Gastroenterology

Comparative Analysis of Large Language Model Outputs for Pre-hypnotherapy Support in Irritable Bowel Syndrome - An Experimental Feasibility Study.

BACKGROUND/AIMS: : Gut directed hypnotherapy can assist with irritable bowel syndrome, but access remains limited. This feasibility study conducted an exploratory comparison of 4 large language models (LLMs) to evaluate the readability and information quality of model generated responses to common patient questions about pre- hypnotherapy support. METHODS: : Four LLMs (ChatGPT-4o, Claude 3.7 Sonne...

Artificial intelligence-driven prediction of neoadjuvant chemotherapy response in adult patients with gastric cancer: a systematic review and meta-analysis.

OBJECTIVES: A substantial proportion of adults with locally advanced gastric cancer derive limited benefit from neoadjuvant chemotherapy (NAC), with a non-response rate of 30%-50%. Artificial intelligence (AI) models that integrate imaging, pathology and clinical data seek to predict pretreatment NAC response to guide decision-making. We conducted a systematic review and meta-analysis to evaluate ...

Radiomics using generative adversarial network enhanced non-contrast computed tomography for gastric cancer diagnosis.

Objective.Non-contrast-enhanced computed tomography (NCCT) images have limited tissue resolution for gastric cancer diagnosis, while contrast-enhanced...

[Endoscopic diagnostics and treatment of early neoplasia of the esophagus].

Endoscopic diagnostics and treatment of early esophageal neoplasia have achieved considerable progress in recent years. For Barrett's esophagus-associ...

Biofabrication and artificial intelligence strategies for investigating solid- and fluid-pressure mechanobiology in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is shaped by a mechanically abnormal tumor microenvironment (TME) in which dysregulated mechanotransduction pr...

Targeting FAR1 to inhibit intestinal macrophage ferroptosis: repurposing irinotecan as a novel sepsis therapy.

Intestinal barrier breakdown is a key driver of sepsis-related multiple organ dysfunction; however, the metabolic mechanisms underlying this process r...

Linking bisphenol a exposure to pancreatic cancer: Evidence from population epidemiology, multi-omics screening, and cellular validation.

Bisphenol A (BPA) is a ubiquitous environmental endocrine disruptor; its association with pancreatic cancer and its potential mechanisms of action rem...

Influence of artificial intelligence on the performance of endosonographers in detection of solid pancreatic tumors: a tandem randomized video study.

BACKGROUND: This study aimed to evaluate the influence of a pre-commercial artificial intelligence (AI)-based software system on endosonographers' per...

Keeping the endoscopist in the loop: can artificial intelligence improve colonoscopy without deskilling endoscopists?

A Combined Clinical/MRI Deep Learning Model for Estimating Outcomes After Percutaneous Transforaminal Endoscopic Discectomy.

BACKGROUND: Percutaneous transforaminal endoscopic discectomy (PTED) is a common treatment for lumbar disc herniation (LDH), but postoperative recover...

Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial.

Liver malignancies are frequently evaluated on contrast-enhanced computed tomography (CE-CT), but missed or delayed diagnoses remain a clinically impo...

Automatic MRI-Based segmentation of ablation zones in spine and liver: A comparative study of network performance and influencing factors.

PURPOSE: Ablation therapies are a treatment option for cancer patients, particularly for conditions such as spinal metastases and liver tumors. Precis...

AI-assisted endoscopic detection of Helicobacter pylori infection in atrophic gastritis: a multicenter diagnostic study.

BACKGROUND: Convolutional neural network (CNN)-based artificial intelligence systems have demonstrated promise in detecting Helicobacter pylori (Hp) i...

An segformer-mix vision transformer-based GIST segmentation in fused CT-PET images.

Accurate segmentation of gastrointestinal stromal tumour (GIST) is always challenging task due to tissues intestines may have similar intensity values...

VesselVision-Net: Hybrid Global-Local Context Learning for Liver Vessel Segmentation in Hepatic Surgery Preplanning.

Accurate segmentation of liver vessels on contrast-enhanced CT is essential for safe hepatic surgery preplanning. It is still challenging because vess...

Dynamic iterative coarse-to-fine prompt learning based on SAM for precise esophageal cancer gross target volume segmentation.

Accurate segmentation of the gross tumor volume from computed tomography images is a core step in the development of precise radiotherapy planning for...

A novel medical image segmentation network for colorectal polyp small targets and fuzzy boundaries.

OBJECTIVES: To address the challenges of complex feature variations and unclear boundary definitions between segmented targets and surrounding regions...

Deep Learning-Driven Discovery and Engineering of an Efficient PETase for Depolymerization and Detoxification of PET Microplastics Under Physiological Conditions.

Microplastics (MPs) accumulation in ecosystem and human organs poses urgent environmental and health risks, yet few enzymes efficiently degrade polyet...

Integrating artificial intelligence and bioimpedance spectroscopy for enhanced pancreatic disease diagnosis.

The purpose of this study is to develop bioimpedance spectroscopy methods for diagnosing pancreatic diseases. We developed a descriptor approach using...

Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration.

Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynami...