AI Medical Compendium Topic:
Alzheimer Disease

Clear Filters Showing 441 to 450 of 897 articles

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application ...

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease.

International journal of molecular sciences
BACKGROUND: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) lev...

StoCast: Stochastic Disease Forecasting With Progression Uncertainty.

IEEE journal of biomedical and health informatics
Forecasting patients' disease progressions with rich longitudinal clinical data has drawn much attention in recent years due to its impactful application in healthcare and the medical field. Researchers have tackled this problem by leveraging traditi...

Alzheimer's disease detection using depthwise separable convolutional neural networks.

Computer methods and programs in biomedicine
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However...

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Nature communications
Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication i...

A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification.

Magnetic resonance imaging
PURPOSE: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been widely used on analysis of neuroimage for quantitative evaluation and computer-aided diagnosis of AD or...

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer's Disease Early Discovery.

Sensors (Basel, Switzerland)
Alzheimer's disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect ...

A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.

BioMed research international
. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to ...

Task Similarity Estimation Through Adversarial Multitask Neural Network.

IEEE transactions on neural networks and learning systems
Multitask learning (MTL) aims at solving the related tasks simultaneously by exploiting shared knowledge to improve performance on individual tasks. Though numerous empirical results supported the notion that such shared knowledge among tasks plays a...