Neurology

Dementia

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

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LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.

Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accu...

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and ...

Patterns and Trends in Global Nursing Robotics Research: A Bibliometric Study.

The present study aimed to investigate the trends and research status of global nursing robot resea...

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance ...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supp...

Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI.

This paper introduces a novel convolutional neural network model with an attention mechanism to adva...

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.

BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do...

Ensemble network using oblique coronal MRI for Alzheimer's disease diagnosis.

Alzheimer's disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mil...

New approach to specific Alzheimer's disease diagnosis based on plasma biomarkers in a cognitive disorder cohort.

BACKGROUND: The validation of a combination of plasma biomarkers and demographic variables is requir...

Class balancing diversity multimodal ensemble for Alzheimer's disease diagnosis and early detection.

Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence...

Transforming neurodegenerative disorder care with machine learning: Strategies and applications.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting...

Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care.

OBJECTIVE: Dementia represents a growing public health challenge, affecting an increasing number of ...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and i...

Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study.

BACKGROUND: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contri...

Weighted Multi-Modal Contrastive Learning Based Hybrid Network for Alzheimer's Disease Diagnosis.

Multiple imaging modalities and specific proteins in the cerebrospinal fluid, providing a comprehens...

Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.

Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving ...

Effectiveness of Chatbot interventions for reducing caregiver burden: Protocol for a systematic review and meta-analysis.

This protocol outlines a systematic review and meta-analysis examining the effectiveness of fully au...

Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression.

BACKGROUND: Early prediction of progression in dementia is of major importance for providing patient...

A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cogn...

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