Deep learning-based amyloid PET positivity classification model in the Alzheimer's disease continuum by using 2-[¹⁸F]FDG PET.

ABSTRACT:

Background

Considering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[¹⁸F]FDG).

Methods

We used 2-[¹⁸F]FDG PET datasets from the Alzheimer's Disease Neuroimaging Initiative and Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease for model development. Moreover, we used an independent dataset from another hospital. A 2.5-D deep learning architecture was constructed using 291 submodules and three axes images as the input. We conducted the voxel-wise analysis to assess the regions with substantial differences in glucose metab

SUBMITTER: Kim S 

PROVIDER: S-EPMC8192639 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature