Gastroenterology

Keros-Net: a convolutional block attention module-squeeze-and-excitation-integrated hybrid learning framework for olfactory fossa depth classification.

PURPOSE: This study aimed to develop a hybrid decision support framework combining deep learning (DL...

A Machine Learning-Based Clinical Tool for Predicting Inadequate Bowel Preparation: Development and Validation.

OBJECTIVES: Colonoscopy is an essential tool for diagnosing and treating colorectal diseases, with b...

AI-Derived Electronic Tumor Marker For Cancer Antigen 19-9 Nonproducers With Pancreatic Ductal Adenocarcinoma.

IMPORTANCE: Cancer antigen 19-9 (CA19-9) is used to assess treatment response among patients with pa...

An artificial intelligence-powered digital pathology platform to support large-scale deworming programs against soil-transmitted helminthiasis and intestinal schistosomiasis in resource-limited settings.

BACKGROUND: The World Health Organization (WHO) has emphasised the need for innovative diagnostic to...

Text-guided automatic segmentation of clinical target volume in rectal cancer radiotherapy.

OBJECTIVE: Current automatic segmentation models in radiotherapy, which are predominantly unimodal a...

Token-guided multimodal prognosis in hepatocellular carcinoma: a framework steered by tumour-stroma ratio.

BACKGROUND: The tumour-stroma ratio (TSR) is a potential prognostic indicator, yet hindered by quant...

ChatGPT in liver transplantation: Current applications, limitations, and future directions.

Liver transplantation (LT) remains the optimal life-saving intervention for patients with end-stage ...

Prospective evaluation of artificial intelligence-assisted monitoring of the effective withdrawal time on adenoma detection.

BACKGROUND/AIMS: Standard withdrawal time (SWT) is a recognized colonoscopy quality metric but may n...

Pretreatment Radiation Esophagitis Prediction Using Quantum Machine Learning in Patients With Esophageal Cancer.

PURPOSE: To introduce a hybrid quantum-classical machine learning approach and validate its feasibil...

Development of a convolutional neural network for the endoscopic classification of pouchitis in patients after restorative proctocolectomy.

BACKGROUND AND AIMS: The aim of this prospective single-center study is to train convolutional neura...

High-Speed Instance Segmentation for Endoscopic Spine Surgery: Multicenter Validation and Inference Speed Evaluation.

Study DesignRetrospective Multicenter Cohort Study.ObjectivesTo develop and validate an AI-based hig...

Artificial intelligence-assisted polyp detection in colonoscopy - Methodological considerations for annotation standardization.

Noninvasive Detection of Occult yet Significant Liver Pathology in Alanine Aminotransferase-Normal Chronic Hepatitis B: A Multicenter Study.

BACKGROUND: Individuals with chronic hepatitis B (CHB) may harbor occult yet significant liver patho...

Application value of gastric contrast ultrasonography in gastric tumors.

In recent years, gastric oral contrast ultrasonography (OCUS) and double contrast-enhanced ultrasono...

Rectal Cancer Radiotherapy Response Prediction: Retrospective Study of Development of a Deep Learning-Based Radiomics Model.

Background: Radiotherapy (RT) is a cornerstone of multimodal treatment for rectal cancer (RC); yet, ...

Minimally invasive spine surgery: current advantages, limitations, and future directions.

Minimally invasive spine surgery (MISS), supported by advancements in endoscopic systems, tubular re...

The first reported case using real-time artificial intelligence with single-port robotics for colorectal surgery.

BACKGROUND: With the increased utilization of artificial intelligence (AI) tools in the operating ro...

Discovery of Novel MLKL PROTAC Degraders for the Treatment of Hepatocellular Carcinoma via Promoting Parthanatos.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, primar...

Machine learning models based on MRI predict the expression levels of Galectin - 9 expression in rectal cancer.

OBJECTIVE: To investigate the utility of a machine learning model based on MRI radiomics in predicti...

Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters.

OBJECTIVES: To develop and validate a machine learning model integrating ultrasound radiomics and cl...

A critical review of technical progress, clinical heterogeneity, and implementation challenges of artificial intelligence in digestive endoscopy.

INTRODUCTION: Digestive endoscopy is a critical modality for diagnosing and managing gastrointestina...