Alzheimer's Disease

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discriminate preclinical Alzheimer's disease (AD) from age- and sex-matched controls.

Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning.

BACKGROUND: Alzheimer's disease (AD) and vascular dementia (VaD) share overlapping pathophysiological characteristics, yet comparative genetic studies are rare. Understanding these overlaps may aid in identifying common diagnostic markers and therapeutic targets.

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicate...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information ab...

Establishing a machine learning dementia progression prediction model with multiple integrated data.

OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical tools for predicting the progression of degenerat...

Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized multikernel support vector machine.

The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is c...

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.

The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or sever...

Exploring the Perspectives of Older Adults on a Digital Brain Health Platform Using Natural Language Processing: Cohort Study.

BACKGROUND: Although digital technology represents a growing field aiming to revolutionize early Alzheimer disease risk prediction and monitoring, the...

Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases.

Machine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. T...

Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid.

Solid-state NMR (ssNMR) methods have continued to be developed in recent years for the efficient assignment of signals and 3D structure modeling of bi...

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory....

Application and effectiveness of adaptive AI in elderly healthcare.

BACKGROUND: In addressing elderly healthcare issues, cognitive impairment can cause significant disruptions in daily life and may potentially develop ...

Ultrasensitive Detection of Blood-Based Alzheimer's Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning.

Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in thei...

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.

BACKGROUND: The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a c...

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 impa...

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. Ma...

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 ...

Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.

Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and...

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...