Gastroenterology

Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples.

Efficient and accurate methods are needed to automatically segmenting organs-at-risk (OAR) to accelerate the radiotherapy workflow and decrease the treatment wait time. We developed and evaluated the use of a fused model Dense V-Network for its ability to accurately segment pelvic OAR. We combined two network models, Dense Net and V-Net, to establish the Dense V-Network algorithm. For the trainin...

Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet.

BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative colitis which can be treated by a targeted drug delivery system.

SoGut: A Soft Robotic Gastric Simulator.

The human stomach breaks down and transports food by coordinated radial contractions of the gastric walls. The radial contractions periodically propag...

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force.

Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve p...

Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type...

[Robot-Assisted Repeated Fundoplication in Children and Adolescents].

BACKGROUND: Recurrent gastroesophageal reflux symptoms in adolescents and young adults who underwent fundoplication in childhood present a technical c...

History of artificial intelligence in medicine.

Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application...

Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determin...

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recogn...

Utilizing artificial intelligence in endoscopy: a clinician's guide.

INTRODUCTION: Artificial intelligence (AI) that surpasses human ability in image recognition is expected to be applied in the field of gastrointestina...

A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.

OBJECTIVE: Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with ...

Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools.

There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmenta...

Development and evaluation of a novel protective device for upper gastrointestinal endoscopy in the COVID-19 pandemic: the EBOX.

BACKGROUND: During the COVID-19 pandemic, aerosol-generating procedures such as upper gastrointestinal endoscopy (UGIE) have been considered high risk...

A deep learning risk prediction model for overall survival in patients with gastric cancer: A multicenter study.

BACKGROUND AND PURPOSE: Risk prediction of overall survival (OS) is crucial for gastric cancer (GC) patients to assess the treatment programs and may ...

Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling.

Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and ...

A Novel Robotic Endoscopic Device Used for Operative Hysteroscopy.

To trial the use of a novel endoscopic robot that functions using concentric tube robots, enabling 2-handed surgery in small spaces, in a bioengineeri...

Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer.

Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by tra...

Assessment of liver metastases radiomic feature reproducibility with deep-learning-based semi-automatic segmentation software.

BACKGROUND: Good feature reproducibility enhances model reliability. The manual segmentation of gastric cancer with liver metastasis (GCLM) can be tim...

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases su...

Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withd...