Alzheimer's Disease

A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-rel...

Serum amyloid A4 is a procoagulant apolipoprotein that it is elevated in venous thrombosis patients.

BACKGROUND: Serum amyloid A4 (SAA4) is an apolipoprotein that is in the SAA family and it is constitutively translated. Previously, acute-phase SAA1 and SAA2 levels were associated with venous thromboembolism (VTE).

Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a...

Dual-functional neural network for bilateral hippocampi segmentation and diagnosis of Alzheimer's disease.

PURPOSE: Knowing the course of Alzheimer's disease is very important to prevent the deterioration of the disease, and accurate segmentation of sensiti...

Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease.

Multi-modality based classification methods are superior to the single modality based approaches for the automatic diagnosis of the Alzheimer's diseas...

Ethical concerns with the use of intelligent assistive technology: findings from a qualitative study with professional stakeholders.

BACKGROUND: Advances in artificial intelligence (AI), robotics and wearable computing are creating novel technological opportunities for mitigating th...

Shall I compare thee…to a robot? An exploratory pilot study using participatory arts and social robotics to improve psychological well-being in later life.

Research suggests that the use of creative, artistic activities in later life may positively impact the psychological well-being of older adults. Soci...

A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild cognitive impairment (MCI) is a clinical precursor of AD....

A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biom...

Exploring the perceptions of people with dementia about the social robot PARO in a hospital setting.

New technology, such as social robots, opens up new opportunities in hospital settings. PARO, a robotic pet seal, was designed to provide emotional an...

Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be...

Comprehensive Exploration of Target-specific Ligands Using a Graph Convolution Neural Network.

Machine learning approaches are widely used to evaluate ligand activities of chemical compounds toward potential target proteins. Especially, explorat...

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's di...

Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches.

Aβ-amyloid deposition is a key feature of Alzheimer's disease, but Consortium to Establish a Registry for Alzheimer's Disease (CERAD) assessment, base...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are appl...

Discriminative margin-sensitive autoencoder for collective multi-view disease analysis.

Medical prediction is always collectively determined based on bioimages collected from different sources or various clinical characterizations describ...

How people with dementia perceive a therapeutic robot called PARO in relation to their pain and mood: A qualitative study.

BACKGROUND: Interacting with social robots, such as the robotic seal PARO, has been shown to improve mood and acute pain for people with dementia. Lit...

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches.

BACKGROUND: Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ul...

Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been rep...

Synthetic review of financial capacity in cognitive disorders: Foundations, interventions, and innovations.

PURPOSE OF REVIEW: Financial capacity (FC) is a complex, multi-dimensional construct that changes over the lifespan and commonly becomes impaired as i...