Alzheimer's Disease

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Unfortunately, current diagnostic techniques are often invasive and expensive. Our research focuses on creating a cost-effective and non-invasive method for the early detection of cognitive decline. METHODS: Using a publicly available dataset of restin...

Integrating standard and native spaces for radiomics and brain network analysis in Alzheimer's disease diagnosis and prognosis.

INTRODUCTION: Structural MRI analysis for Alzheimer's disease (AD) is limited by balancing group-level comparability in standard space with anatomical fidelity in native space. We therefore propose a multi-space, hybrid-feature framework, integrating radiomics and network metrics from both spaces to classify AD and predict mild cognitive impairment (MCI) progression. METHODS: An integrated dual-sp...

CGLK-GNN : A connectome generation network with large kernels for GNN based Alzheimer's disease analysis.

Alzheimer's disease (AD) is a currently incurable neurodegenerative disease, with early detection representing a high research priority. AD is charact...

AI-driven biomarker learning for the early diagnosis of neurodegenerative diseases: ABLEDx.

BACKGROUND: Tears are an easily accessible biofluid that reflects both emotional states and disease conditions. They are particularly enriched in extr...

An improved activation function for the recognition of knee osteoarthritis severity.

BACKGROUND: Existing knee osteoarthritis (KOA) severity classification methods typically rely on a combination of object detection algorithms and clas...

Probabilistic mapping and automated segmentation of human brainstem white matter bundles.

Brainstem white matter (WM) bundles are essential conduits for neural signals that modulate homeostasis and consciousness. Their architecture forms th...

An interpretable machine learning framework with data-informed imaging biomarkers for diagnosis and prediction of Alzheimer's disease.

Machine learning methods based on imaging and other clinical data have shown great potential for improving the early and accurate diagnosis of Alzheim...

LEFF-ViT: A locally enhanced vision transformer framework for accurate Alzheimer's Disease classification from brain MRI.

Early and accurate diagnosis of Alzheimer's Disease (AD) is critical for effective disease management and progression delay. Researches have been done...

Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficien...

Implementation of artificial intelligence in palliative and supportive care for people with cancer: A scoping review.

BACKGROUND: Cancer remains a leading global health burden. Artificial intelligence offers new opportunities to address complex physical and psychologi...

Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.

Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline, with no known cure. Early detectio...

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 cort...

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...

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...