AIMC Topic: Alzheimer Disease

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Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Oct 15, 2020
OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsE...
Humans Magnetic Resonance Imaging Electroencephalography Alzheimer Disease Neural Networks, Computer Brain Retrospective Studies
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Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

European journal of nuclear medicine and molecular imaging Sep 29, 2020
PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learnin...
Positron-Emission Tomography Stilbenes Humans Amyloid Deep Learning Alzheimer Disease Aniline Compounds
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Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

Computer methods and programs in biomedicine Sep 20, 2020
BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after hea...
Aged, 80 and over Deep Learning Area Under Curve Humans Neural Networks, Computer Aged Alzheimer Disease
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Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography.

Biomedical engineering online Sep 7, 2020
BACKGROUND: Alzheimer's Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to manage, accurate diagnosis of the disorder is critical. Previous studies have revealed effective deep ...
Alzheimer Disease Fluorodeoxyglucose F18 Image Processing, Computer-Assisted Cognition Positron Emission Tomography Computed Tomography Humans Neural Networks, Computer Sensitivity and Specificity
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Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Aug 14, 2020
OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic res...
Humans Machine Learning Brain Prognosis Female Aged Alzheimer Disease Neuroimaging Atrophy Cognitive Dysfunction Male Longitudinal Studies Middle Aged Magnetic Resonance Imaging Disease Progression
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VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

Computers in biology and medicine Aug 5, 2020
INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neurodegenerative diseases. Next Generation Sequencing (NGS) techniques are widely used for developing h...
Early Diagnosis Magnetic Resonance Imaging Alzheimer Disease Humans Machine Learning Genome-Wide Association Study
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Predicting Alzheimer's disease progression using deep recurrent neural networks.

NeuroImage Aug 4, 2020
Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more t...
Magnetic Resonance Imaging Alzheimer Disease Neuroimaging Deep Learning Models, Theoretical Longitudinal Studies Female Aged Aged, 80 and over Male Middle Aged Disease Progression Humans Prognosis
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AD risk score for the early phases of disease based on unsupervised machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association Jul 30, 2020
INTRODUCTION: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.
Female Risk Factors Unsupervised Machine Learning Aged Male Humans Alzheimer Disease Middle Aged Early Diagnosis Disease Progression
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Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

PloS one Jul 27, 2020
The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on subjects at various stage of symptomatic severity due to Alzheimer's disease. As expected, magnetic r...
Disease Progression Databases, Factual Principal Component Analysis Neuroimaging Humans Area Under Curve ROC Curve Biomarkers Apolipoprotein A-V Machine Learning Alzheimer Disease Magnetic Resonance Imaging Brain
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Alzheimer's Disease stage identification using deep learning models.

Journal of biomedical informatics Jul 23, 2020
OBJECTIVE: The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning models. This process facilitates the monitoring of the disease and allows actions to be taken in orde...
Deep Learning Magnetic Resonance Imaging Alzheimer Disease Humans Neural Networks, Computer
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