Alzheimer's Disease

PRDX6 as a Ferroptosis-Related Hub Gene in the Entorhinal Cortex of Alzheimer's Disease: A Multidimensional Bioinformatics and Experimental Validation Study.

Ferroptosis, an iron-dependent regulated cell death form, is a key pathogenic mechanism in Alzheimer's disease (AD), especially in the entorhinal cortex, a brain region selectively vulnerable to early AD neuropathology. This study aimed to identify peroxiredoxin 6 (PRDX6) as a novel ferroptosis-related hub gene in the entorhinal cortex and validate its diagnostic and therapeutic potential in AD. G...

Applying a statistical model-based AI method to identify prognostic factors for long-term cognitive decline in Alzheimer's disease: Evidence from pooled placebo data of four phase III trials.

BACKGROUND: Heterogeneity in the long-term progression of Alzheimer's disease (AD) challenges the efficiency of clinical trials. Identifying long-term prognostic factors is critical for enhancing trial efficiency, although it has been limited by the lack of appropriate statistical approaches. We applied a recently developed statistical model-based AI method to identify the baseline prognostic fact...

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 nature, biological heterogeneity, and limited the...

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 of MRI surveillance for amyloid-related imaging abn...

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 assessing brain structure and identifying subtle morp...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential that predictive models provide not only accurate ...

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 learning algorithms to predict the risk of dementia...

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 neurological disorder. For the longevity of people, a com...

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 core network of brain regions involved in cogniti...

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 countries, including Ethiopia, remain unvaccinated, pre...

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 routine care, particularly in home health care (HHC),...

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 interpretability of artificial intelligence (AI) ...

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 cognitive function, particularly among older adults. Ea...

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 'black box' models that can be difficult to trust. Expl...

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 and are established biomarkers in cerebrospinal flu...

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 artificial intelligence (AI) tools, DALL-E 3 and Midjour...

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 diseases. Effective wound care is often hindered b...

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 Alzheimer's disease (AD) with high accuracy. These models ...

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 determining amyloid beta (Aβ) pathology positivity/n...