Image-Based Early Detection of Alzheimer’s Disease by Using Adaptive Structural Deep Learning

2021 ◽  
pp. 595-605
Author(s):  
Shin Kamada ◽  
Takumi Ichimura ◽  
Toshihide Harada
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection

scholarly journals Deep Learning Image Processing Technique for Early Detection of Alzheimer's Disease

2017 ◽  
Vol 107 ◽  
pp. 85-104
Author(s):  
Raju Anitha ◽  
S. Jyothi ◽  
Venkata Naresh Mandhala ◽  
Debnath Bhattacharyya ◽  
Tai-hoon Kim
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection ◽  
Processing Technique ◽  
Image Processing Technique

scholarly journals Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Taeho Jo ◽  
◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Andrew J. Saykin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Deep Learning ◽  
Early Detection ◽  
Medial Temporal Lobe ◽  
Memory Loss ◽  
Disease Classification ◽  
Classification Model ◽  
Tau Pet

Abstract Background Alzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. Results The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). Conclusion A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

scholarly journals Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

2020 ◽  
Author(s):  
Taeho Jo ◽  
Kwangsik Nho ◽  
Shannon L. Risacher ◽  
Andrew J. Saykin ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Deep Learning ◽  
Early Detection ◽  
Group Analysis ◽  
Statistical Parametric Mapping ◽  
Classification Model ◽  
Tau Pet ◽  
3D Cnn

AbstractBackgroundAlzheimer’s disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans.MethodsWe analysed [18F]flortaucipir PET image data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. We first developed an image classifier to distinguish AD from cognitively normal (CN) older adults by training a 3D convolutional neural network (CNN)-based deep learning model on tau PET images (N=132; 66 CN and 66 AD), then applied the classifier to images from individuals with mild cognitive impairment (MCI; N=168). In addition, we applied a layer-wise relevance propagation (LRP)-based model to identify informative features and to visualize classification results. We compared these results with those from whole brain voxel-wise between-group analysis using conventional Statistical Parametric Mapping (SPM12).ResultsThe 3D CNN-based classification model of AD from CN yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The LRP results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r=0.43 for early MCI and r=0.49 for late MCI).ConclusionA deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

scholarly journals P3-359: EARLY DETECTION OF ALZHEIMER'S DISEASE BASED ON DEEP LEARNING

2019 ◽  
Vol 15 ◽  
pp. P1082-P1083
Author(s):  
Dan Pan ◽  
An Zeng ◽  
Longfei Jia ◽  
Xiaowei Song
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Early Detection

Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly: Preliminary Study (Preprint)

2021 ◽  
Author(s):  
Hyoun-Joong Kong ◽  
Kichan Ahn ◽  
Minwoo Cho ◽  
Suk Wha Kim ◽  
Kyu Eun Lee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health Care ◽  
Deep Learning ◽  
Early Detection ◽  
The Elderly ◽  
Training Data ◽  
Control Group ◽  
Screening Process ◽  
Remote Health

BACKGROUND Alzheimer's disease is the most common form of dementia, and it is a disease that makes it difficult for patients and their families due to various symptoms. For these reasons, early detection is very important, and after early detection, symptoms can be alleviated through medication and treatment. OBJECTIVE Since Alzheimer's disease strongly induces language disorders, our research goal is detecting Alzheimer's disease quickly and easily through the analysis of language characteristics. METHODS Using the Mini-Mental State Examination for Dementia Screening (MMSE-DS), which is the most used in Korean public health centers, negative answers were obtained according to the questionnaire. Among the acquired voices, significant questionnaires and answers were selected, spectrogrammed, and converted into MFCC. After accumulating significant answers, training data was created, augmented, and then trained on various deep learning models and the results were observed. RESULTS Due to the lack of data, the results of the five-fold cross validation were more significant than the holdout method. the accuracy of separating AD patients from the control group using Densnet121 was 91.25%. CONCLUSIONS In this regard, the potential for remote health care can be increased by simplifying the AD screening process. By facilitating remote health care, the proposed method is expected to enhancing the accessibility of AD screening and increase the rate of early AD detection. CLINICALTRIAL IRB No. CNUH2019-02-068

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