Gastroenterology

Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning.

OBJECTIVES: To investigate the diagnostic performance of deep transfer learning (DTL) to detect live...

Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction.

PURPOSE: To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma...

Commentary: Minimally invasive esophagectomy (MIE) and robotic-assisted esophagectomy (RAMIE): We need high-volume surgeons, more science, and more robots!

Hybrid dilation and attention residual U-Net for medical image segmentation.

Medical image segmentation is a typical task in medical image processing and critical foundation in ...

Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions...

Development and validation of MRI-based deep learning models for prediction of microsatellite instability in rectal cancer.

BACKGROUND: Microsatellite instability (MSI) predetermines responses to adjuvant 5-fluorouracil and ...

Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.

OBJECTIVE: To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS...

Totally robotic intracorporeal Monti-Yang continent ileovesicostomy in patient with previous robotic surgery-Technique description.

We present a video case report of a pediatric patient with previous robotic abdominal surgery who un...

Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique.

PURPOSE: To evaluate the feasibility of High-resolution (HR) magnetic resonance imaging (MRI) of the...

Cytotoxicity and cell imaging of six types of carbon nanodots prepared through carbonization and hydrothermal processing of natural plant materials.

In this study we prepared six types of carbon nanodots (CNDs) from natural plant materials - through...

An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis.

INTRODUCTION: Very little artificial intelligence (AI) work has been performed to investigate acetam...

Chronic cholestasis detection by a novel tool: automated analysis of cytokeratin 7-stained liver specimens.

BACKGROUND: The objective was to build a novel method for automated image analysis to locate and qua...

Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound.

BACKGROUND AND AIM: This study aims to construct a strategy that uses assistance from artificial int...

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC-MS) can de...

A frogspawn-like Ag@C core-shell structure for an ultrasensitive label-free electrochemical immunosensing of carcinoembryonic antigen in blood plasma.

A three-dimensional (3D) frogspawn-like structure was achieved by simply coating nano-carbon outside...

Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic ...

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

PURPOSE: Radioactive seed implantation is an effective invasive treatment method for malignant liver...

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography.

PURPOSE: To construct a deep convolutional neural network that generates virtual monochromatic image...

A Machine Learning Model Approach to Risk-Stratify Patients With Gastrointestinal Cancer for Hospitalization and Mortality Outcomes.

PURPOSE: Patients with gastrointestinal (GI) cancer frequently experience unplanned hospitalizations...

Chinese guideline for the application of rectal cancer staging recognition systems based on artificial intelligence platforms (2021 edition).

Gastric polyp detection in gastroscopic images using deep neural network.

This paper presents the research results of detecting gastric polyps with deep learning object detec...