AIMC Topic: Alzheimer Disease
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Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.
Neural networks : the official journal of the International Neural Network Society
Oct 15, 2024
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which ...
A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.
Journal of Alzheimer's disease : JAD
Oct 14, 2024
BACKGROUND: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors.
A support vector machine-based approach to guide the selection of a pseudo-reference region for brain PET quantification.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Oct 13, 2024
A Support Vector Machine (SVM) based approach was developed to identify a pseudo-reference region for brain PET scans with the aim of reducing interscan and intersubject variability. By training a binary linear SVM classifier with PET datasets from t...
Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery.
Medical image analysis
Oct 9, 2024
In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population...
A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data.
Neural networks : the official journal of the International Neural Network Society
Oct 5, 2024
Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for efficient, early diagnostic tools. Traditional methods like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans are expensive, bulky, and s...
Deep learning-based binary classification of beta-amyloid plaques using 18 F florapronol PET.
Nuclear medicine communications
Sep 27, 2024
PURPOSE: This study aimed to investigate a deep learning model to classify amyloid plaque deposition in the brain PET images of patients suspected of Alzheimer's disease.
Explainability of CNN-based Alzheimer's disease detection from online handwriting.
Scientific reports
Sep 27, 2024
With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning technique...
3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer's disease detection using structural MRI.
Biomedical physics & engineering express
Sep 24, 2024
Alzheimer's disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dem...
Early diagnosis of Alzheimer's Disease based on multi-attention mechanism.
PloS one
Sep 24, 2024
Alzheimer's Disease is a neurodegenerative disorder, and one of its common and prominent early symptoms is language impairment. Therefore, early diagnosis of Alzheimer's Disease through speech and text information is of significant importance. Howeve...
Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Sep 19, 2024
The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical appl...
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