Peptic Ulcer Disease

LightGastroFormer: a lightweight multi-resolution transformer for gastrointestinal disease classification.

In order to promote early diagnosis and lessen the workload of doctors during endoscopic and capsule endoscopy tests, automated analysis of gastrointestinal (GI) images is essential. However, current deep learning techniques frequently fail to simultaneously capture long-range contextual information and fine-grained local patterns, especially in large-scale and highly imbalanced datasets. We sugge...

Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.

Artificial intelligence (AI) is rapidly transforming healthcare, supporting disease management and enabling outcome prediction across multiple clinical settings. Inflammatory bowel diseases (IBD) are complex, heterogeneous conditions whose assessment relies on integrating several modalities, including endoscopy, histology, cross-sectional imaging, and omics data, all of which are critical for eval...

A histopathologically verified dataset of magnifying narrow-band imaging endoscopy for classifying the gastric precancerous cascade.

Gastric cancer is a leading cause of mortality worldwide, yet the development of computer-aided diagnosis (CAD) systems for its early detection is hin...

A novel deep learning network for small bowel ulcerative lesion detection and differential diagnosis on double-balloon endoscopy images.

Differentiating small bowel ulcerative diseases (SBUDs) on double-balloon endoscopy (DBE) is challenging. We aimed to develop an artificial intelligen...

Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.

BACKGROUND: Gastric intestinal metaplasia (GIM) is often visually inconspicuous on routine endoscopy, while many artificial intelligence systems rely ...

Development, validation, and user-centric evaluation of an interpretable machine learning decision support tool for the preoperative prediction of mild bleeding disorders (MBD-Check): a prospective diagnostic prediction study.

BACKGROUND: Mild bleeding disorders are the most common inherited bleeding disorders, often leading to perioperative haemorrhages. Preoperative screen...

EXPRESS: Transcriptome profiling and experimental validation identify FOS, RAC2, and TYROBP as potential biomarkers for Fu's subcutaneous needling in neuropathic pain treatment.

BACKGROUND: This study aimed to explore potential biomarkers and mechanisms underlying in the treatment of neuropathic pain(NP) with Fu's subcutaneous...

Single-cell-based analysis establishes C1QA-mediated promotion of high glucose-induced tubular epithelial injury via ERS in DN.

BACKGROUND: Diabetic nephropathy (DN) poses a growing worldwide health challenge as a leading cause of end-stage renal disease, a condition that arise...

Learning and deskilling effects of artificial intelligence in colonoscopy among endoscopists with different levels of experience: a pragmatic, prospective trial.

BACKGROUND: The effect of computer-aided detection (CADe) on performance of endoscopists with different experience levels is not well understood. This...

Integrating machine learning and WGCNA to identify core candidate genes and decipher immune-related molecular networks in TcdA/TcdB-associated CDI.

Clostridioides difficile infection (CDI) remains a leading cause of healthcare-associated diarrhea and is characterized by high recurrence rates and i...

Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by complex molecular alterations across multiple brain regions. ...

ProtAttn-QuadNet: An attention-based deep learning framework for protein-protein interaction prediction using ProtBERT embeddings.

Protein-protein interactions (PPIs) form the backbone of most cellular processes, governing signal transduction, gene regulation, and metabolic contro...

Development and validation of a machine learning model predicting post-tonsillectomy hemorrhage.

PURPOSE: To develop and validate machine learning models to predict post-tonsillectomy hemorrhage. METHODS: This was a machine learning analysis of a ...

Designing the Future of Hemostasis.

Bleeding disorders arising from dysfunctional platelet-protein interactions pose a significant clinical challenge due to their heterogeneity and compl...

LLM Chatbot Integration With Inventory Systems for Gastroenterology and Endoscopy Services: A Systematic Review.

BACKGROUND: Generally, gastroenterology and digestive endoscopy units commonly face constraints. It may be due to a lack of equipment, poor scheduling...

Integrated Network Pharmacology, Molecular Docking, and Experimental Validation for Uncovering the Pharmacodynamic Basis of Sanghuang against Human Pulmonary Fibrosis.

INTRODUCTION/OBJECTIVE: Sanghuang, a traditional Chinese medicinal fungus, exhibits well-documented anti-inflammatory, antioxidant, and antitumor acti...

Quality of AI-generated post-operative patient education after minimally invasive gynecologic surgery: A comparative analysis.

STUDY OBJECTIVE: To compare the quality of AI-generated responses to gynecologic post-operative questions with educational materials published by prof...

Techniques and outcomes review of the endoscopic management of gastrointestinal bleeding.

INTRODUCTION: Gastrointestinal (GI) bleeding is a frequent and potentially life-threatening emergency that imposes a substantial healthcare burden wor...

Common biomarkers of Alzheimer disease and postoperative delirium.

Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets r...

Machine Learning-Based Prediction of Treatment Success, Postoperative Pain, and Analgesic Use After Full Pulpotomy in Permanent Molars with Symptomatic Irreversible Pulpitis.

INTRODUCTION: This study aimed to develop and evaluate machine learning (ML) models for predicting treatment success, postoperative pain, and analgesi...