Alzheimer's Disease

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detection and identification of CMBs in Magnetic Resonance (MR) images is a very challenging task due to their wide distribution throughout the brain, small s...

Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment.

Subjective cognitive decline (SCD) is a high-risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD ind...

Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing.

Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often evades detection in the early stages of the conditio...

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activi...

Formal caregivers' perceptions and experiences of using pet robots for persons living with dementia in long-term care: A meta-ethnography.

AIM: To explore the formal caregivers' perceptions and experiences of using pet robots for persons living with dementia residing in long-term care set...

Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting....

Effects on sleep from group activity with a robotic seal for nursing home residents with dementia: a cluster randomized controlled trial.

OBJECTIVES: Sleep disturbances are common in people with dementia and increase with the severity of the disease. Sleep disturbances are complex and ca...

The effect of a social robot intervention on sleep and motor activity of people living with dementia and chronic pain: A pilot randomized controlled trial.

OBJECTIVE: To investigate the effect of a social robot intervention on sleep and motor activity in nursing home residents living with dementia and chr...

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative diso...

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily a...

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia.

Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Sin...

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neur...

Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography.

BACKGROUND: Alzheimer's Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to ma...

Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network.

Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influ...

A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.

Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we h...

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning.

OBJECTIVE: Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervent...

Caregiver burden in stroke inpatients: a randomized study comparing robot-assisted gait training and conventional therapy.

The effects of caregiver burden during the inpatient rehabilitation period have not yet been investigated. The purpose of this study was to evaluate t...

Monitoring behavioral symptoms of dementia using activity trackers.

Tertiary disease prevention for dementia focuses on improving the quality of life of the patient. The quality of life of people with dementia (PwD) an...

VEPAD - Predicting the effect of variants associated with Alzheimer's disease using machine learning.

INTRODUCTION: Alzheimer's disease (AD) is a complex and heterogeneous disease that affects neuronal cells over time and it is prevalent among all neur...