A test of machine-learning algorithms shows promise for computer-aided prognosis of acute spinal cord injury, according to a study to be presented at the ARRS 2018 Annual Meeting, set for April 22-27 in Washington, DC.
The study to be presented by Jason Talbot, assistant professor of radiology at the University of California, San Francisco, involved using semiautomated image analysis with machine-learning algorithms to assess the accuracy of axial T2-weighted radiomic features for classifying patients by degree of neurologic injury.
Several machine-learning algorithms were tested for injury classification based on texture variables. For each trained model, the accuracy of predicting the testing set was recorded, as were variables important to the model.
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