Alzheimer's Disease

Erratum to "Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning".

[This corrects the article DOI: 10.1177/25424823241289804.].

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic r...

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks.

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and develop...

Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.

Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worl...

Explainable machine learning on clinical features to predict and differentiate Alzheimer's progression by sex: Toward a clinician-tailored web interface.

Alzheimer's disease (AD), the most common neurodegenerative disorder world-wide, presents sex-specific differences in its manifestation and progressio...

Cholinesterase Inhibitory Activity of Paeoniflorin: Molecular Dynamics Simulation, and In Vitro Mechanistic Investigation.

Alzheimer's disease (AD), a neurological disorder, is one of the major reasons for memory loss in the world. AD is characterized by a sequela of cogni...

Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine t...

MHAGuideNet: a 3D pre-trained guidance model for Alzheimer's Disease diagnosis using 2D multi-planar sMRI images.

BACKGROUND: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as ...

Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials.

Dementia probably due to Alzheimer's disease is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected...

Artificial Intelligence-Assisted Comparative Analysis of the Overlapping Molecular Pathophysiology of Alzheimer's Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia.

The overlapping molecular pathophysiology of Alzheimer's Disease (AD), Amyotrophic Lateral Sclerosis (ALS), and Frontotemporal Dementia (FTD) was anal...

DCA-Enhanced Alzheimer's detection with shearlet and deep learning integration.

Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quali...

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.

With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promisi...

A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.

BACKGROUND: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective interventi...

Multimodal Alzheimer's disease classification through ensemble deep random vector functional link neural network.

Alzheimer's disease (AD) is a condition with a complex pathogenesis, sometimes hereditary, characterized by the loss of neurons and synapses, along wi...

Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.

OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerative diseases characterized by altered brain function...

Early detection of Alzheimer's disease in structural and functional MRI.

OBJECTIVES: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocamp...

Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms.

Alzheimer's disease (AD) is a serious neurodegenerative disorder that causes incurable and irreversible neuronal loss and synaptic dysfunction. The pr...

ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images.

One of the most familiar types of disease is Alzheimer's disease (AD) and it mainly impacts people over the age limit of 60. AD causes irreversible br...

Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.

BACKGROUND: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.

Natural compounds for Alzheimer's prevention and treatment: Integrating SELFormer-based computational screening with experimental validation.

BACKGROUND: This study aimed to develop and apply a novel computational pipeline combining SELFormer, a transformer architecture-based chemical langua...