Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection

Abstract

In Alzheimer's disease, asymptomatic patients may have amyloid deposition, but predicting their progression rate remains a substantial challenge with implications for clinical trial enrollment. Here, we demonstrate an artificial intelligence approach to use baseline clinical information and images to predict changes in quantitative biomarkers of brain pathology on future images.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2021
Source ID
10.1002/trc2.12212

Entities

People

  • Elizabeth C. Mormino
  • Fabian Reith
  • Greg Zaharchuk

Organizations

  • National Institutes of Health
  • Stanford University
  • United States Department of Defense

Tags

Fields of Study

  • Medicine
  • Psychology

Readers

  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Neural Network Machine Learning.
  • Traumatic Brain Injury (TBI) and Cognitive Aging in the Guam and Border Populations Affected by Alzheimer's Disease and Tau-Associated Dementias.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks