for the algorithms ranged between 0.79 and 0.82, with a standard deviation no greater than 0.02. The findings of this study also showed that k-nearest neighbors and logistic regression algorithms were better at …