GERD

Predicting semantic segmentation quality in laryngeal endoscopy images.

Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to jud...

A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.

In recent years, artificial intelligence (AI) has been widely explored to enhance capsule endoscopy (CE), with the goal of improving the efficiency of the reading process. While most AI models have been developed for small bowel and colon analysis, the development of esophagogastric (E-G) models has been limited due to the scarcity of frames captured during the procedure, making it challenging to ...

Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, environmental factors, and lifestyle choices. With t...

A novel artificial intelligence-based system for quality monitoring during esophagogastroduodenoscopy: a multicenter randomized controlled study.

Esophagogastroduodenoscopy (EGD) is the pivotal procedure for diagnosis of upper gastrointestinal (UGI) lesions. However, significant variation in EGD...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 a...

Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning.

Early detection of lung nodules (LNs) is critical for prevention and treatment of lung cancer. However, current noninvasive diagnostic methods face si...

Identification of CXCR4 as a potential preventive gene in clear cell renal cell carcinoma from machine learning and immune analysis.

Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant kidney tumor characterized by high metastatic potential and recurrence rates....

Artificial Intelligence in Gastrointestinal Endoscopy: The Japan Gastroenterological Endoscopy Society Position Statements.

Research and development of artificial intelligence (AI) in the field of gastrointestinal endoscopy is progressing rapidly. In Japan alone, there are ...

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 gastrointestinal tract resulting from an inappropriate immune re...

Artificial Intelligence-assisted Endoscopy and Examiner Confidence: A Study on Human-Artificial Intelligence Interaction in Barrett's Esophagus (With Video).

OBJECTIVE: Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall sho...

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 the early detection of laryngopharyngeal lesions. ...

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

Deep learning, a cornerstone of artificial intelligence, is driving rapid advancements in computational biology. Protein-protein interactions (PPIs) a...

Computer-aided detection for esophageal achalasia (with video).

OBJECTIVES: Achalasia is an esophageal motility disorder that impairs quality of life and is often missed (20-50%) on endoscopy. A newly developed com...

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 image processing. Yet, the plethora of sources fo...

Tracheal Targeted Nanogrid Delivery Systems of Dexamethasone Visualized by Single-Particle Tracing and Multiscale Pathological Mapping.

Current clinical treatment of pulmonary diseases requires an advanced three-dimensional (3D) pathological atlas of the microenvironment, particularly ...

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 deeper insights into its molecular mechanisms for im...

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 immunomodulation plays a pivotal role. However, immuno-tra...

Exploration of the potential therapeutic effects and targets of Coriandrum sativum on non-erosive esophagitis based on bioinformatics and molecular dynamics simulation.

Gastroesophageal reflux disease (GERD) is one of the most frequently diagnosed gastrointestinal disorders, adversely affecting quality of life. Corian...

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. Computationally mining PPI can help us better understand ce...

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

BACKGROUND: Artificial intelligence (AI) has revolutionized the field of gastroenterology, particularly in endoscopic and colonoscopic procedures. The...