Alzheimer's Disease

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

BACKGROUND: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information ...

Social Robotics and Dementia: Results from the eWare Project in Supporting Older People and Their Informal Caregivers.

The aim of this paper is to describe the results collected with the Italian study conducted within eWare project, aimed at supporting the autonomy and health of the older people affected by dementia and their informal caregivers, through the use of an innovative system based on a social robot and a sensorized environmental infrastructure. Nine dyads of older participants with their caregivers were...

A Care Robot with Ethical Sensing System for Older Adults at Home.

Many studies have explored emotional and mental services that robots can provide for older adults, such as offering them daily conversation, news, mus...

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study.

BACKGROUND: There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typicall...

Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer's disease (AD), there remains a clear clinical need for rel...

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks.

Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that...

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.

BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predic...

Interpretable deep learning of myelin histopathology in age-related cognitive impairment.

Age-related cognitive impairment is multifactorial, with numerous underlying and frequently co-morbid pathological correlates. Amyloid beta (Aβ) plays...

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 wit...

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...

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 ...