Gastroenterology

Using machine learning to identify the most important predictors of fatty liver index in healthy young Taiwanese men.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. While many factors have been associated with NAFLD, their relative importance in healthy young populations remains unclear. In this study, we enrolled 7,037 healthy young Taiwanese men aged 20-50 years and applied five machine learning (Mach-L) methods to identify the most important predictors of the fatty...

Patient-Specific Constitutive Models Based on Biaxial and Microstructural Characterisation of Fresh Human Gastric Tissue.

PURPOSE: As obesity has reached pandemic proportions worldwide, improving technical solutions for its treatment requires robust planning and numerical modelling. Yet existing material models obtained from biaxial tensile test are scarce and based only on frozen human or porcine samples, without histological quantitative consideration. Our objective was to generate fresh-human biaxial data includin...

Radiologist-AI Collaboration for Ischemia Diagnosis in Small-Bowel Obstruction: Multicentric Development and External Validation of a Multimodal Deep Learning Model.

This study aims to develop and externally validate a multimodal AI model for detecting ischemia complicating small-bowel obstruction (SBO). We combine...

Performance of artificial intelligence software: EndoAngel, EndoAID, CAD-EYE, GI Genius, and EndoScreener in adenoma detection: an extended network meta-analysis.

BACKGROUND: Colorectal cancer is a major global health burden, with most cases arising from adenomatous polyps. Although colonoscopy is the gold stand...

Global Co-Occurrence Patterns of Ischemic Heart Disease and Metabolic Dysfunction-Associated Steatotic Liver Disease: Spatial Clusters, Risk Factors, and Quality-of-Care Index.

BACKGROUND: Ischemic heart disease and metabolic dysfunction-associated steatotic liver disease represent significant global health challenges, charac...

OCTN1 variants shape innate immunity and predict individual response to anti-TNFα in ulcerative colitis patients: An exploratory study.

BACKGROUND: Ulcerative colitis (UC) exhibits substantial heterogeneity in inflammatory pathways and therapeutic response. The organic cation transport...

Evaluation of AI-Driven Automatic Segmentation Algorithms for Lung Shunt Fraction Estimation in 90Y-Labeled Microsphere Therapy.

90Y-labeled microsphere (90Y-microsphere) hepatic arterial radioembolization is a direct treatment for hepatic neoplasms. Before therapy, the fraction...

Next-generation AI for visually occult pancreatic cancer detection in a low-prevalence setting with longitudinal stability and multi-institutional generalisability.

BACKGROUND: Failure of conventional imaging to detect pancreatic ductal adenocarcinoma (PDA) at its visually occult pre-diagnostic stage is a primary ...

A machine learning predictive model for staging chronic hepatitis B inflammation based on non-invasive metrics.

Early screening and antiviral therapy for significant liver inflammation in chronic hepatitis B (CHB) remain significant barriers as the key to diseas...

Deep learning-based automatic scoring of drug-induced sleep endoscopy in obstructive sleep apnea.

Polysomnography is the standard tool for assessing obstructive sleep apnea (OSA) severity; however, it does not provide information regarding the anat...

Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.

Artificial intelligence (AI) is increasingly used in gastrointestinal endoscopy for polyp detection and classification. However, most AI models are tr...

A multiobjective AI model for LNP engineering enhances tissue-selective mRNA delivery.

Lipid nanoparticle (LNP) delivery of RNA therapeutics is constrained by poor tissue selectivity and off-target toxicity. Most high-throughput screenin...

A novel system design for monocular laser assisted capsule endoscopy for 3D small bowel modeling.

The long, tortuous, and tissue-homogeneous structure of the small bowel makes image-based three-dimensional (3D) modeling studies technically complex....

Image quality improvement of liver ultrasound using unsupervised deep learning.

Chronic liver disease (CLD) and subsequent liver cirrhosis (LC) are common causes of death and healthcare-related socio-economical costs worldwide. Ul...

Gastric cancer susceptibility mapping using GIS and machine learning in East Azerbaijan province, Iran.

Machine Learning Prediction of Hospital Stay in Pediatric Typhoid Intestinal Perforation: Pilot Study.

INTRODUCTION: Typhoid intestinal perforation (TIP) remains a significant cause of pediatric morbidity in resource-limited settings, with prolonged hos...

Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.

OBJECTIVE: To critically evaluate advances in artificial intelligence (AI) within rhinology, focusing on translational readiness, regulatory alignment...

The needle study: Machine learning as a new method for case-finding in celiac disease.

OBJECTIVES: Despite a well-defined diagnostic work-up, uncertainties persist regarding celiac disease (CeD) detection strategies in the general popula...

Machine Learning Models Using Hospital Admission Characteristics Do Not Optimally Predict Nosocomial Infection Development in a Global Cirrhosis Cohort.

BACKGROUND: Nosocomial infections (NI) in cirrhosis are associated with high mortality but could be preventable. Logistic regression (LR) models have ...

Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.

BACKGROUND: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis. Autom...