Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images Academic Article uri icon

Overview

MeSH Major

  • Alzheimer Disease
  • Genomics
  • Neuroimaging
  • Proteomics

abstract

  • Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

authors

publication date

  • December 2018

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC5890270

Digital Object Identifier (DOI)

  • 10.1038/s41598-018-22871-z

PubMed ID

  • 29632364

Additional Document Info

start page

  • 5697

volume

  • 8

number

  • 1