Peptic Ulcer Disease

Mapping the scientific landscape of robotic hernia repair: a bibliometric and topic modeling analysis of thematic transitions.

Robotic hernia repair (RHR) has profoundly transformed modern surgery, yet it remains intensely debated. To understand this dynamic field, bibliometric analysis offers a powerful tool to evaluate its evolving research trends. Our study presents a comprehensive bibliometric and machine learning analysis of 1,503 publications from 2003 to 2025. The literature demonstrates a growth trend (R2 = 0.96),...

Miniaturization in robotic surgery: a bibliometric and science mapping analysis of technological evolution and clinical translation (1996-2026).

Miniaturization has emerged as a major technological trajectory in robotic surgery, encompassing single-port systems, flexible endoscopic platforms, capsule robotics, and microrobots designed to reduce surgical trauma and improve procedural precision. Despite rapid growth in this area, no previous bibliometric study has comprehensively mapped miniaturization as an integrated technological and clin...

Preoperative and intraoperative localization of small pulmonary nodules for thoracoscopic surgery: a contemporary narrative review.

For thoracoscopic resection to be successful, accurate preoperative and intraoperative localization is necessary due to the growing detection of small...

Psychological adaptation to scaffolded generative AI use in higher education: SEM and thematic evidence from a private university's new media marketing course.

This study examines how students psychologically adapt to scaffolded generative artificial intelligence (GenAI) use in higher education. Drawing on th...

Machine learning model for predicting rebleeding risk after endoscopic variceal ligation in esophageal variceal bleeding.

Rebleeding is a severe complication following recovery from esophageal variceal bleeding (EVB), yet robust predictive tools for assessing post-treatme...

Large-scale transcriptomic data mining using explainable XGBoost and SHAP reveals shared biomarkers and molecular mechanisms between type-2 diabetes and triple-negative breast cancer for drug repurposing.

Type 2 diabetes (T2D) is considered as a risk factor of triple-negative breast cancer (TNBC). So, there is a significant chance of their co-existence....

Automated extraction of colonoscopy quality indicators from free-text reports using a prompt-based large language model pipeline.

BACKGROUND AND AIMS: Artificial intelligence has increasingly enabled large-scale analysis of clinical documentation, offering new opportunities to im...

Integrated bioinformatics and machine learning for shared diagnostic genes and mechanisms between periodontitis and psoriasis.

BACKGROUND: Periodontitis and psoriasis are two prevalent conditions that are bidirectionally associated. However, the molecular basis remains poorly ...

Artificial Intelligence-Based Risk Prediction Models for Complications After Tongue Cancer Surgery.

IMPORTANCE: Glossectomy and reconstruction for tongue tumors carries substantial risk of postoperative morbidity, yet current tools offer limited indi...

Adaptive distribution-aware transformer for multi-scale visual representation learning on imbalanced and low-resolution data.

Deep learning models often struggle with class imbalance and low-resolution medical images, where critical spatial details and minority-class features...

Integrating machine learning and pathway modelling to explore factors associated with chronic post-surgical pain and quality of life: a secondary observational analysis of the ENIGMA-II trial.

BACKGROUND: Chronic post-surgical pain (CPSP) is a common long-term complication with multifactorial contributors, and improved risk stratification re...

Automated diagnosis of Helicobacter pylori infection from routine endoscopic images using deep learning: a development and validation study.

BACKGROUND: Real-time endoscopic diagnosis of Helicobacter pylori infection remains challenging and often requires biopsy-based testing, delaying trea...

Applying machine learning to pharmacovigilance data: A proof-of-concept study.

AIM: Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance d...

Predicting PPOI secondary to laparoscopic radical resection of rectal cancer combined with lobaplatin hyperthermic intraperitoneal chemotherapy: a multi-center study.

PURPOSE: PPOI is one of the common complications of intraperitoneal hyperthermic chemotherapy during laparoscopic radical resection of rectal cancer, ...

Innovation and commercialization pathway in gastrointestinal endoscopy: a practical primer from American Society for Gastrointestinal Endoscopy.

Gastrointestinal endoscopy has undergone rapid technological evolution, yet many clinician innovators remain unfamiliar with the pathways required to ...

Surface-enhanced Raman spectroscopy and machine learning based profiling of β-lactam antibiotic-induced biochemical responses in methicillin-resistant and methicillin-susceptible Staphylococcus aureus strains.

The global escalation of antimicrobial resistance in Staphylococcus aureus (S. aureus) necessitates rapid analytical tools to reliably distinguish bet...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

Global performance of predictive models for dengue severity, hospitalization and mortality: a systematic review and meta-analysis of 146 studies.

OBJECTIVES: Predictive models are increasingly used to support the clinical management of dengue, but their performance varies widely across settings....

Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.

Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision-languag...

GatedGeoGO:Multi-Modal Geometry-Aware Network with Gated Fusion and GO Semantic Attention for Protein Function Prediction.

Proteins play essential roles in diverse biological processes, and accurate function annotation is fundamental for understanding cellular mechanisms a...