Multimodal attention-based deep learning for Alzheimer's disease diagnosis.

ABSTRACT:

Objective

Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis.

Materials and methods

We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between modalities-a method not previously explored in this domain. We perform multi-class classification, a challenging task considering the strong similarities bet

SUBMITTER: Golovanevsky M 

PROVIDER: S-EPMC9667156 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature