Gastroenterology

An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas.

BACKGROUND: Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtypes of gastric submucosal tumors (SMTs) characterized by distinct biological characteristics and treatment modalities. The accurate differentiation between GSTs and GLs poses a significant clinical challenge. Recent advancements in artificial intelligence (AI) leveraging endoscopic ultrasonography (EU...

Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.

BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC), but have not been systematically assessed.

Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.

BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical e...

T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging.

BACKGROUND: T-cell receptor (TCR) repertoires provide insights into tumor immunology, yet their variations across digestive system cancers are not wel...

Pseudotargeted metabolomics profiles potential damage-associated molecular patterns as machine learning predictors for acute pancreatitis.

Acute pancreatitis (AP) is a common gastrointestinal disease characterized by pancreatic cell damage and inflammation. Given the early clinical diagno...

Enhancing polyp classification: A comparative analysis of spatio-temporal techniques.

Colorectal cancer (CRC) is a major health concern, ranking as the third deadliest cancer globally. Early diagnosis of adenomatous polyps which are pre...

Recent Advances in Molecular Targeted Therapy for Inflammatory Bowel Disease.

Progress in the development of molecular-targeted therapies in the field of inflammatory bowel disease has been remarkable. With the advent of molecul...

Identification of patients at risk for pancreatic cancer in a 3-year timeframe based on machine learning algorithms.

Early detection of pancreatic cancer (PC) remains challenging largely due to the low population incidence and few known risk factors. However, screeni...

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between ma...

Randomized Trial on Electroacupuncture for Recovery of Postoperative Gastrointestinal Function Based on Long-Term Monitoring Device.

BACKGROUND: This research aimed to explore the efficacy and safety of electroacupuncture in promoting the recovery of postoperative gastrointestinal f...

Next questions on gastrointestinal stromal tumors: unresolved challenges and future directions.

PURPOSE OF REVIEW: Despite remarkable progress in the management of gastrointestinal stromal tumors (GISTs), critical challenges persist. Key aspects ...

Explainable machine learning for the assessment of donor grafts in liver transplantation.

BACKGROUND AND AIM: The shortage of liver grafts compared to recipients necessitates precise organ assessment. This study aimed to develop a Machine l...

Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.

Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty ...

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence ra...

C2BNet: A Deep Learning Architecture With Coupled Composite Backbone for Parasitic Egg Detection in Microscopic Images.

Internet of Medical Things (IoMT) enabled by artificial intelligence (AI) technologies can facilitate automatic diagnosis and management of chronic di...

LKAN: LLM-Based Knowledge-Aware Attention Network for Clinical Staging of Liver Cancer.

Clinical staging of liver cancer (CSoLC), an important indicator for evaluating primary liver cancer (PLC), is key in the diagnosis, treatment, and re...

Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data.

Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage...

Identification of Crohn's Disease-Related Biomarkers and Pan-Cancer Analysis Based on Machine Learning.

: In recent years, the incidence of Crohn's disease (CD) has shown a significant global increase, with numerous studies demonstrating its correlation ...

Direct-acting antivirals (DAA) positively affect depression and cognitive function in patients with chronic hepatitis C.

The aim of the study was to determine how depression and cognitive dysfunction in patients with chronic hepatitis C virus (HCV) infection are affected...

Root of Prunus persica (taoshugen) ameliorated renal fibrosis by inhibiting TGF-β signaling via upregulating Pmepa1 in mice with unilateral ureter obstruction.

ETHNOPHARMACOLOGICAL RELEVANCE: Various parts of Prunus persica (L.) Batsch (peach) exhibit medicinal properties and are utilized in traditional Chine...