Editor’s Choice Machine learning-based feature selection can identify parameters with higher performance in outcome prediction.
Forghani R, ed. Mukherji SK, consulting ed. Neuroimaging Clinics of North America: Machine Learning and Other Artificial Intelligence Applications. Elsevier; 2020;30(4):393–530; $397.00 Machine Learning and Other Artificial Intelligence Applications is a thoughtfully arranged collection of review articles offering an overview
Editor’s Choice This retrospective study included preoperative MR imaging of 288 pediatric patients with pediatric posterior fossa tumors, including medulloblastoma (n=111), ependymoma (n=70), and pilocytic astrocytoma (n=107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps.
Editor’s Choice The authors used resting-state fMRI in a prospective study to compare whole-brain functional connectivity between patients with good and poor outcomes, implementing support vector machine learning. They automatically predicted coma outcome using resting-state fMRI and also compared the
