Gastroenterology

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.

PURPOSE: To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs).

Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and the Acute Liver Failure Study Group (ALFSG) Prognostic Index are developed to predict outcome using only a single time point on hospital admission. Mod...

Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to ...

Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for pa...

Improving Outcomes Defending Patient Safety: The Learning Journey in Robotic Liver Resections.

BACKGROUND: While laparoscopy is currently adopted for hepatic resections, robotic approaches to the liver have not gained wide acceptance. We decided...

Robot-Assisted Simple Prostatectomy: Illustration of a Simplified Extraperitoneal Transcapsular Technique.

Robot-assisted simple prostatectomy (RASP) performed with the extraperitoneal (EP) technique (RASP-EP) minimizes the risk of bowel injury, particular...

Oncologic outcomes in patients treated with endoscopic robot assisted simple enucleation (ERASE) for renal cell carcinoma: Results from a tertiary referral center.

INTRODUCTION: Open Simple Enucleation (OSE) has been demonstrated to be an oncologically safe alternative to standard partial nephrectomy. We assessed...

Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video.

Automatic polyp detection has been shown to be difficult due to various polyp-like structures in the colon and high interclass variations in polyp siz...

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning.

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy d...

Booster immunity - diagnosis of chronic hepatitis B viral infection.

INTRODUCTION: Diagnosis of chronic hepatitis B virus (HBV) infection particularly its occult form requires monitoring and repeat serological and molec...

Diagnosis of focal liver lesions from ultrasound using deep learning.

PURPOSE: The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion ...

Quality assurance of computer-aided detection and diagnosis in colonoscopy.

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-a...

An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy.

OBJECTIVE: This study was designed to assess clinical predictors of hypoxemia and develop an artificial neural network (ANN) model for prediction of h...

Appetite ratings of foods are predictable with an in vitro advanced gastrointestinal model in combination with an in silico artificial neural network.

The expected increase of global obesity prevalence makes it necessary to have information about the effects of meal intakes on the feeling of appetite...

Real-time gastric polyp detection using convolutional neural networks.

Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advance...

Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network.

Laryngeal endoscopy is one of the primary diagnostic tools for laryngeal disorders. The main techniques are videostroboscopy and lately high-speed vid...

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatm...

Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images.

BACKGROUND AND AIM: We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basi...

Comment on: "Early Outcomes of Robot-Assisted Versus Thoracoscopic-Assisted Ivor Lewis Esophagectomy for Esophageal Cancer: A Propensity Score-Matched Study".

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex...