Applications of machine learning to MR imaging of pediatric low-grade gliomas.

Journal: Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Published Date: Jul 8, 2024

Abstract

INTRODUCTION: Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify crucial information from medical images that is otherwise currently unattainable. For example, ML appears to be capable of preoperatively identifying the underlying genetic status of pLGG.

Authors

  • Kareem Kudus
    Neurosciences & Mental Health Research Program, The Hospital for Sick Children, Toronto, Canada.
  • Matthias Wagner
    From the Department of Medical Imaging, University of Toronto, 555 University Ave, Toronto, ON, Canada M5S 1A1.
  • Birgit Betina Ertl-Wagner
    Hospital for Sick Children (F.K., B.B.E.-W.), Toronto, Ontario, Canada.
  • Farzad Khalvati
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada. farzad.khalvati@utoronto.ca.

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