Gastroenterology

The Machine Learning Model for Predicting Inadequate Bowel Preparation Before Colonoscopy: A Multicenter Prospective Study.

INTRODUCTION: Colonoscopy is a critical diagnostic tool for colorectal diseases; however, its effect...

Benchmarking clinical risk prediction algorithms with ensemble machine learning for the noninvasive diagnosis of liver fibrosis in NAFLD.

BACKGROUND AND AIMS: Ensemble machine-learning methods, like the superlearner, combine multiple mode...

Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damag...

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study.

OBJECTIVE: The purpose of this study was to develop an individual survival prediction model based on...

Segmentation of liver CT images based on weighted medical transformer model.

Deep convolutional neural networks have made significant strides in the field of medical image segme...

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review.

The pancreas is a vital organ in digestive system which has significant health implications. It is i...

Efficiency of endoscopic artificial intelligence in the diagnosis of early esophageal cancer.

BACKGROUND: The accuracy of artificial intelligence (AI) and experts in diagnosing early esophageal ...

Deep learning-aided 3D proxy-bridged region-growing framework for multi-organ segmentation.

Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-aided diagnos...

Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database.

OBJECTIVE: This study aims to establish a machine learning (ML) model for predicting the risk of liv...

Multiclassification of Hepatic Cystic Echinococcosis by Using Multiple Kernel Learning Framework and Ultrasound Images.

UNLABELLED: To properly treat and care for hepatic cystic echinococcosis (HCE), it is essential to m...

UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images.

Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localiz...

Artificial Intelligence for Quantifying Cumulative Small Bowel Disease Severity on CT-Enterography in Crohn's Disease.

INTRODUCTION: Assessing the cumulative degree of bowel injury in ileal Crohn's disease (CD) is diffi...

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic perfor...

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.

BACKGROUND: The objective of this study is to develop and validate a machine learning (ML) predictio...

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging...

Artificial intelligence supported ultrasonography: Can it be the solution for early and accurate diagnosis of steatotic liver disease?

An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy.

BACKGROUND: Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differ...

BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images.

The progress of research focused on cholangiocytes and the biliary tree during development and follo...

Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer.

BACKGROUND: Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent ...

Imaging segmentation mechanism for rectal tumors using improved U-Net.

OBJECTIVE: In radiation therapy, cancerous region segmentation in magnetic resonance images (MRI) is...

Endoscopic surgical field clarity index: An artificial intelligence-based measure of transnasal endoscopic surgical field quality.

Clear visualization during transnasal endoscopic surgery (TNES) is crucial for safe, efficient surge...