Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is characterized by significant variability in clinical progression; however, few studies have focused on developing models to predict cognitive decline. Anticipating these trajectories is essential for patient management, care planning, and developing new treatments. This study explores the potential of artificial intelligence (AI) techniques to model neurocognitive trajectories from multimodal neuroimaging data and further investigates different data representation frameworks.

Methods

Using information from 653 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we developed models to predict future clinical diagnoses and cognitive decline, both quantitatively (rate of decline) and qualitatively (presence or absen

SUBMITTER: Garcia-Gutierrez F 

PROVIDER: S-EPMC12753913 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature