Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment.

ABSTRACT:

Background

In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer's disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning-based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype ¹⁸F-FDG PET, age, and sex.

Methods

Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4

SUBMITTER: Doering E 

PROVIDER: S-EPMC9605923 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature