• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2362-2366
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• 2003

The paper describes a new instrument for early detection of Alzheimer's disease. A new instrument for early detection of Alzheimer's disease is constructed on both the questionnaire for the investigation of living environment, and the lists for the functional tests of the sense, the physiology, and the left and right brains. When the medical doctor has made a diagnosis of Alzheimer's disease, the demented patient does not recover the indication adding available treatments. Then, the indication of a patient only takes a turn for the worse. For the demented patient can be kept his/her life style, Alzheimer's disease can make an early detection using a new instrument before a diagnosis of the dementia. And the indication of a demented patient can be delayed by the available medical treatments.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2367-2371
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• 2003

A new instrument for early detection of Alzheimer's disease is constructed from the investigative items with both the investigation of living environment, and the functional tests of the sense, the physiology, and the left and right brains. This paper describes the indexes obtained from the results of test using a new instrument for early detection of Alzheimer's disease. The indexes for early detection of Alzheimer's disease were obtained from the investigations of the living environment and the social adaptability, the functional tests of the sight and the hearing in the five senses, and the functional tests of left hemispheres in brain.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.39
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• pp.129-142
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• 1996

The early diagnosis has an very important role in curing dementia. But there was not the effective method to diagnose it until now. In this paper we analyzed the EEG of Alzheimer's disease patients and normal groups by nonlinear methods. In the analysis we calculated the correlation dimensions $D_2$ and the largest Lyapunov exponent $L_1$. We found that patients with Alzheimer's disease have significantly lower $D_2$ and TEX>$L_1$ than normal groups. It means that brains injured by Alzheimer's disease have electrophysiological inactive elements and have decreased chaotic behaviour. We propose the nonlinear analysis of the EEG as a useful tool for the early diagnosis of Alzheimer's disease.

• Biomedical Science Letters
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• v.21 no.1
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• pp.1-8
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• 2015

Alzheimer's disease is a common form of dementia occurring among the elderly population and can be identified by symptoms such as cognition impairments, memory loss and neuronal dysfunction. Alzheimer's disease was found to be caused by the deposition of $\beta$-amyloid plaques and neurofibrillary tangles. In addition, mutation in the APP (Amyloid precursor protein), Presenilin 1 (PSEN1) and Presenilin 2 (PSEN2) genes were also found to contribute to Alzheimer's disease. Since the potential conformational diagnosis of Alzheimer's disease requires histopathological tests on brain through autopsy, potential early diagnosis still remains challenging. In recent years, several researches have proposed the use of biomarkers for early diagnosis. In cerebrospinal fluid (CSF), $\beta$-amyloid(1-42), phosphorylated-tau and total tau were suggested to be effective biomarkers for Alzheimer's disease diagnosis. However, a single biomarker might not be sufficient for potential diagnosis of Alzheimer's disease. Thus, the use of RNA interference (RNAi) through microRNAs (miRNAs) has been proposed by several researchers for simultaneous analysis of several biomarkers using microarray technology. These miRNA based biomarkers can be analysed from both blood and CSF, but miRNAs from blood are advantageous over CSF as they are non-invasive and simple for collection. Moreover, the RNAi based therapeutics by siRNA (short interference RNA) or shRNA (short hairpin RNA) have also been proposed to be effective in the treatment of Alzheimer's disease. This review describes the promising application of RNAi technology in therapeutics and as a biomarker for both Alzheimer's disease diagnosis and treatment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.97-99
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• 2021

As the aging society continues in recent years, interest in dementia is increasing. Among them, Alzheimer's disease is a degenerative brain disease that accounts for the largest percentage of all dementia patients, with the medical community currently not offering clear prevention and treatment for Alzheimer's disease, and the importance of early treatment and early prevention is emphasized. In this paper, we intend to find the most efficient activation function by combining various activation functions centering on convolutional neural networks using MRI datasets of normal people and patients with Alzheimer's disease. In addition, it is intended to be used as a dementia classification modeling suitable for the medical field in the future through Alzheimer's dementia classification modeling.

• Smart Media Journal
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• v.13 no.6
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.166-171
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• 2008

PET of the cerebral metabolic rate of glucose is increasingly used to support the clinical diagnosis in the examination of patients with suspected major neurodegenerative disorders, such as Alzheimer's disease. $^{18}F-FDG$ PET has been reported to have high diagnostic performance, especially, very high sensitivity in the diagnosis and clinical assessment of therapeutic efficacy. According to clinical research data hitherto, $^{18}F-FDG$ PET is expected to be an effective diagnostic tool in early and differential diagnosis of Alzheimer's disease. Since 2004, Medicare covers $^{18}F-FDG$ PET scans for the differential diagnosis of fronto-temporal dementia (FTD) and Alzheimer's disease (AD) under specific requirements; or, its use in a CMS approved practical clinical trial focused on the utility of $^{18}F-FDG$ PET in the diagnosis or treatment of dementing neurodegenerative diseases.

• BMB Reports
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• v.53 no.1
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• pp.28-34
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• 2020

Sphingolipids are ubiquitous building blocks of eukaryotic cell membranes that function as signaling molecules for regulating a diverse range of cellular processes, including cell proliferation, growth, survival, immune-cell trafficking, vascular and epithelial integrity, and inflammation. Recently, several studies have highlighted the pivotal role of sphingolipids in neuroinflammatory regulation. Sphingolipids have multiple functions, including induction of the expression of various inflammatory mediators and regulation of neuroinflammation by directly effecting the cells of the central nervous system. Accumulating evidence points to sphingolipid engagement in neuroinflammatory disorders, including Alzheimer's and Parkinson's diseases. Abnormal sphingolipid alterations, which involves an increase in ceramide and a decrease in sphingosine kinase, are observed during neuroinflammatory disease. These trends are observed early during disease development, and thus highlight the potential of sphingolipids as a new therapeutic and diagnostic target for neuroinflammatory diseases.

• Journal of the Korean Society of Radiology
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• v.10 no.5
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• pp.307-312
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• 2016

Alzheimer's disease is the most common degenerative brain diseases that causes dementia. ${\beta}$-amyloid neuritic plaque density that accumulates in the brain is difficult to perform daily living, such as memory loss, language ability deterioration. It is used to estimate ${\beta}$-amyloid neuritic plaque density in adult patients with cognitive impairment who are being evaluated for Alzheimer's disease and other causes of cognitive impairment. Using the $^{18}F$-Florbetaben with high sensitivity and specificity for the ${\beta}$-amyloid neuritic plaque density to evaluate the usefulness for the early diagnosis of Alzheimer's disease. In $^{18}F$-FDG Brain imaging shows no specific findings. And it appeared on the MR-Brain imaging without atrophy of the hippocampus. However, the intake of ${\beta}$-amyloid neuritic plaque density in $^{18}F$-Florbetaben informs that it is the progress of Alzheimer's disease. Therefore, $^{18}F$-Florobetaben is very useful for early diagnosis of Alzheimer's disease.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.160-167
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• 1996

The early diagnosis has an very important role in curing dementia. But there was not an effective method to diagnose it until now. In this paper we analyzed the EEG in Alzheimer's disease and normal control groups to compare by nonlinear parameter such as the largest Lyapunov exponent $L_{1}$. We found that patients with Alzheimer's disease have significantly lower$L_{1}$ than normal groups. And we propose the nonlinear analysis of EEG as a useful tool for the early diagnosis of Alzheimer's disease.