Dementia

Detection of dementia on voice recordings using deep learning: a Framingham Heart Study.

BACKGROUND: Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data are not readily available.

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestima...

Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.

Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting sem...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the le...

Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.

OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to ac...

Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study.

Normative modelling is an emerging method for quantifying how individuals deviate from the healthy populational pattern. Several machine learning mode...

The Humanoid Robot Sil-Bot in a Cognitive Training Program for Community-Dwelling Elderly People with Mild Cognitive Impairment during the COVID-19 Pandemic: A Randomized Controlled Trial.

BACKGROUND: Mild cognitive impairment (MCI) is a stage preceding dementia, and early intervention is critical. This study investigated whether multi-d...

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disea...

Neuroinflammation and Alzheimer's Disease: A Machine Learning Approach to CSF Proteomics.

In Alzheimer's disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although...

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion.

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their ...

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of ...

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment.

OBJECTIVES: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain gen...

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain...

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive...

Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signatur...

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and diffi...

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairme...

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the est...

Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal car...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review.

BACKGROUND: Psychosocial issues, such as social isolation and loneliness among older adults and people with dementia, continue to pose challenges with...