Alzheimer's Disease

miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach.

Alzheimer's disease (AD) is the most common type and accounts for 60%-70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non-coding RNAs that play a crucial role in gene expression regulation. Although the diagnosis of AD is primarily clinical, several miRNAs have been associated with AD and considered as potential markers for diagnosis and progression of AD. We sought to match AD...

Robotic Care Equipment Improves Communication between Care Recipient and Caregiver in a Nursing Home as Revealed by Gaze Analysis: A Case Study.

The use of robotic nursing care equipment is an important option for solving the shortage of nursing care personnel, but the effects of its introduction have not been fully quantified. Hence, we aimed to verify that face-to-face care is still provided by caregivers in transfer situations when using robotic nursing care equipment. This study was conducted at a nursing home where the bed-release ass...

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.

Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeu...

Predictive deep learning models for cognitive risk using accessible data.

The early detection of mild cognitive impairment (MCI) is crucial to preventing the progression of dementia. However, it necessitates that patients vo...

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological...

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to var...

Intelligent decision support systems for dementia care: A scoping review.

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and ma...

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic o...

Identifying Functional Status Impairment in People Living With Dementia Through Natural Language Processing of Clinical Documents: Cross-Sectional Study.

BACKGROUND: Assessment of activities of daily living (ADLs) and instrumental ADLs (iADLs) is key to determining the severity of dementia and care need...

Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease.

Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicate...

Prediction of dementia based on older adults' sleep disturbances using machine learning.

BACKGROUND: The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progress...

Artificial intelligence for drug discovery and development in Alzheimer's disease.

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strateg...

The Intersections of COVID-19, HIV, and Race/Ethnicity: Machine Learning Methods to Identify and Model Risk Factors for Severe COVID-19 in a Large U.S. National Dataset.

We investigate risk factors for severe COVID-19 in persons living with HIV (PWH), including among racialized PWH, using the U.S. population-sampled Na...

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities.

IMPORTANCE: Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-dire...

Naturally occurring caffeic acid phenethyl ester from chestnut honey-based propolis and virtual screening towards DYRK1A.

Neurodegenerative diseases (NDDs) are disorders with dysfunction and ongoing loss of neurons, glial cells and the neural networks in the brain and spi...

Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.

BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopat...

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple ...

The coherence between PSMC6 and α-ring in the 26S proteasome is associated with Alzheimer's disease.

Alzheimer's disease (AD) is a heterogeneous age-dependent neurodegenerative disorder. Its hallmarks involve abnormal proteostasis, which triggers prot...

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection.

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work ...

Predictive power of gait and gait-related cognitive measures in amnestic mild cognitive impairment: a machine learning analysis.

INTRODUCTION: Gait disorders and gait-related cognitive tests were recently linked to future Alzheimer's Disease (AD) dementia diagnosis in amnestic M...