Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators.

ABSTRACT: Alzheimer's disease (AD), the most prevalent degenerative brain disease associated with dementia, requires early diagnosis to alleviate worsening of symptoms through appropriate management and treatment. Recent studies on AD stage classification are increasingly using multimodal data. However, few studies have applied graph neural networks to multimodal data comprising F-18 florbetaben (FBB) amyloid brain positron emission tomography (PET) images and clinical indicators. The objective of this study was to demonstrate the effectiveness of graph convolutional network (GCN) for AD stage classification using multimodal data, specifically FBB PET images and clinical indicators, collected from Dong-A University Hospital (DAUH) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The effective

SUBMITTER: Lee GB 

PROVIDER: S-EPMC11666044 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature