Dementia

Generative artificial intelligence in dementia care: a scoping review protocol on nursing roles, experiences and ethical perspectives.

INTRODUCTION: Generative artificial intelligence (GAI), including large language models and multimodal generative systems, is rapidly emerging in healthcare with growing interest in its potential applications for dementia care. These technologies offer new possibilities for communication support, cognitive engagement and personalised interaction, yet they also introduce complex ethical, relational...

TASNet: A tri-modal attentive scale robust adaptive fusion framework for progressive mild cognitive impairment classification.

BACKGROUND: Accurate prediction of progressive Mild Cognitive Impairment (pMCI) versus stable MCI (sMCI) is crucial for early Alzheimer's disease (AD) intervention and delaying disease progression. The combination of Positron Emission Tomography (PET) and structural Magnetic Resonance Imaging (sMRI) is currently the most recognized diagnostic basis. However, PET and sMRI reveal lesions with hetero...

Sleep disturbances and Alzheimer's disease: a multiscale approach from exposome to neurobiology and precision medicine.

Sleep disturbances and Alzheimer's disease (AD) are interconnected public health challenges. However, the underlying mechanisms of their complex relat...

AI and Wearables for Early Detection of Cognitive Impairment and Dementia: Systematic Review.

BACKGROUND: Traditional cognitive screening relies on episodic clinical assessments and may miss early changes preceding cognitive impairment and deme...

Real-time intraoperative confocal laser endomicroscopy with on-site neuropathologist consultation for glioma margin assessment: illustrative case.

BACKGROUND: Achieving maximal safe resection in glioma surgery requires accurate real-time margin assessment, yet existing technologies have limitatio...

Vocal biomarkers for geriatric health assessment: a scoping review protocol.

INTRODUCTION: Global ageing populations require accessible, non-invasive tools for early detection and monitoring of neurological chronic and neurodeg...

Gradient magnitude-based texture feature of the entorhinal cortex as a biomarker for Alzheimer's disease.

Volumetric analysis of brain structures is widely used to detect pathological changes in Alzheimer's disease (AD), but it has limited sensitivity to s...

Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.

Alzheimer's disease is a slow, progressive neurological disorder that impacts the brain tissue and causes cells to die, the most common reason for dem...

Unraveling the role of polyamine metabolism in postoperative delirium: insights into biochemical mechanisms and biomarker potential.

Postoperative delirium (POD) is a common complication in older surgical patients, linked to long-term cognitive decline and progression to dementia, y...

Patients and Caregivers Leveraging AI to Improve Their Health Care Journey: Case Study and Lessons Learned.

Artificial intelligence (AI) is increasingly integrated into everyday life. Yet in health care, patients and families are challenged to understand how...

Multimodal graph fusion-based GCN for Alzheimer's disease diagnosis using fMRI and T1-weighted MRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by both structural atrophy and functional dysregulation in the brain, yet ...

Subregional limbic radiomics on FDG-PET provides accurate early detection of Alzheimer's disease.

BACKGROUND: To investigate the radiomics features of the hippocampus and the amygdala subregions in FDG-PET images that can best differentiate Mild Co...

Gut-brain axis-mediated mechanisms and immune regulatory pathways in vascular dementia: insights into microbiota-derived metabolites and novel therapeutic strategies.

BACKGROUND: Vascular dementia (VaD), known for cognitive issues and cerebrovascular irregularities, is a common dementia type, but its molecular under...

Privacy-preserving multimodal fusion for Alzheimer's staging: A federated vision transformer framework with explainable AI.

Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multim...

Turning versus straight walking under dual-task conditions: a preliminary study for identifying mild cognitive impairment and Alzheimer's disease.

Deep learning models identify brain changes during the progression of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder whose progression is closely associated with time. However, most diagnostic mod...

[Criteria for evaluation of social robotics].

Currently a large number of people living in long-term care facilities suffer from cognitive disorders such as dementia. In this context, new robotic ...

Brain age prediction in a multiethnic Asian population: A comparison of machine learning algorithms and their application for early-stage cognitive impairment diagnosis.

BackgroundNeuroimaging-derived brain age is a promising biomarker of early neurodegeneration, but methodological variation in machine learning (ML) al...

Insights on Recruitment, Implementation, and Movement Pattern Detection by Exploring the Feasibility of Sensor-Based Insole Technology in Long-Term Care: Mixed Methods Feasibility Study.

BACKGROUND: Sensor-based footwear is increasingly discussed as a promising tool for mobility monitoring and fall-risk assessment, yet its applicabilit...

Neuropathologic basis of quantitative susceptibility mapping in the substantia nigra: contributions of tau, pigmented neurons, and iron.

Quantitative susceptibility mapping (QSM) on MRI quantifies tissue magnetic susceptibility, which increases with iron accumulation, myelin loss, and n...