Alzheimer's Disease

Monitoring Amyloidogenesis with a 3D Deep-Learning-Guided Biolaser Imaging Array.

Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet microlaser to monitor the amyloidogenesis process and evaluate the efficacy of anti-amyloid drugs. The lasing wavelength changes accordingly with the...

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared to univariate analysis. However, there exist serious differences among most studies analyzing the same dataset for the reasons arising from the methods...

Multi-stage classification of Alzheimer's disease from F-FDG-PET images using deep learning techniques.

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of ...

Deep learning signature of brain [F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder.

An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study ...

Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning.

Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine...

Strategies to Implement Pet Robots in Long-Term Care Facilities for Dementia Care: A Modified Delphi Study.

OBJECTIVES: Pet robots are technology-based substitutes for live animals that have demonstrated psychosocial benefits for people living with dementia ...

Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is generally prevalent in elderly people with s...

NeuroConstruct: 3D Reconstruction and Visualization of Neurites in Optical Microscopy Brain Images.

We introduce NeuroConstruct, a novel end-to-end application for the segmentation, registration, and visualization of brain volumes imaged using wide-f...

Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study.

The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging ...

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects.

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as de...

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.

Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not per...

Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs.

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based...

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, mai...

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...

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...