¹⁸F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer's disease clinical spectrum.

ABSTRACT:

Background

With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an ¹⁸F-fluorodeoxyglucose-positron emission tomography (¹⁸F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC).

Materials and methods

A ¹⁸F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (n = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (n = 663), J-ADNI MCI (

SUBMITTER: Sohn B 

PROVIDER: S-EPMC12429239 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature