AIMC Topic: Alzheimer Disease

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Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

IEEE transactions on medical imaging
Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recentl...

Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.

Annals of biomedical engineering
PURPOSE: To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI.

Regression convolutional neural network models implicate peripheral immune regulatory variants in the predisposition to Alzheimer's disease.

PLoS computational biology
Alzheimer's disease (AD) involves aggregation of amyloid β and tau, neuron loss, cognitive decline, and neuroinflammatory responses. Both resident microglia and peripheral immune cells have been associated with the immune component of AD. However, th...

An efficient ANN SoC for detecting Alzheimer's disease based on recurrent computing.

Computers in biology and medicine
Alzheimer's Disease (AD) is an irreversible, degenerative condition that, while incurable, can have its progression slowed or impeded. While there are numerous methods utilizing neural networks for AD detection, there is a scarcity of High-performanc...

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

Journal of neuroscience methods
BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imagi...

Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Acta neuropathologica communications
Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Disease (AD). This multidisciplinary study addresses the current limitations on neuropathology by leveragi...

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

Arquivos de neuro-psiquiatria
BACKGROUND:  The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep...

Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development.

Incremental Value of Multidomain Risk Factors for Dementia Prediction: A Machine Learning Approach.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
OBJECTIVE: The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk pred...

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis.

Medical image analysis
Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of fun...