Alzheimer's Disease

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer's Disease neuropathology), which leverages an unexpected synerg...

Deep learning-based model for diagnosing Alzheimer's disease and tauopathies.

AIMS: This study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau-immunostained digital slide images.

Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain....

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 ...

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)...

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...

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pne...

Predicting mutant outcome by combining deep mutational scanning and machine learning.

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experi...

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...

Generation of synthetic PET images of synaptic density and amyloid from F-FDG images using deep learning.

PURPOSE: Positron emission tomography (PET) imaging with various tracers is increasingly used in Alzheimer's disease (AD) studies. However, access to ...

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 ...

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine ...

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...