Gastroenterology

NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images.

Artificial intelligence (AI) has shown great promise in analyzing nasal endoscopic images for diseas...

Deep learning empowered gadolinium-free contrast-enhanced abbreviated MRI for diagnosing hepatocellular carcinoma.

BACKGROUND & AIMS: By reducing some magnetic resonance imaging (MRI) sequences, abbreviated MRI (aMR...

Molecular structure and mechanism of protein MSMB, TPPP3, SPI1: Construction of novel 4 pancreatic cancer-related protein signatures model based on machine learning.

The high mortality rate of pancreatic cancer is closely related to its inconspicuous early symptoms ...

Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video).

BACKGROUND: Endoscopic submucosal dissection (ESD) is a crucial yet challenging multi-phase procedur...

A Novel Mouse Model of Type 2 Diabetes Using a Medium-Fat Diet, Fructose, and Streptozotocin to Study the Complications of Human Disease.

The study of type 2 diabetes mellitus (T2DM) pathophysiology relies mainly on the use of animal mode...

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy ou...

Querying the capability of the post-HoLEP endoscopic aspect of the membranous urethral mucosa in predicting urinary incontinence: a prospective AI-based analysis.

INTRODUCTION: Transient stress urinary incontinence (SUI) after holmium laser enucleation of prostat...

Applying machine learning to predict bowel preparation adequacy in elderly patients for colonoscopy: development and validation of a web-based prediction tool.

BACKGROUND: Adequate bowel preparation is crucial for effective colonoscopy, especially in elderly p...

Endoscopists' knowledge, perceptions, and attitudes toward the use of artificial intelligence in endoscopy: a systematic review.

BACKGROUND AND AIMS: Artificial intelligence (AI) is rapidly evolving in the field of GI endoscopy. ...

Therapeutic potential of Da Cheng Qi Decoction and its ingredients in regulating ferroptosis via the NOX2-GPX4 signaling pathway to alleviate and predict severe acute pancreatitis.

OBJECTIVE: This study aimed to elucidate the protective effects of Da Cheng Qi Decoction (DCQD) on s...

Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study.

BACKGROUND: The aim of this study was to develop and internally validate an interpretable machine le...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in...

Post-Bariatric Hypoglycemia After Gastric Bypass: Clinical Characteristics, Risk Factors, and Future Directions-A Response to Grover et al.

BACKGROUND: Post-bariatric hypoglycemia (PBH) after Roux-en-Y gastric bypass (RYGB) is a complex com...

Innovative construction and application of bile duct organoids: Unraveling the complexity of bile duct diseases and potential therapeutic strategies.

The biliary system is crucial for liver function, regulating bile production, secretion, and transpo...

Enhancing pancreatic cancer diagnostics: Ensemble-based model for automated urine biomarker classification.

This research addresses the critical challenge of early detection in pancreatic ductal adenocarcinom...

Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is a major cause of drug development failures and postmarket drug w...

Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified common variants associated with m...

Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology feature...

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.

Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor...

Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning.

No studies have examined the prognostic value of the log odds of negative lymph nodes/T stage (LONT)...

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