scholarly journals Differential Diagnosis of Dementia with Lewy Bodies and Alzheimer Disease Using Combined MR Imaging and Brain Perfusion Single-Photon Emission Tomography

2010 ◽  
Vol 31 (4) ◽  
pp. 720-725 ◽  
Author(s):  
H. Goto ◽  
K. Ishii ◽  
T. Uemura ◽  
N. Miyamoto ◽  
T. Yoshikawa ◽  
...  
Keyword(s):  
Dementia With Lewy Bodies ◽  
Single Photon ◽  
Photon Emission ◽  
Lewy Bodies ◽  
Brain Perfusion ◽  
Emission Tomography ◽  
Single Photon Emission ◽  
Diagnosis Of Dementia ◽  
Differential Diagnosis Of Dementia ◽  
Photon Emission Tomography

scholarly journals Deep learning-based imaging classification identified cingulate island sign in dementia with Lewy bodies

2019 ◽  
Author(s):  
Tomomichi Iizuka ◽  
Makoto Fukasawa ◽  
Masashi Kameyama
Keyword(s):  
Deep Learning ◽  
Dementia With Lewy Bodies ◽  
Single Photon ◽  
Photon Emission ◽  
Lewy Bodies ◽  
Brain Perfusion ◽  
Emission Tomography ◽  
Single Photon Emission ◽  
Photon Emission Tomography ◽  
Imaging Classification

abstractThe differentiation of dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) using brain perfusion single photon emission tomography is important but has been a challenge because these conditions have common features. The cingulate island sign (CIS) is the most recently identified specific feature of DLB for a differential diagnosis. The present study aimed to examine the usefulness of deep learning-based imaging classification for the diagnoses of DLB and AD. We also investigated whether CIS was focused by the deep convolutional neural network (CNN) during differentiation.Brain perfusion single photon emission tomography images were acquired from 80 patients each with DLB and with AD and 80 individuals with normal cognition (NL). The CNN was trained on brain surface perfusion images. Gradient-weighted class activation mapping (Grad-CAM) was applied to the CNN for visualization of the features that the trained CNN focused on.Binary classifications between DLB and NL, DLB and AD and AD and NL were 94.69%, 87.81% and 94.38% accurate, respectively. The CIS ratios closely correlated with softmax output scores for DLB-AD discrimination (DLB/AD scores). The Grad-CAM highlighted CIS in the DLB discrimination. Visualization of learning process by guided Grad-CAM revealed that CIS became more focused by the CNN as the training progressed. DLB/AD score was significantly associated with three core-features of DLB.Deep learning-based imaging classification was useful not only for objective and accurate differentiation of DLB from AD but also for predicting clinical features of DLB. The CIS was identified as a specific feature during DLB classification. The visualization of specific features and learning process could have important implications for the potential of deep learning to discover new imaging features.

Early prediction of donepezil cognitive response in Alzheimer’s disease by brain perfusion single photon emission tomography

2019 ◽  
Vol 13 (6) ◽  
pp. 1665-1673
Author(s):  
Supatporn Tepmongkol ◽  
Solaphat Hemrungrojn ◽  
Patrick Dupont ◽  
Chavit Tunvirachaisakul ◽  
Daruj Aniwattanapong ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Single Photon ◽  
Photon Emission ◽  
Brain Perfusion ◽  
Single Photon Emission Tomography ◽  
Emission Tomography ◽  
Early Prediction ◽  
Single Photon Emission ◽  
Cognitive Response ◽  
Photon Emission Tomography
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