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

ABSTRACT:

Introduction

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.

Methods

Patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who underwent positron emission tomography (PET) with the amyloid radiotracer 18F-AV45 (florbetapir) were included. We identified important baseline PET image features using a deep convolutional neural network based on ResNet. These were combined with eight clinical, demographic, and genetic markers using a grad

SUBMITTER: Reith FH 

PROVIDER: S-EPMC8515556 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature