Dementia

Feasibility study protocol of the PainChek app to assess the efficacy of a social robot intervention for people with dementia.

AIM: This study aims to test the feasibility of the PainChek app to assess pain for people with dementia living in residential aged care facilities (RACFs). It will also identify the optimal dosage and efficacy of a social robot (personal assistant robot [PARO]) intervention on chronic pain for people with dementia.

A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease.

Ageing is associated with various ailments including Alzheimer 's disease (AD), which is a progressive form of dementia. AD symptoms develop over a period of years and, unfortunately, there is no cure. Existing AD treatments can only slow down the progression of symptoms and thus it is critical to diagnose the disease at an early stage. To help improve the early diagnosis of AD, a deep learning-ba...

Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method.

BACKGROUND: Alzheimer's disease is a chronic neurodegenerative disease that destroys brain cells, causing irreversible degeneration of cognitive funct...

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis.

Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer's disease (AD). The factors that lead to neurodegenerati...

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging ...

Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives.

OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The process...

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting...

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, in...

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review.

Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highl...

A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.

Structural magnetic resonance imaging (sMRI) can capture the spatial patterns of brain atrophy in Alzheimer's disease (AD) and incipient dementia. Rec...

Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.

Parkinson's disease (PD), Alzheimer's disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the...

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However...

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.

PURPOSE: To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving ...

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual pro...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' qu...

Dementia care, robot pets, and aliefs.

Studies have shown that using robot pets in dementia care contributes to a reduction in loneliness and anxiety, and other benefits. Studies also show ...

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology.

Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that p...

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to b...

Deep learning-based model for diagnosing Alzheimer's disease and tauopathies.

AIMS: This study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranu...

Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain....