Alzheimer's Disease

Artificial intelligence (AI) for home support interventions in dementia: a scoping review protocol.

INTRODUCTION: Dementia has become one of the significant causes of disability and dependency among older people globally. The proportion of people with dementia who are cared for at home has soared. The rapid growth of technology and data has stimulated artificial intelligence (AI) in patients with dementia at home. However, there are still tremendous opportunities and challenges in applying AI to...

Predicting conversion to Alzheimer's disease in individuals with Mild Cognitive Impairment using clinically transferable features.

Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer's disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a ba...

A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss.

Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive a...

Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.

PURPOSE: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentatio...

Socially assistive robotics and older family caregivers of young adults with Intellectual and Developmental Disabilities (IDD): A pilot study exploring respite, acceptance, and usefulness.

INTRODUCTION: The need for caregiver respite is well-documented for the care of persons with IDD. Social Assistive Robotics (SAR) offer promise in add...

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.

Although current research aims to improve deep learning networks by applying knowledge about the healthy human brain and vice versa, the potential of ...

Multi-modality MRI for Alzheimer's disease detection using deep learning.

Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human bod...

The Impact of Engagement with the PARO Therapeutic Robot on the Psychological Benefits of Older Adults with Dementia.

OBJECTIVES: This study aimed to examine the effect of 8-weeks of a 60-minute PARO intervention to reduce depressive symptoms and loneliness in older a...

Public Discourse and Sentiment Toward Dementia on Chinese Social Media: Machine Learning Analysis of Weibo Posts.

BACKGROUND: Dementia is a global public health priority due to rapid growth of the aging population. As China has the world's largest population with ...

Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning.

In Alzheimer's disease, the molecular pathogenesis of the extracellular Aβ-amyloid (Aβ) instigation of intracellular tau accumulation is poorly unders...

Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection.

Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad ...

Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.

Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art ...

Identifying neurocognitive disorder using vector representation of free conversation.

In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies use...

Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.

Accurate prediction of clinical scores (of neuropsychological tests) based on noninvasive structural magnetic resonance imaging (MRI) helps understand...

Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over ...

Methodologies Used to Study the Feasibility, Usability, Efficacy, and Effectiveness of Social Robots For Elderly Adults: Scoping Review.

BACKGROUND: New research fields to design social robots for older people are emerging. By providing support with communication and social interaction,...

ScanNet: A Web Server for Structure-based Prediction of Protein Binding Sites with Geometric Deep Learning.

Predicting the various binding sites of a protein from its structure sheds light on its function and paves the way towards design of interaction inhib...

Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models.

The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human...

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level amon...

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.