Alzheimer's Disease

Real Time Hand Movement Trajectory Tracking for Enhancing Dementia Screening in Ageing Deaf Signers of British Sign Language.

Real time hand movement trajectory tracking based on machine learning approaches may assist the early identification of dementia in ageing Deaf individuals who are users of British Sign Language (BSL), since there are few clinicians with appropriate communication skills, and a shortage of sign language interpreters. Unlike other computer vision systems used in dementia stage assessment such as RGB...

Toward an interpretable Alzheimer's disease diagnostic model with regional abnormality representation via deep learning.

In this paper, we propose a novel method for magnetic resonance imaging based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches into a unified framework. Specifically, we parcellate the brain into predefined regions based on anatomical knowledge (i.e., templates) and derive complex nonlinear re...

Toward a Better Estimation of Functional Brain Network for Mild Cognitive Impairment Identification: A Transfer Learning View.

Mild cognitive impairment (MCI) is an intermediate stage of brain cognitive decline, associated with increasing risk of developing Alzheimer's disease...

Automatic extraction and assessment of lifestyle exposures for Alzheimer's disease using natural language processing.

INTRODUCTION: Previous biomedical studies identified many lifestyle exposures that could possibly represent risk factors for dementia in general or de...

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts.

About 29.8 million people worldwide had been diagnosed with Alzheimer's disease (AD) in 2015, and the number is projected to triple by 2050. In 2018, ...

Characterizing Alzheimer's Disease With Image and Genetic Biomarkers Using Supervised Topic Models.

Neuroimaging and genetic biomarkers have been widely studied from discriminative perspectives towards Alzheimer's disease (AD) classification, since n...

Telepresence robots: Encouraging interactive communication between family carers and people with dementia.

OBJECTIVE: The aim of the study was to explore the feasibility of using telepresence robots to encourage interactive communication in dementia care, f...

Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.

While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is sti...

Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks.

Accurate prediction of disease trajectories is critical for early identification and timely treatment of patients at risk. Conventional methods in sur...

Deep Learning Algorithms for Corneal Amyloid Deposition Quantitation in Familial Amyloidosis.

OBJECTIVES: The aim of this study was to train and validate deep learning algorithms to quantitate relative amyloid deposition (RAD; mean amyloid depo...

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment.

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be ...

Assessment of lipid peroxidation and artificial neural network models in early Alzheimer Disease diagnosis.

OBJECTIVE: Lipid peroxidation constitutes a molecular mechanism involved in early Alzheimer Disease (AD) stages, and artificial neural network (ANN) a...

The Model of Aging Acceleration Network Reveals the Correlation of Alzheimer's Disease and Aging at System Level.

As the incidence of senile dementia continues to increase, researches on Alzheimer's disease (AD) have become more and more important. Several studies...

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records.

BACKGROUND: Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces oppor...

Application of artificial neural network model in diagnosis of Alzheimer's disease.

BACKGROUND: Alzheimer's disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish...

Identifying incident dementia by applying machine learning to a very large administrative claims dataset.

Alzheimer's disease and related dementias (ADRD) are highly prevalent conditions, and prior efforts to develop predictive models have relied on demogr...

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations.

Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease...

Incorporating Conversational Strategies in a Social Robot to Interact with People with Dementia.

BACKGROUND: Socially assistive robots (SARs) have the potential to assist nonpharmacological interventions based on verbal communication to support th...

Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies.

The differentiation of dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) using brain perfusion single photon emission tomography is import...

Diagnosis of Alzheimer's disease with Sobolev gradient-based optimization and 3D convolutional neural network.

Alzheimer's disease is a neuropsychiatric, progressive, also an irreversible disease. There is not an effective cure for the disease. However, early d...