Alzheimer's Disease

Artificial intelligence and machine learning in neurodegenerative disease management: A 21st century paradigm.

Neurodegenerative diseases represent a major and growing clinical challenge due to their progressive...

Artificial Intelligence-Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls.

The advent of anti-amyloid therapies (AATs) for Alzheimer disease (AD) has elevated the importance o...

DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and registration methods.

Voxel-based morphometry (VBM) using T1-weighted magnetic resonance imaging is a pivotal tool for ass...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential...

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine l...

MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurolo...

Amyloid-β and phosphorylated-tau accumulation in retrosplenial cortex of young Alzheimer's disease model: Impact on network activity.

BackgroundThe retrosplenial cortex (RSC) is a cortical area that functions as a key component of the...

Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.

BACKGROUND: Despite progress in childhood vaccination, many children in low- and middle-income count...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in rou...

Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and...

Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.

AI-driven framework for accurate detection of Alzheimer's disease in EEG.

Alzheimer's Disease (AD) is a rapidly growing neurodegenerative disorder that severely impairs cogni...

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.

Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'blac...

Convolutional neural network-derived neurofibrillary tangle classifiers: Investigative tools to identify maturation levels and explore post-translational modifications using laser capture microdissection coupled mass spectrometry.

Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression a...

Biases in an artificial intelligence image-generator's depictions of healthy aging and Alzheimer's.

OBJECTIVE: This content analysis study investigates potential biases in image generation by 2 artifi...

Artificial Intelligence-Assisted Clinical Decision Support System in Telemedical Wound Care: A Randomized Controlled Trial.

BACKGROUND: Chronic wounds are increasingly prevalent due to an aging population and rising chronic ...

Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.

PURPOSE: Functional magnetic resonance imaging (fMRI) and deep learning models can classify Alzheime...

Targeted blood proteome profiling using NULISAseq identifies a high-performance biomarker panel for Aβ pathology quantification and staging.

INTRODUCTION: While current blood-based biomarkers for Alzheimer's disease (AD) are effective for de...

Automatic lymphedema segmentation in T2-STIR MRI using an unsupervised clustering method.

PURPOSE: To develop and evaluate an unsupervised artificial intelligence (AI)-based method for the a...

Towards a speech-based digital biomarker for cognitive impairment: speech as a proxy for cognitive assessment.

With the growing prevalence of cognitive decline in ageing populations, accessible and scalable scre...

Multi-omics mapping identifies CYBA-mediated mitochondrial dysfunction driving macrophage polarization and ferroptosis via Nrf2 pathway in atherosclerosis.

Atherosclerosis (AS), a chronic inflammatory process driven largely by macrophage-mediated plaque fo...