Gastroenterology

Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures.

PURPOSE: Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels ...

Robot-Assisted Minimally Invasive Esophagectomy Reduces the Risk of Recurrent Laryngeal Nerve Palsy.

Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task.

PURPOSE: A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-ai...

Esophageal cancer detection based on classification of gastrointestinal CT images using improved Faster RCNN.

PURPOSE: Esophageal cancer is a common malignant tumor in life, which seriously affects human health. In order to reduce the work intensity of doctors...

Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network.

Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment...

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we addr...

Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with h...

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 liver cirrhosis from clinical MRI.

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 (PDAC) in pancreatic low-dose computed tomography...

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 medical image analysis. U-Net is well-liked in med...

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 is important. However, population-based endoscopi...

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 immunotherapy in rectal cancer and serves as a pro...

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) MRI of the liver with deep learning-based image ...

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 underwent robotic assisted Monti-Yang continent ileo...

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 liver using deep learning reconstruction (DLR) ba...

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 carbonization of two species of bamboo (Bamboo-I,...

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

INTRODUCTION: Very little artificial intelligence (AI) work has been performed to investigate acetaminophen-associated hepatotoxicity. The objective o...

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 quantify the number of cytokeratin 7 (K7)-positive he...

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 intelligence (AI) to assist radiologists in the ident...