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Alzheimer Disease

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Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network.

European radiology
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...

Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning.

IEEE journal of biomedical and health informatics
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...

Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.

Journal of healthcare engineering
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...

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

JAMA network open
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.

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Sensors (Basel, Switzerland)
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...

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

Genome medicine
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...

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

Scientific reports
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...

Cholesterol Levels, Hormone Replacement Therapy, and Incident Dementia among Older Adult Women.

Nutrients
Previous studies revealed that hormone replacement therapy (HRT) probably has a protective effect for preventing dementia in post-menopausal women. However, the results were still controversial. The association between cholesterol levels and incident...

Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.

Neuroscience letters
PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnost...

c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.

BMC medical genomics
BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based b...