AIMC Topic: Alzheimer Disease

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A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Neural networks : the official journal of the International Neural Network Society Dec 6, 2023
Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined wit...
Deep Learning Cognitive Dysfunction Electroencephalography Humans Aged Alzheimer Disease Lewy Body Disease
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Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi Dec 2, 2023
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.
Alzheimer Disease Female Gene-Environment Interaction Aged Mitochondrial Precursor Protein Import Complex Proteins Humans Aged, 80 and over ROC Curve Sensitivity and Specificity Neural Networks, Computer Polymorphism, Single Nucleotide Machine Learning Male Support Vector Machine Case-Control Studies
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A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.

Neuroinformatics Dec 2, 2023
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble metho...
Magnetic Resonance Imaging Humans Alzheimer Disease Early Diagnosis Deep Learning Cognitive Dysfunction
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Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.

European radiology Nov 9, 2023
OBJECTIVES: Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, trai...
Humans Neural Networks, Computer Databases, Factual Brain Retrospective Studies Alzheimer Disease Atrophy Male Frontotemporal Lobar Degeneration Middle Aged Magnetic Resonance Imaging Female Aged
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Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning.

IEEE journal of biomedical and health informatics Nov 7, 2023
PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study inv...
Reproducibility of Results Deep Learning Magnetic Resonance Imaging Humans Brain Alzheimer Disease
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Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.

Journal of healthcare engineering Nov 3, 2023
The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer's. Therefore, the det...
Alzheimer Disease Deep Learning Humans Image Interpretation, Computer-Assisted Cognitive Dysfunction Aged Magnetic Resonance Imaging Neuroimaging
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Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

JAMA network open Nov 1, 2023
IMPORTANCE: Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.
Benchmarking Hispanic or Latino Alzheimer Disease Black or African American Asian White Male Middle Aged Disease Progression Aged Aged, 80 and over Humans Machine Learning Female
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Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Sensors (Basel, Switzerland) Oct 31, 2023
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of c...
Cognitive Dysfunction Alzheimer Disease Aging Deep Learning Humans Biomarkers Aged
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DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

Genome medicine Oct 31, 2023
BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine...
Neural Networks, Computer Mice Genotype Machine Learning Alzheimer Disease Phenotype Animals
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Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

Scientific reports Oct 24, 2023
The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of...
Alzheimer Disease Neoplasms Deep Learning Gene Expression Humans Learning
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