Gastroenterology

Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction.

This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients across two centers were analyzed, with clinical variables assessed via Cox regression and radiomic features extracted using deep learning. The 2D model ...

Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning.

OBJECTIVE: To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) using advanced machine learning algorithms. The focus is on improving early clinical identification and interpretability.

Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.

PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastri...

Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.

BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimina...

Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.

BACKGROUND: Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed ...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tu...

MCAUnet: a deep learning framework for automated quantification of body composition in liver cirrhosis patients.

Traditional methods for measuring body composition in CT scans rely on labor-intensive manual delineation, which is time-consuming and imprecise. This...

Development and validation of a small-sample machine learning model to predict 5-year overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Early-onset hepatocellular carcinoma (HCC) is insidious, with characteristics of easy metastasis, high recurrence rate, and significant mo...

Risk factors and prediction of distant metastasis (DM) of colon adenocarcinoma: a logistic regression and machine learning study based on surveillance, epidemiology, and end results (SEER) database.

An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.

BACKGROUND: Liver metastasis is the most frequent site of distant metastasis in pancreatic ductal adenocarcinoma (PDAC), significantly contributing to...

Machine learning models integrating dietary data predict all-cause mortality in U.S. NAFLD patients: an NHANES-based study.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease, closely associated with metabolic abnormalities and...

Impact of Introducing Artificial Intelligence on Colonoscopy: A Retrospective Study on Potential Benefits and Drawbacks.

BACKGROUND AND AIM: Computer-aided detection (CAD) can improve adenoma detection rates (ADRs); however, the impact of its introduction into real-world...

AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios.

Drug-induced liver injury (DILI) is a significant concern with prescription medications and supplements. Accordingly, it is crucial to develop tools a...

Taking the Guess Work Out of Endoscopic Polyp Measurement: From Traditional Methods to AI.

Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for...

Enhanced abdominal multi-organ segmentation with 3D UNet and UNet +  + deep neural networks utilizing the MONAI framework.

Accurate segmentation of organs in the abdomen is a primary requirement for any medical analysis and treatment planning. In this study, we propose an ...

MRI's evolving role in rectal cancer in the era of personalized medicine.

Recent decades have seen great advances in the diagnosis and management of rectal cancer, and magnetic resonance imaging (MRI) has become pivotal for ...

Establishment of a machine learning-based predictive model with dual-center external validation: investigating the role of robotic surgery in preventing delayed gastric emptying for right-sided colon cancer.

After colorectal surgery, delayed gastric emptying (DGE) is a clinically significant postoperative complication that significantly lowers patients' qu...

Artificial Intelligence in Gastrointestinal Endoscopy: The Japan Gastroenterological Endoscopy Society Position Statements.

Research and development of artificial intelligence (AI) in the field of gastrointestinal endoscopy is progressing rapidly. In Japan alone, there are ...

Integrated multi-omics reveals the impact of ruminal keystone bacteria and microbial metabolites on average daily gain in Xuzhou cattle.

UNLABELLED: The rumen microbiome plays a crucial role in determining the metabolic and digestive efficiency of livestock. Despite its crucial role, th...

Machine-Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study.

BACKGROUND: Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastric cancer (GC); however, post-ESD bleeding remains...