Geriatrics

Alzheimer's Disease

Latest AI and machine learning research in alzheimer's disease for healthcare professionals.

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Geriatrics Subcategories: Alzheimer's Disease Medicare
Showing 1198-1218 of 11,687 articles
A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease.

The disorder distribution of protein in the compartment or organelle leads to many human diseases, i...

Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia.

Dementia is a neurological and cognitive condition that affects millions of people around the world....

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.

Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD...

Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. E...

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome.

Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) ...

Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.

Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of ne...

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks.

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's ...

Antemortem CSF A42/A40 ratio predicts Alzheimer's disease pathology better than A42 in rapidly progressive dementias.

OBJECTIVE: Despite the critical importance of pathologically confirmed samples for biomarker validat...

Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic qualit...

Correlation-Aware Sparse and Low-Rank Constrained Multi-Task Learning for Longitudinal Analysis of Alzheimer's Disease.

Alzheimer's disease (AD), as a severe neurodegenerative disease, is now attracting more and more res...

Pet robot intervention for people with dementia: A systematic review and meta-analysis of randomized controlled trials.

This study aims to systematically evaluate the efficacy of Pet robot intervention (PRI) for people w...

Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification.

We develop three efficient approaches for generating visual explanations from 3D convolutional neura...

Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes.

Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in ...

Machine learning studies on major brain diseases: 5-year trends of 2014-2018.

In the recent 5 years (2014-2018), there has been growing interest in the use of machine learning (M...

Using telepresence for social connection: views of older people with dementia, families, and health professionals from a mixed methods pilot study.

To explore the acceptability of telepresence robots in dementia care from the perspectives of peopl...

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-FDG PET of the Brain.

Purpose To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzhe...

Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data fo...

Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors.

Machine learning and data mining approaches are being successfully applied to different fields of li...

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI.

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small v...

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