Neurology

Dementia

Latest AI and machine learning research in dementia for healthcare professionals.

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APOE ε4 carriers share immune-related proteomic changes across neurodegenerative diseases.

The APOE ε4 genetic variant is the strongest genetic risk factor for late-onset Alzheimer's disease ...

Evaluating cognitive decline detection in aging populations with single-channel EEG features based on two studies and meta-analysis.

Timely detection of cognitive decline is paramount for effective intervention, prompting researchers...

Bayesian Posterior Distribution Estimation of Kinetic Parameters in Dynamic Brain PET Using Generative Deep Learning Models.

Positron Emission Tomography (PET) is a valuable imaging method for studying molecular-level process...

Multimodal Detection of Agitation in People With Dementia in Clinical Settings: Observational Pilot Study.

BACKGROUND: Dementia is a progressive neurodegenerative condition that affects millions worldwide, o...

Cohort profile: China healthy aging cohort study (China-Aging).

BackgroundAs China undergoes a demographic transition towards an aging society, the prevalence and i...

A hybrid learning approach for MRI-based detection of alzheimer's disease stages using dual CNNs and ensemble classifier.

Alzheimer's Disease (AD) and related dementias are significant global health issues characterized by...

Biomarkers and therapeutic strategies targeting microglia in neurodegenerative diseases: current status and future directions.

Recent advances in our understanding of non-cell-autonomous mechanisms in neurodegenerative diseases...

Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques.

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing s...

Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.

The progression of Alzheimer's disease is relentless, leading to a worsening of mental faculties ove...

Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to m...

Structural and pragmatic language skills in school-age children relate to resting state functional connectivity.

Language difficulties are common in school-age children but their etiology is often unknown. Althoug...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leadin...

Speech production as an artificial intelligence-based 'process' measure of cognition sensitive to mild cognitive impairment and Alzheimer's disease.

Process scores in neuropsychological tests add incremental validity for detecting non-normative cog...

Biomimetic Analysis of Neurotransmitters for Disease Diagnosis through Light-Driven Nanozyme Sensor Array and Machine Learning.

Neurological diseases, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, p...

Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.

BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current bio...

Effect of a generative artificial intelligence digital scribe on pediatric provider documentation time, cognitive burden, and burnout.

OBJECTIVE: To assess the effect of a digital scribe among pediatric providers on documentation time,...

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Accurately predicting cognitive scores based on structural MRI holds significant clinical value for ...

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pat...

Artificial Intelligence in Diagnosis and Prognosis of Cognitive Impairment in Parkinson's Disease.

Parkinson's disease (PD), a progressive neurodegenerative disorder, affects millions globally, with ...

Using Machine Learning to Predict Treatment Outcome in a Concatenated Dataset of Youth Anxiety Treatments.

Machine Learning (ML) is a promising approach for predicting outcomes of youth anxiety treatments. T...

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