Gastroenterology

Development and Validation of an Explainable Machine Learning Model for Warning of Hepatitis E Virus-Related Acute Liver Failure.

BACKGROUND AND AIMS: Early identification of patients with acute hepatitis E (AHE) who are at high risk of progressing to hepatitis E virus-related acute liver failure (HEV-ALF) is crucial for enabling timely monitoring and intervention. This multicentre retrospective cohort study aimed to develop and validate an interpretable machine learning (ML) model for predicting the risk of HEV-ALF in hospi...

Efficient polyp detection algorithm based on deep learning.

OBJECTIVE: Colon polyp detection is crucial in reducing the incidence of colorectal cancer. However, due to the diverse morphology of colon polyps, their high similarity to surrounding tissues, and the difficulty of detecting small target polyps, false negatives and false positives are common problems.

Design and Evaluation of an Omnidirectional Wheel-Driven Endoscope-Assisted Robotic System Based on Motion Capture Control.

BACKGROUND: In endoscopic surgery, surgeons collaborate with assistants to manipulate the endoscope and instruments, making it impossible to perform t...

3-D contour-aware U-Net for efficient rectal tumor segmentation in magnetic resonance imaging.

Magnetic resonance imaging (MRI), as a non-invasive detection method, is crucial for the clinical diagnosis and treatment plan of rectal cancer. Howev...

Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.

INTRODUCTION: Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as...

Boosting polyp screening with improved point-teacher weakly semi-supervised.

Polyps, like a silent time bomb in the gut, are always lurking and can explode into deadly colorectal cancer at any time. Many methods are attempted t...

Uncovering hepatic transcriptomic and circulating proteomic signatures in MASH: A meta-analysis and machine learning-based biomarker discovery.

BACKGROUND: Metabolic-associated steatohepatitis (MASH), the progressive form of metabolic-associated steatotic liver disease (MASLD), poses significa...

A Pilot Study on Deep Learning With Simplified Intravoxel Incoherent Motion Diffusion-Weighted MRI Parameters for Differentiating Hepatocellular Carcinoma From Other Common Liver Masses.

OBJECTIVES: To develop and evaluate a deep learning technique for the differentiation of hepatocellular carcinoma (HCC) using "simplified intravoxel i...

Attention in surgical phase recognition for endoscopic pituitary surgery: Insights from real-world data.

BACKGROUND AND OBJECTIVE: Surgical Phase Recognition systems are used to support the automated documentation of a procedure and to provide the surgica...

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This s...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patien...

Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary inf...

Development and validation of the Open-Source Automatic Bowel Preparation Scale.

BACKGROUND AND AIMS: Insufficient bowel preparation accounts for up to 42% of missed adenomas in colonoscopy. However, major analysis programs found n...

Prediction of endoscopic restenosis after endoscopic balloon dilation in patients with Crohn's disease: a machine learning approach.

BACKGROUND: Endoscopic balloon dilation (EBD) is recognized as a minimally invasive and effective procedure for managing intestinal stenosis in patien...

Artificial intelligence-assisted endoscopic ultrasound diagnosis of esophageal subepithelial lesions.

BACKGROUND: Endoscopic ultrasound (EUS) is one of the most accurate methods for determining the originating layer of subepithelial lesions (SELs). How...

XYZ-IBD: High-precision Bin-picking Dataset for Object 6D Pose Estimation Capturing Real-world Industrial Complexity

We introduce XYZ-IBD, a bin-picking dataset for 6D pose estimation that captures real-world industrial complexity, including challenging object geom...

Integration of Bulk RNA and Single-Cell Analyses Reveal Distinct Expression Patterns of Anoikis-Related Genes and the Immunosuppressive Role of NQO1 Macrophages in Hepatocellular Carcinoma.

Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes invol...

Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.

Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective m...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to c...

DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset

Pathologists comprehensive evaluation of donor liver biopsies provides crucial information for accepting or discarding potential grafts. However, ra...