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

Publication Type	Academic Article
Authors	Lu D, Popuri K, Ding G, Balachandar R, Beg M
Journal	Sci Rep
Volume	8
Issue	1
Pagination	5697
Date Published	04/09/2018
ISSN	2045-2322
Keywords	Alzheimer Disease, Brain, Cognitive Dysfunction, Fluorodeoxyglucose F18, Multimodal Imaging, Radiopharmaceuticals
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.
DOI	10.1038/s41598-018-22871-z
PubMed ID	29632364
PubMed Central ID	PMC5890270