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

Publication ,  Journal Article
Lu, D; Popuri, K; Ding, GW; Balachandar, R; Beg, MF; Alzheimer’s Disease Neuroimaging Initiative,
Published in: Sci Rep
April 9, 2018

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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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

April 9, 2018

Volume

8

Issue

1

Start / End Page

5697

Location

England

Related Subject Headings

  • Sensitivity and Specificity
  • Radiopharmaceuticals
  • Positron-Emission Tomography
  • Neural Networks, Computer
  • Multimodal Imaging
  • Middle Aged
  • Magnetic Resonance Imaging
  • Humans
  • Fluorodeoxyglucose F18
  • Early Diagnosis
 

Citation

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Lu, D., Popuri, K., Ding, G. W., Balachandar, R., Beg, M. F., & Alzheimer’s Disease Neuroimaging Initiative, . (2018). Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Sci Rep, 8(1), 5697. https://doi.org/10.1038/s41598-018-22871-z
Lu, Donghuan, Karteek Popuri, Gavin Weiguang Ding, Rakesh Balachandar, Mirza Faisal Beg, and Mirza Faisal Alzheimer’s Disease Neuroimaging Initiative. “Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.Sci Rep 8, no. 1 (April 9, 2018): 5697. https://doi.org/10.1038/s41598-018-22871-z.
Lu D, Popuri K, Ding GW, Balachandar R, Beg MF, Alzheimer’s Disease Neuroimaging Initiative. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Sci Rep. 2018 Apr 9;8(1):5697.
Lu, Donghuan, et al. “Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images.Sci Rep, vol. 8, no. 1, Apr. 2018, p. 5697. Pubmed, doi:10.1038/s41598-018-22871-z.
Lu D, Popuri K, Ding GW, Balachandar R, Beg MF, Alzheimer’s Disease Neuroimaging Initiative. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Sci Rep. 2018 Apr 9;8(1):5697.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

April 9, 2018

Volume

8

Issue

1

Start / End Page

5697

Location

England

Related Subject Headings

  • Sensitivity and Specificity
  • Radiopharmaceuticals
  • Positron-Emission Tomography
  • Neural Networks, Computer
  • Multimodal Imaging
  • Middle Aged
  • Magnetic Resonance Imaging
  • Humans
  • Fluorodeoxyglucose F18
  • Early Diagnosis