Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.

Journal: Journal of Alzheimer's disease : JAD
Published Date: Jan 1, 2021

Abstract

BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature.

Authors

  • Da Ma
    School of Engineering Science, Simon Fraser University, Burnaby, Canada.
  • Evangeline Yee
    School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada.
  • Jane K Stocks
    Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Lisanne M Jenkins
    Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
  • Karteek Popuri
    Simon Fraser University, School of Engineering Science, Burnaby BC V5A 1S6, Canada.
  • Guillaume Chausse
    Jewish General Hospital, Montreal, Canada.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Stephan Probst
    Department of Nuclear Medicine, Jewish General Hospital, Québec, Montreal, Canada.
  • Mirza Faisal Beg
    School of Engineering Science, Simon Fraser University, Burnaby, Canada.

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