Neurology

Dementia

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

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Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, the...

Deep learning based mild cognitive impairment diagnosis using structure MR images.

Mild cognitive impairment (MCI) is an early sign of Alzheimer's disease (AD) which is the fourth lea...

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with ...

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Diseas...

Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis.

Functional connectivity networks (FCNs) based on functional magnetic resonance imaging (fMRI) have b...

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cere...

AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia.

OBJECTIVE: An artificial intelligence (AI)-based algorithm typically requires a considerable amount ...

Automatic assessment of Alzheimer's disease diagnosis based on deep learning techniques.

Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists...

Caregiver perspectives on a smart home-based socially assistive robot for individuals with Alzheimer's disease and related dementia.

: Innovative assistive technology can address aging-in-place and caregiving needs of individuals wit...

Deep learning prediction of falls among nursing home residents with Alzheimer's disease.

AIM: This study aimed to use a convolutional neural network (CNN) to investigate the associations be...

Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction.

In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a st...

Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent.

The search for early biomarkers of mild cognitive impairment (MCI) has been central to the Alzheimer...

Identification of Methylated Gene Biomarkers in Patients with Alzheimer's Disease Based on Machine Learning.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder and characterized by the cognit...

Alzheimer's disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning.

A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) ...

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease.

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cogni...

The Effect of Using PARO for People Living With Dementia and Chronic Pain: A Pilot Randomized Controlled Trial.

OBJECTIVES: To evaluate the effect of interaction with a robotic seal (PARO) on pain and behavioral ...

A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI.

Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) h...

Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.

OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that is characte...

A proof of concept machine learning analysis using multimodal neuroimaging and neurocognitive measures as predictive biomarker in bipolar disorder.

BACKGROUND: Concomitant use of complementary, multimodal imaging measures and neurocognitive measure...

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