TY - JOUR
T1 - Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images
AU - Lu, Donghuan
AU - Popuri, Karteek
AU - Ding, Gavin Weiguang
AU - Balachandar, Rakesh
AU - Beg, Mirza Faisal
AU - Weiner, Michael
AU - Aisen, Paul
AU - Petersen, Ronald
AU - Jack, Cliford
AU - Jagust, William
AU - Trojanowki, John
AU - Toga, Arthur
AU - Beckett, Laurel
AU - Green, Robert
AU - Saykin, Andrew
AU - Morris, John C.
AU - Shaw, Leslie
AU - Kaye, Jefrey
AU - Quinn, Joseph
AU - Silbert, Lisa
AU - Lind, Betty
AU - Carter, Raina
AU - Dolen, Sara
AU - Schneider, Lon
AU - Pawluczyk, Sonia
AU - Beccera, Mauricio
AU - Teodoro, Liberty
AU - Spann, Bryan
AU - Brewer, James
AU - Vanderswag, Helen
AU - Fleisher, Adam
AU - Heidebrink, Judith
AU - Lord, Joanne
AU - Mason, Sara
AU - Albers, Colleen
AU - Knopman, David
AU - Johnson, Kris
AU - Doody, Rachelle
AU - Villanueva-Meyer, Javier
AU - Chowdhury, Munir
AU - Rountree, Susan
AU - Dang, Mimi
AU - Stern, Yaakov
AU - Honig, Lawrence
AU - Bell, Karen
AU - Ances, Beau
AU - Carroll, Maria
AU - Creech, Mary
AU - Franklin, Erin
AU - Jicha, Greg
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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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U2 - 10.1038/s41598-018-22871-z
DO - 10.1038/s41598-018-22871-z
M3 - Article
C2 - 29632364
AN - SCOPUS:85052338503
SN - 2045-2322
VL - 8
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 5697
ER -