Alzheimer's Disease

Intelligent decision-making systems for early detection of alzheimer's disease using wearable technologies and deep learning.

Intelligent decision-making systems using wearable electronics and deep learning (DL) might identify Alzheimer's disease (AD) early for treatment. These technologies can continually monitor vital signs and behavioral characteristics to identify early cognitive deterioration in patients. Clinical examinations, neuroimaging, and cognitive testing are the main ways to identify Alzheimer's, but they a...

Detecting cognitive impairment and psychological well-being among older adults.

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or mild cognitive impairment, derived from remote video conversations and quantified thei...

Early diagnosis of Alzheimer's disease from functional rs-fMRI images based on deep learning networks and transfer learning approach.

Exploiting deep learning methods to accelerate the analysis of medical images and the interpretation of pathology results for early diagnosis of Alzhe...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...

Large Language Models Improve Scene Invariant Detection of Behaviours of Risk in Dementia Residential Care Across Multiple Surveillance Camera Views.

Behavioural and psychological symptoms of dementia pose challenges to the safety and well-being of individuals in residential care. The integration of...

Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer.

This study utilized a novel Proximity Barcoding Assay to perform high-resolution proteomic profiling of individual plasma extracellular vesicles from ...

A multi-view DTI feature fusion framework for enhanced diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder. Diffusion tensor imaging (DTI) is widely used to detect brain alterations for ...

Neural-linguistic analysis for Alzheimer's detection: A deep learning approach informed by cognitive neuroscience.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that disrupts cognitive function across multiple domains, particularly affecting ...

The Application of Artificial Intelligence to Older Adults With Disabilities: A Scoping Review.

ObjectiveTo review the application of artificial intelligence (AI) in the care of older adults with disabilities, identifying intervention type, targe...

Program Theory and Core Outcome Set Development for a Technology-Assisted Counseling Intervention in Dementia: Multimethods Study.

BACKGROUND: Counseling in family dementia care aims to support caregivers in mastering challenges. The use of information and communication technologi...

Deep Learning-Based Classification of Temporal Stages of AT8-Labeled Tau Pathology After Experimental Traumatic Brain Injury.

Tauopathies are characterised by a progressive accumulation of hyperphosphorylated tau. However, early and intermediate stages remain challenging to q...

Fluorescence from dual molecular rotors allows sensitive detection of widespread brain pathology in a mouse model of Alzheimer's disease.

Amyloid fibrils formed by the misfolding and aggregation of proteins are a pathological hallmark of many neurodegenerative conditions including Alzhei...

Classification of Alzheimer's Disease by Modeling Brain Networks as Signed Networks under Deep Learning Frameworks.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of defi...

Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future.

Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becoming increasingly prevalent worldwide. However, cur...

Circulating inflammatory proteins predict dementia risk, are linked to structural brain changes and modifiable risk factors.

INTRODUCTION: Systemic inflammation has been identified as a key factor in neurodegeneration but the value of circulating inflammatory proteins in dem...

Multi-omics Mendelian randomization and machine learning identify candidate therapeutic targets for Alzheimer's and Parkinson's diseases.

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are major public health challenges lacking effecti...

An attention-augmented multimodal classification of alzheimer's disease and parkinson's disease vs healthy controls using MRI, EEG, and SNP data.

Due to the late manifestation of structural symptoms and symptomatic overlap, neurodegenerative diseases such as Parkinson's Disease (PD) and Alzheime...

Sex Differences in Alzheimer's Disease: A Systematic Review of Two Decades of Neuroimaging Research.

OBJECTIVES: Given the heterogeneous nature of Alzheimer's Disease (AD) and its higher prevalence in females, it is crucial to understand sex-related d...

Core Elements of Goals of Care Initiatives Across Eight Health Systems.

For patients with serious illnesses, goals of care conversations improve quality of life and patient and family satisfaction and may reduce healthcare...

Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.

Convolutional neural networks (CNNs) are widely recognized for their high precision in image classification. Nevertheless, the lack of transparency in...