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

Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

19 Citations (Scopus)

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.

Original languageEnglish (US)
Article number5697
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

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Early Diagnosis
Alzheimer Disease
Neuroimaging
Neurodegenerative Diseases
Biomarkers
Magnetic Resonance Imaging
Learning
Glucose
Brain

ASJC Scopus subject areas

  • General

Cite this

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images. / Alzheimer’s Disease Neuroimaging Initiative.

In: Scientific Reports, Vol. 8, No. 1, 5697, 01.12.2018.

Research output: Contribution to journalArticle

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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.",
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AU - Ding, Gavin Weiguang

AU - Balachandar, Rakesh

AU - Beg, Mirza Faisal

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AU - Aisen, Paul

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AU - Chowdhury, Munir

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