Gastroenterology

Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients wi...

Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations.

PURPOSE: Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, manual tracing is resource intensive. To address these limitations and to expand the availability of a quantitative CT measure of hepatic steatosis, we propose the automatic...

A histopathologic feature of the behavior of gastric signet-ring cell carcinoma; an image analysis study with deep learning.

Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis.

BACKGROUND: Multiple organ failure (MOF) is a serious complication of moderately severe (MASP) and severe acute pancreatitis (SAP). This study aimed t...

Association of trough vedolizumab levels with clinical, biological and endoscopic outcomes during maintenance therapy in inflammatory bowel disease.

OBJECTIVE: To establish the relationship between trough vedolizumab levels and outcomes during maintenance therapy.

Predictors of blood transfusion use in robotic beating-heart totally endoscopic coronary artery bypass with anastomotic connectors.

BACKGROUND: We sought to identify the independent predictors of blood transfusion requirement in robotic beating-heart patients with totally endoscopi...

Supervised Learning and Mass Spectrometry Predicts the Fate of Nanomaterials.

The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes ...

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most case...

A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology.

CONTEXT.—: Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.

Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours ...

MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to p...

Automatic gas detection in prostate cancer patients during image-guided radiation therapy using a deep convolutional neural network.

PURPOSE: The detection of intestinal/rectal gas is very important during image-guided radiation therapy (IGRT) of prostate cancer patients because int...

Application of Deep Learning in Quantitative Analysis of 2-Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease.

OBJECTIVES: To verify the value of deep learning in diagnosing nonalcoholic fatty liver disease (NAFLD) by comparing 3 image-processing techniques.

A Novel Relative Position Estimation Method for Capsule Robot Moving in Gastrointestinal Tract.

Recently, a variety of positioning and tracking methods have been proposed for capsule robots moving in the gastrointestinal (GI) tract to provide rea...

Modelling disease risk for amyloid A (AA) amyloidosis in non-human primates using machine learning.

Amyloid A (AA) amyloidosis is found in humans and non-human primates, but quantifying disease risk prior to clinical symptoms is challenging. We appl...

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being....

Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier.

BACKGROUND: Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching o...

Therapeutic effect of gastrografin and predictors of operative intervention in patients with adhesive small bowel obstruction: A randomized controlled study.

OBJECTIVES: The study aimed to investigate the therapeutic effect of gastrografin for the conservative treatment of patients with adhesive small bowel...

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram.

OBJECTIVE: Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and ...

Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance.

Hepatitis B surface antigen (HBsAg) seroclearance during treatment is associated with a better prognosis among patients with chronic hepatitis B (CHB)...