Neurology

Dementia

Latest AI and machine learning research in dementia for healthcare professionals.

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Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders.

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolv...

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria ...

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functionin...

A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such a...

Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors.

Human activity recognition has become an active research field over the past few years due to its wi...

Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles.

BACKGROUND AND OBJECTIVES: Clathrin is an adaptor protein that serves as the principal element of th...

Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort.

Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's di...

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies...

Engagement and experience of older people with socially assistive robots in home care.

Social isolation is one of the most common consequences of older people with dementia, especially fo...

Identifying Children With Clinical Language Disorder: An Application of Machine-Learning Classification.

In this study, we identified child- and family-level characteristics most strongly associated with c...

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection o...

Systems Pharmacological Approach to Investigate the Mechanism of for Application to Alzheimer's Disease.

(OC)-a traditional Chinese medicine (TCM)-has been reported to have large numbers of flavonoids, al...

Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia.

OBJECTIVES: The overall aim of the present study was to explore the role of cognitive reserve (CR) i...

Application of Artificial Neural Networks to Identify Alzheimer's Disease Using Cerebral Perfusion SPECT Data.

The aim of this study was to demonstrate the usefulness of artificial neural networks in Alzheimer d...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

BACKGROUND: In a clinical setting, an individual subject classification model rather than a group an...

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement.

PURPOSE: Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor progno...

Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD)....

Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced Deep Discriminant Learning.

Adequate medical images are often indispensable in contemporary deep learning-based medical imaging ...

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and g...

Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models.

The health and function of tissue rely on its vasculature network to provide reliable blood perfusio...

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