Gastroenterology

Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC.

OBJECTIVES: To develop and evaluate a deep learning-based model capable of detecting primary hepatic malignancies in multiphase CT images of patients at high risk for hepatocellular carcinoma (HCC).

Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation.

Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, make the segmentation task challenging. To alleviate these challenges, in this article, we propose a cascaded multitask 3-D fully convolution network ...

Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.

PURPOSE: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on...

Robotic D2 Total Gastrectomy with Fluorescent Lymphatic Mapping for Gastric Cancer: Effective Use of the 4th Arm.

Minimally invasive surgery techniques have evolved remarkably over the past few decades in the field of surgical oncology, including robotic technique...

Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.

PURPOSE: The objective of this paper was to develop a computer-aided diagnostic (CAD) tools for automated analysis of capsule endoscopic (CE) images, ...

Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.

BACKGROUND: The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperativ...

Robotic approach to locally advanced rectal cancer: a video vignette.

A retrospective comparative study of robotic distal pancreatectomy with or without splenic vessel and spleen preservation.

BACKGROUND: Robotic distal pancreatectomy has been accepted to be safe and effective for pancreatic tail lesion. Whether spleen preservation by preser...

Tumor attention networks: Better feature selection, better tumor segmentation.

Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce th...

Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning.

BACKGROUND: Hepatic cystic echinococcosis is the main form of hepatic echinococcosis, which is a life-threatening liver disease caused by parasites th...

Robot-Assisted Cystectomy and Ileal Conduit for Neurogenic Bladder: Comparison of Extracorporeal Intracorporeal Urinary Diversion.

The aim of the present study was to compare the perioperative outcomes of extracorporeal (EXTRA) intracorporeal (INTRA) urinary diversion in patient...

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images.

BACKGROUND AND OBJECTIVE: Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better ...

Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning.

N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of wh...

Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn's Disease, Based on Confocal Laser Endomicroscopy Images.

BACKGROUND AND AIMS: Mucosal healing (MH) is associated with a stable course of Crohn's disease (CD) which can be assessed by confocal laser endomicro...

The evolving role of EUS-guided tissue acquisition.

The introduction of endoscopic ultrasound-guided fine-needle aspiration into clinical practice was a pivotal moment for diagnostic gastrointestinal en...

An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.

INTRODUCTION: Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. P...

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease.

Potential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies...

Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks.

OBJECTIVES: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different...

Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.

PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma ...

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

BACKGROUND: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples,...