Gastroenterology

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-based LLM in analyzing clinical, radiological, and laboratory data from patients with hepatocellular carcinoma (HCC) to assess liver function, assign BCLC stage, and recommend treatment. Data from 106 HCC patients (82% male, median age 65 [22-86]) were c...

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 patients and may assist in developing a personalized treatment plan. However, to the best of our knowledge, studies on automatic CD lesion segmentation remain limited. This paper proposes a novel model (named CDSegNet) based on the U-Net to improve t...

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...

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...

IBDome: An integrated molecular, histopathological, and clinical atlas of inflammatory bowel diseases.

BACKGROUND & AIMS: Multi-omic and multimodal datasets with detailed clinical annotations offer significant potential to advance our understanding of i...

A Scoping Review of Endoscopic Assessment Considering Disease Extent in Ulcerative Colitis: Insights for the Artificial Intelligence Era.

Conventional endoscopic indices for ulcerative colitis (UC) primarily assess the most severely affected segment, potentially underestimating the cumul...

Data-Informed Intuition in Hepatology: Integrating Evidence, Context, and Clinical Reasoning.

Artificial intelligence and data-driven models are changing hepatology, but expert clinical judgment remains essential. Liver diseases are complex and...