Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly.

ABSTRACT:

Background

The robustness of conventional amyloid PET harmonization across tracers has been questioned.

Purpose

To evaluate deep learning-based harmonization of amyloid PET in predicting conversion from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and MCI to Alzheimer's disease (AD).

Materials and methods

We developed an amyloid PET-based deep-learning model to classify participants with a clinical diagnosis of AD-dementia vs CU across different tracers from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Japanese ADNI, and Australian Imaging, Biomarker, and Lifestyle cohorts (n = 1050). The model output [deep learning-based probability of Alzheimer's disease-dementia (DL-ADprob)], with other prognostic factors, was evaluated for predi

SUBMITTER: Choi YS 

PROVIDER: S-EPMC12429267 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature