Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.

ABSTRACT: A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics in

SUBMITTER: Lin E 

PROVIDER: S-EPMC8347529 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature