Dementia

Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer and Their Caregivers.

OBJECTIVE: This study evaluated dyadic endocrine interdependence among cancer patients and their spousal caregivers using a machine learning (ML) framework that assesses how partner-level information improves prediction of individual stress biomarker responses. METHODS: A multilevel ML framework was applied to 149 patient-caregiver dyads. Predictive performance was evaluated for mean and variabili...

[Fall prediction models in older adults with dementia: a rapid review].

INTRODUCTION: Falls in older adults with dementia are common and have multiple consequences for their health and quality of life. Fall prediction models are the core of the development of interventions to prevent falls, especially in institutional settings. OBJECTIVES: To identify and describe existing clinical and technology-based models to predict falls in older adults with dementia. METHODS: A ...

Adversarial attacks on a multimodal Alzheimer's disease detection system reveal complex interdependences between heterogeneous modalities.

Multi-modal models that fuse neuroimaging with clinical assessment data represent the current state of the art for automated Alzheimer's disease detec...

A review of Alzheimer's disease diagnosis and prognosis models based on multimodal deep learning.

Although some drugs have been approved for clinical treatment, early diagnosis and intervention remain the most effective strategies for managing Alzh...

Beyond complex architectures: a streamlined CNN pipeline for robust Alzheimer's disease classification from brain MRI.

BACKGROUND AND PURPOSE: Alzheimer's disease, a common type of dementia, gradually steals memories and impacts daily life as brain cells deteriorate. W...

Explainable foundation model for dementia screening and risk stratification using retinal fundus images.

Dementia is a growing global health challenge, and early identification is essential for timely intervention. We evaluated whether foundation model-ba...

Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study.

BACKGROUND: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there a...

Real-Time Smartphone Monitoring Assessments as a Cognitive Biomarker of Alzheimer Disease: Protocol for a Development Study.

BACKGROUND: The diagnosis and monitoring of Alzheimer disease (AD) currently rely on clinician-administered, in-person, and cross-sectional pen-and-pa...

Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.

Autism spectrum disorder (ASD) affects tens of millions of families worldwide, yet parents confront abundant but unreliable online advice and limited ...

CausalTCC: causal temporal contrastive learning for automated Alzheimer's disease biomarker discovery with bio-electrical signals.

OBJECTIVE: Learning robust representations from scarce labeled bio-electrical time-series data remains a critical challenge in clinical diagnosis. Whi...

Variations of global brain asymmetry are associated with aging and related diseases.

Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cog...

Readability and Linguistic Characteristics of Alzheimer's Disease and Related Dementias Prevention, Symptom, and Treatment Information from Generative Artificial Intelligence Chatbots.

PURPOSE: To examine the readability and linguistic characteristics of Alzheimer's disease and related dementias (ADRD) prevention, symptom, and treatm...

Leveraging Swin Transformer for Enhanced Diagnosis of Alzheimer's Disease Using Multi-Shell Diffusion MRI.

OBJECTIVE: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural i...

Time Distributed Classification of Alzheimer's Disease on MRI Scans.

The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study...

The creation and verification of a detection model for mild cognitive impairment by employing eye-tracking and gait metrics.

BackgroundMild cognitive impairment is a prodromal stage of dementia, and early identification is crucial for prognosis.ObjectiveThis study aims to cr...

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

This study introduces a deep learning framework for the inferential exploration of latent representations in 3D brain MRI, leveraging a simple convolu...

Wearable sensor-based Mild Cognitive Impairment Identification: A Multi-Domain Gait Analysis Approach with Association Rule Mining.

Mild Cognitive Impairment (MCI) involves cognitive decline beyond age-expected norms without impairing daily function. Crucially, it often precedes de...

Machine Learning-Based Identification and Ranking of Mortality Predictors After Hip Fracture Surgery: An Analysis of the Australian and New Zealand Hip Fracture Registry.

BACKGROUND: Hip fractures are a major global health issue with high mortality and morbidity, especially in older adults. One-year mortality post-surge...

From companion technologies to social care infrastructure: A multi-level perspective on loneliness-related support in dementia care in an era of artificial intelligence (AI).

Loneliness and social isolation are important psychosocial concerns in dementia care, but they are difficult to address through pharmacological treatm...

Magnetic resonance imaging-based Alzheimer's disease detection using an EfficientNet-CMSACCN framework.

Alzheimer's Disease (AD) is a degenerative neurological condition characterized by memory loss, cognitive deterioration, and brain tissue shrinkage. D...