Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.

Journal: Journal of Alzheimer's disease : JAD
Published Date: Jan 1, 2020

Abstract

BACKGROUND: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials.

Authors

  • Ali Ezzati
    Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Danielle J Harvey
    Department of Public Health Sciences, University of California-Davis, Davis, CA, USA.
  • Christian Habeck
    Department of Neurology, Cognitive Neuroscience Division, Columbia University, New York, NY, USA.
  • Ashkan Golzar
    Element AI, Montreal, Quebec, QC, Canada.
  • Irfan A Qureshi
    Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Andrea R Zammit
    Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Jinshil Hyun
    Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Monica Truelove-Hill
    Center for Biomedical Image Computing and Analytics, monica.hill@pennmedicine.upenn.edu christos.davatzikos@uphs.upenn.edu.
  • Charles B Hall
    Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Richard B Lipton
    Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.

Keywords

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