Alzheimer's Disease

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

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-performance AD detection chips. Moreover, excessively complex neural networks are not conducive to on-chip implementation and clinical applications. This study ...

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

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 Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost ov...

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

Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Diseas...

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan.

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air po...

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

BACKGROUND: Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging...

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

BACKGROUND:  The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with co...

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

INTRODUCTION: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race...

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

OBJECTIVE: The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims...

Significance of plasma p-tau217 in predicting long-term dementia risk in older community residents: Insights from machine learning approaches.

INTRODUCTION: Whether plasma biomarkers play roles in predicting incident dementia among the general population is worth exploring.

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

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activat...

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.

BACKGROUND: With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and ...

Ensemble Deep Random Vector Functional Link Network Using Privileged Information for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have been proposed for the diagnosis of AD at early stage. Recently,...

A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.

Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that d...

Graph Embedded Ensemble Deep Randomized Network for Diagnosis of Alzheimer's Disease.

Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural ...

A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD wors...

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study.

BACKGROUND: Prediction of mortality is very important for care planning in hospitalized patients with dementia and artificial intelligence has the pot...

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expressio...

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippoca...

A novel graph neural network method for Alzheimer's disease classification.

Alzheimer's disease (AD) is a chronic neurodegenerative disease. Early diagnosis are very important to timely treatment and delay the progression of t...

Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.

Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychologica...