Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.

ABSTRACT:

Background

Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical management and selecting proper therapeutic interventions. Structural magnetic resonance imaging (MRI) markers have been widely investigated for predicting the conversion of MCI to AD, and recent advances in deep learning (DL) methods offer enhanced capabilities for identifying subtle neurodegenerative changes over time.

Methods

We selected 228 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had at least three T1-weighted MRI scans within 18 months of baseline. MRI volumes underwent bias correction, segme

SUBMITTER: Aghajanian S 

PROVIDER: S-EPMC12330009 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature