Gastroenterology

Nationwide 30-Day Surgical Outcomes in Korea: An Interim Analysis of the Korean Quality Improvement Platform in Surgery.

BACKGROUND: National surgical registries such as the American College of Surgeons NSQIP and Japan's National Clinical Database have shown that structured, risk-adjusted outcome monitoring improves surgical quality. However, no comparable nationwide registry has been reported in Korea. STUDY DESIGN: This interim analysis of the Korean Quality Improvement Platform in Surgery included 42,943 cases fr...

A multidimensional risk prediction framework for malignant intestinal obstruction based on machine learning: computational model development and clinical validation.

Malignant intestinal obstruction (MIO) is a severe complication of advanced cancer. Traditional static assessment models struggle to capture its dynamic pathological mechanisms, limiting their clinical value. To address this, this study developed a multimodal machine learning framework. Core features (including the dynamic tumor enhancement ratio TER) were extracted via Lasso-Boruta dual-modality ...

Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations.

Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-base...

CDSegNet: a multi-scale convolution and attention mechanism U-shaped network for Crohn's disease lesion segmentation.

Accurate segmentation of Crohn's disease (CD) lesions from computed tomography enterography (CTE) cross-sectional images is crucial for diagnosing CD ...

Comments on "Artificial Intelligence in Colonoscopy Surveillance for Lynch Syndrome: Emerging Evidence, Lessons Learned From Average-Risk Populations, and Future Directions".

How can reasoning capability empower the AI copilot robot in endoscopic surgery.

Reasoning capability has significantly advanced complex logical inference and robotic decision-making in general domains. However, its potential in th...

Fusion of MRI-radiomics and clinical features to improve pathological T-stage prediction of rectal cancer: a multicenter study.

BACKGROUND: Developing a rational model for precise preoperative staging of rectal cancer using magnetic resonance imaging is crucial for improving th...

CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with high early recurrence rates after curative resection. Current predictio...

Smart feeding: the role of artificial intelligence and integrated nutrition platforms in the ICU.

PURPOSE OF REVIEW: Tremendous improvement in the use of artificial intelligence has opened new opportunities to analyze the data obtained from electro...

Machine learning models to predict the recurrence of common bile duct stones after ERCP.

OBJECTIVE: To construct predictive models for the recurrence of common bile duct stones (CBDS) following endoscopic retrograde cholangiopancreatograph...

A Single-Cell Guided Machine Learning Model Predicts Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Resistance to immune checkpoint inhibitors is a major clinical obstacle in the treatment of gastric cancer. Identifying drug-resistant cell population...

IgG4-related disease has a specific intestinal microbiota signature.

BACKGROUND: While the intestinal microbiome has been implicated in Immunoglobulin-4 related disease (IgG4-RD), it remains poorly characterised. Theref...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

Machine learning-guided design of mechanoadaptive bioglues for multitissue trauma and first-aid applications.

Treating wounds that involve multiple types of injury is particularly challenging due to their complex morphology and the diverse mechanical propertie...

Real-World Validation of PinPoint Blood Tests in the NHS: Multivariable Machine Learning to Predict Cancer Risk in Primary Care Urgent Referrals.

OBJECTIVE: To validate the United Kingdom Conformity Assessed-marked PinPoint blood tests, which use machine learning models and routinely available b...

TENTACLES: a consensus machine learning tool for robust biomarker discovery in heterogeneous data.

BACKGROUND: Transcriptomic biomarker discovery often fails to produce reproducible gene signatures across independent cohorts due to model-specific bi...

Deep Learning-Driven Saccharide Online Sequencing for Elucidating the Pathological Alterations of Heparan Sulfate in APAP-Induced Acute Liver Injury.

Heparan sulfate (HS), one of the mostly negatively charged biomacromolecules anchored on the membrane surface of nearly all mammal cells, plays critic...

Machine learning for predicting liver metastasis in colorectal cancer.

AIM: To evaluate the performance of machine learning models in predicting liver metastasis in colorectal cancer (CRC) patients using the SEER database...

Identification of estrogen-related biomarkers in metabolic dysfunction-associated steatotic liver disease (MASLD) through machine learning and single-cell analysis.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a preval...

Sinomenine alleviates ulcerative colitis by targeting FXR to regulate arachidonic acid metabolism and Th17/Treg homeostasis.

Sinomenine (SIN), a bioactive alkaloid with anti-inflammatory activity, has shown therapeutic potential in ulcerative colitis (UC), but its precise mo...