Gastroenterology

Peptic Ulcer Disease

Latest AI and machine learning research in peptic ulcer disease for healthcare professionals.

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Showing 64-84 of 3,581 articles
Artificial Intelligence in Hemophilia Management: Revolutionizing Patient Care and Future Directions.

Recent advancements in artificial intelligence (AI) hold significant promise for transforming hemoph...

Biomarker risk stratification with capsule sponge in the surveillance of Barrett's oesophagus: prospective evaluation of UK real-world implementation.

BACKGROUND: Endoscopic surveillance is the clinical standard for Barrett's oesophagus, but its effec...

Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction.

OBJECTIVE: IgA nephropathy (IgAN) is the most common form of glomerulonephritis worldwide, character...

The impact of artificial intelligence on the endoscopic assessment of inflammatory bowel disease-related neoplasia.

Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestin...

A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.

BACKGROUND: Endoscopy is essential in upper aerodigestive tract (UADT) examination, particularly in ...

Recent advances in deep learning for protein-protein interaction: a review.

Deep learning, a cornerstone of artificial intelligence, is driving rapid advancements in computatio...

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased...

The 2024 ESC guidelines on atrial fibrillation: essential updates for everyday clinical practice.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it is associated with substan...

Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.

INTRODUCTION: Successful application of artificial intelligence (AI) in endoscopy requires effective...

Towards large nuclear imaging system optical simulations with optiGAN, a generative adversarial network.

Optical Monte Carlo (MC) simulations are essential for modeling light transport in radiation detecto...

Identification of key proteins and pathways in myocardial infarction using machine learning approaches.

Acute myocardial infarction (AMI) is a leading cause of global morbidity and mortality, requiring de...

Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent inflammatory disease where immunomo...

PCLT-PPI: Predicting Multi-type Interactions between Proteins based on Point Cloud Structure and Local Topology Preservation.

Protein-protein interactions (PPIs) play a crucial role in cellular biochemical reactions. Computati...

Artificial intelligence in endoscopy and colonoscopy: a comprehensive bibliometric analysis of global research trends.

BACKGROUND: Artificial intelligence (AI) has revolutionized the field of gastroenterology, particula...

Predicting the Compressive Properties of Carbon Foam Using Artificial Neural Networks.

This article focusses on predicting the compressive properties of polyurethane-derived carbon foam u...

AI-assisted multi-OMICS analysis reveals new markers for the prediction of AD.

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder, characterized by progress...

Machine learning to predict de novo protein-protein interactions.

Advances in machine learning for structural biology have dramatically enhanced our capacity to predi...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked diffe...

Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.

In order to address the challenges posed by the large number of images acquired during wireless caps...

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