Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

ABSTRACT:

Background

Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression.

Methods

This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) databas

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10926590 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature