Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets.

Journal: NeuroImage. Clinical
PMID: 37079936

Abstract

INTRODUCTION: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions of people. Early diagnosis is important to facilitate prompt interventions to slow down disease progression. However, accurate PD diagnosis can be challenging, especially in the early disease stages. The aim of this work was to develop and evaluate a robust explainable deep learning model for PD classification trained from one of the largest collections of T1-weighted magnetic resonance imaging datasets.

Authors

  • Milton Camacho
    Biomedical Engineering Program, University of Calgary, Canada; Department of Radiology, University of Calgary, Canada. Electronic address: milton.camachocamach@ucalgary.ca.
  • Matthias Wilms
    Department of Radiology, University of Calgary, Calgary, AB, Canada.
  • Pauline Mouches
    Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.
  • Hannes Almgren
    Department of Clinical Neurosciences, University of Calgary, Canada; Hotchkiss Brain Institute, University of Calgary, Canada.
  • Raissa Souza
    Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Richard Camicioli
    Neuroscience and Mental Health Institute and Department of Medicine (Neurology), University of Alberta, Edmonton, Alberta, Canada.
  • Zahinoor Ismail
    Department of Psychiatry, University of Calgary, Calgary, AB, Canada.
  • Oury Monchi
    Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. oury.monchi@ucalgary.ca.
  • Nils D Forkert
    Department of Radiology, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca.

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