Title | Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Lu D, Popuri K, Ding GWeiguang, Balachandar R, Beg MFaisal |
Corporate Authors | Alzheimer’s Disease Neuroimaging Initiative |
Journal | Sci Rep |
Volume | 8 |
Issue | 1 |
Pagination | 5697 |
Date Published | 2018 04 09 |
ISSN | 2045-2322 |
Keywords | Aged, Aged, 80 and over, Alzheimer Disease, Brain, Case-Control Studies, Cognitive Dysfunction, Deep Learning, Early Diagnosis, Fluorodeoxyglucose F18, Humans, Magnetic Resonance Imaging, Middle Aged, Multimodal Imaging, Neural Networks, Computer, Positron-Emission Tomography, Radiopharmaceuticals, Sensitivity and Specificity |
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 |
Alternate Journal | Sci Rep |
PubMed ID | 29632364 |
PubMed Central ID | PMC5890270 |
Grant List | P41 EB015922 / EB / NIBIB NIH HHS / United States R01 AG055121 / AG / NIA NIH HHS / United States / / CIHR / Canada U01 AG024904 / AG / NIA NIH HHS / United States |
Related Institute:
Brain Health Imaging Institute (BHII)