Gastroenterology

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 study assessed whether CADe use promotes learning or deskilling. METHODS: We performed a prospective, multicenter, registry-based, pragmatic clinical trial. The primary end point was change in the proportion of colonoscopies with detection of at lea...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detected only after symptoms appear, which usually occurs at later stages of a disease when available treatments may be less effective. Current detection techniques primarily depend on identifying metabolites in biofluids such as blood and urine. The analy...

Acid Yellow 36, Methyl Red, and Methylene Blue adsorption using ammonia-modified red algae biochar: isotherm, kinetic, regeneration, and ANN studies.

This paper examined the removal of Acid Yellow 36 (AY36), Methyl Red (MR), and Methylene Blue (MB) dyes using a novel Ammonia-decorated Red Algae Bioc...

A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.

Immune checkpoint inhibitors, particularly antibodies targeting programmed cell death 1 (PD-1), are increasingly used for advanced hepatocellular carc...

How to quickly determine whether patients with chronic cough need corticosteroid treatment--construction of a predictive model for corticosteroid-responsive cough in chronic cough.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-r...

Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures.

Metabolic Dysfunction-Associated Steatohepatitis (MASH) is a severe form of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), traditio...

Dual swin transformer for assisting in the diagnosis and surgical prediction of necrotizing enterocolitis.

BACKGROUND: The diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain challenging. Our goal is to develop an interpretable multi...

Integration of single-cell and bulk RNA sequencing reveals programmed cell death-associated transcriptional programs in sepsis-induced acute lung injury.

BACKGROUND: Sepsis-induced acute lung injury (ALI) is a frequent and life-threatening complication of sepsis, yet clinically actionable transcriptomic...

Accuracy of machine learning in predicting recurrence risk of lumbar disc herniation after percutaneous endoscopic lumbar discectomy: a systematic review and meta-analysis.

BACKGROUND: Lumbar disc herniation (LDH) is a major cause of low back pain and radicular leg pain. Percutaneous endoscopic lumbar discectomy (PELD) is...

Ultrasound elastography in pediatric care: bridging innovation and noninvasive diagnostics.

This review explores the application and limitations of ultrasound elastography (USE) in the pediatric population, addressing its diagnostic value acr...

Complex in vitro model developers' perspective on advanced hepatic in vitro culture methods for predictive drug safety.

INTRODUCTION: Adverse drug reactions remain a major barrier to drug development, with hepatotoxicity representing a persistent cause of clinical failu...

Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis.

BACKGROUND: Conventional clinical scoring systems and contrast-enhanced computed tomography (CECT) interpretation provide limited accuracy in predicti...

Nanomedicine in precision treatment of gastric cancer: current clinical landscape.

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with therapeutic efficacy often hindered by late-stage diagnosis, c...

A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer.

BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths globally, with early detection crucial for improving survival. Current non...

Hepatic Vessel Map (HVM): An Expert-Annotated CT Dataset for Clinically Applicable AI in Liver Vascular Segmentation and Surgical Planning.

Precise delineation of hepatic and portal venous anatomy is crucial for the diagnosis of liver disease, surgical planning, and prognosis prediction. C...

Deep learning reconstruction dual-energy computed tomography for gastrointestinal system tumors: low-kiloelectron volt imaging vs routine imaging.

AIM: To qualitatively and quantitatively compare dual-energy computed tomography (DECT)-derived 55 keV virtual monochromatic images (VMIs) using deep ...

Early Prediction of Diabetic Macular Edema via Machine Learning Survival Analysis on Checkup Data.

PURPOSE: To predict the risk of diabetic macular edema (DME) onset and to identify features of the risk subgroups. DESIGN: Population-based observatio...

Construction of liver organoid models by hepatobiliary differentiation from human induced pluripotent stem cells: state of the art, challenges and improving strategies.

Physiologically relevant liver models are essential for advancing hepatic disorder research, especially for disease modeling and drug development, yet...

Performance comparison of a neuro-symbolic large language model system versus conventional AI models and human experts in cholangitis management.

BACKGROUND: Large language models (LLMs) have shown promising results in medical decision support; Background: Large language models (LLMs) have demon...

Computational Quantification of Peristalsis in Preclinical Mouse Models Using Smartphone Videography.

BACKGROUND: A number of diseases and medical interventions affect gastrointestinal motility. However, quantitative methods for measuring effects on pe...