AIMC Topic: Alzheimer Disease

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Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Computational and mathematical methods in medicine Oct 4, 2021
Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine lear...
Computational Biology Case-Control Studies Magnetic Resonance Imaging Deep Learning Multimodal Imaging Humans Alzheimer Disease Cognitive Dysfunction Bayes Theorem Neural Networks, Computer Early Diagnosis Imaging, Three-Dimensional Prognosis Neuroimaging Normal Distribution
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Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging Oct 2, 2021
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...
Humans Biomarkers Cognitive Dysfunction Alzheimer Disease Positron-Emission Tomography Fluorodeoxyglucose F18 Brain Tomography, X-Ray Computed Quality of Life Deep Learning
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A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection.

Computational and mathematical methods in medicine Sep 13, 2021
Artificial Intelligence (AI) is the domain of computer science that focuses on the development of machines that operate like humans. In the field of AI, medical disease detection is an instantly growing domain of research. In the past years, numerous...
Machine Learning Diagnosis, Computer-Assisted Computational Biology Bayes Theorem Humans Neural Networks, Computer Alzheimer Disease Artificial Intelligence Nonlinear Dynamics Image Interpretation, Computer-Assisted Stochastic Processes
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Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

Nature communications Sep 10, 2021
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we...
Datasets as Topic Aged Gene Expression Regulation Microglia Aged, 80 and over Alzheimer Disease Animals Sex Factors Male Humans Deep Learning Brain Cohort Studies Female RNA-Seq Mice
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Deep learning-based model for diagnosing Alzheimer's disease and tauopathies.

Neuropathology and applied neurobiology Aug 31, 2021
AIMS: This study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau-immunostai...
tau Proteins Supranuclear Palsy, Progressive Tauopathies Pick Disease of the Brain Humans Deep Learning Alzheimer Disease
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Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.

IEEE transactions on medical imaging Aug 31, 2021
Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural c...
Humans Brain Deep Learning Attention Cognitive Dysfunction Magnetic Resonance Imaging Alzheimer Disease
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DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.

NeuroImage Aug 24, 2021
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. Howe...
Humans Neural Networks, Computer Algorithms Alzheimer Disease Biomarkers Hippocampus Female Disease Progression Neuroimaging Cognitive Dysfunction Aged Atrophy Longitudinal Studies Aged, 80 and over Magnetic Resonance Imaging Male
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Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.

Acta neuropathologica communications Aug 21, 2021
Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting semiquantitative scores often vary between individual raters and research centers, limiting statistical...
Cerebral Amyloid Angiopathy Microglia Astrocytes Humans Alzheimer Disease Neural Networks, Computer Brain Deep Learning
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Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Aug 16, 2021
OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to accurately classify Healthy Controls, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment,...
Alzheimer Disease Rest Nerve Net Male Cognitive Dysfunction Humans Support Vector Machine Algorithms Magnetic Resonance Imaging Female Databases, Factual Aged Brain Aged, 80 and over
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Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Scientific reports Aug 3, 2021
Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning models have been implemented to develop normative models to investigate brain disorders, including regre...
Male Middle Aged Disease Progression Case-Control Studies Female Aged Cross-Sectional Studies Neuroimaging Cognitive Dysfunction Cohort Studies Models, Statistical Alzheimer Disease Humans Neural Networks, Computer Machine Learning Brain
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