Dementia

Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024).

BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burde...

Machine learning-based predictive model for post-stroke dementia.

BACKGROUND: Post-stroke dementia (PSD), a common complication, diminishes rehabilitation efficacy and affects disease prognosis in stroke patients. Many factors may be related to PSD, including demographic, comorbidities, and examination characteristics. However, most existing methods are qualitative evaluations of independent factors, which ignore the interaction amongst various factors. Therefor...

Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.

BACKGROUND: The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) mode...

Comparison of machine learning algorithms for automatic prediction of Alzheimer disease.

BACKGROUND: Alzheimer disease is a progressive neurological disorder marked by irreversible memory loss and cognitive decline. Traditional diagnostic ...

Evaluating the User Experience and Usability of the MINI Robot for Elderly Adults with Mild Dementia and Mild Cognitive Impairment: Insights and Recommendations.

: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continu...

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning.

BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular ...

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms...

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification ...

Prediction and clustering of Alzheimer's disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data.

Early detection of Alzheimer's disease (AD) is crucial to maximize clinical outcomes. Most disease progression analyses include people with diagnoses ...

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectr...

A modified deep learning method for Alzheimer's disease detection based on the facial submicroscopic features in mice.

Alzheimer's disease (AD) is a chronic disease among people aged 65 and older. As the aging population continues to grow at a rapid pace, AD has emerge...

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity.

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders...

Plasma protein-based identification of neuroimage-driven subtypes in mild cognitive impairment via protein-protein interaction aware explainable graph propagational network.

As an early indicator of dementia, mild cognitive impairment (MCI) requires specialized treatment according to its subtypes for the effective preventi...

Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.

BACKGROUND: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming ...

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

BACKGROUND: Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characterized by the accumulation of misfolded amyloid beta p...

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.

Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbiased, and sensitive methods to assess mouse behavior...

MRI-based mild cognitive impairment and Alzheimer's disease classification using an algorithm of combination of variational autoencoder and other machine learning classifiers.

BACKGROUND: Correctly diagnosing mild cognitive impairment (MCI) and Alzheimer's disease (AD) is important for patient selection in drug discovery. Re...

Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.

Two themes are coming to the forefront in this decade: Cognitive impairment of an aging population and the quantum leap in developing artificial intel...

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease an...

An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket.

Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychologi...