Use of deep learning in the MRI diagnosis of Chiari malformation type I.

Journal: Neuroradiology
Published Date: Feb 24, 2022

Abstract

PURPOSE: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making.

Authors

  • Kaishin W Tanaka
    Macquarie Medical School, Macquarie University, NSW, 2109, Sydney, Australia.
  • Carlo Russo
    Computational NeuroSurgery (CNS) Lab, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
  • Sidong Liu
    Save Sight Institute, Sydney Medical School, The University of Sydney, Sydney, Australia; Brain and Mind Centre, Sydney Medical School, The University of Sydney, Sydney, Australia.
  • Marcus A Stoodley
    Macquarie Medical School, Macquarie University, NSW, 2109, Sydney, Australia.
  • Antonio Di Ieva
    Neurosurgery Unit, Department of Clinical Medicine, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.

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