Gastroenterology

Detection of flat colorectal neoplasia by artificial intelligence: A systematic review.

OBJECTIVES: This study review focuses on a deep learning method for the detection of colorectal lesions in colonoscopy and AI support for detecting colorectal neoplasia, especially in flat lesions.

Charting a path forward for clinical research in artificial intelligence and gastroenterology.

Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific st...

A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy.

Histopathological diagnosis of pancreatic ductal adenocarcinoma (PDAC) on endoscopic ultrasonography-guided fine-needle biopsy (EUS-FNB) specimens has...

Robotic versus laparoscopic total mesorectal excision for rectal cancer: a meta-analysis of long-term survival and urogenital functional outcomes.

INTRODUCTION: Robotic surgical technology has been widely introduced and applied in various fields of surgery. The aim of this study was to analyze lo...

Robotic hepatectomy and biliary reconstruction for perihilar cholangiocarcinoma: a pioneer western case series.

Open surgery is the standard of care for perihilar cholangiocarcinoma (pCCA). With the aim of oncologic radicality, it requires a complex major hepate...

Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning.

Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automat...

Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation.

The advancement of artificial intelligence (AI) has facilitated its application in medical fields. However, there has been little research for AI-assi...

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge an...

Use of Endoscopic Images in the Prediction of Submucosal Invasion of Gastric Neoplasms: Automated Deep Learning Model Development and Usability Study.

BACKGROUND: In a previous study, we examined the use of deep learning models to classify the invasion depth (mucosa-confined versus submucosa-invaded)...

Automatic tip detection of surgical instruments in biportal endoscopic spine surgery.

BACKGROUND: Recent advances in robotics and deep learning can be used in endoscopic surgeries and can provide numerous advantages by freeing one of th...

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed netwo...

State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020.

PURPOSE OF REVIEW: Recently numerous researchers have shown remarkable progress using convolutional neural network-based artificial intelligence (AI) ...

An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients.

In recent years, artificial intelligence-based computer aided diagnosis (CAD) system for the hepatitis has made great progress. Especially, the comple...

Machine Learning Algorithms for Predicting Fatty Liver Disease.

BACKGROUND: Fatty liver disease (FLD) has become a rampant condition. It is associated with a high rate of morbidity and mortality in a population. Th...

Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.

BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular ...

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network.

A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be remove...

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.

PURPOSE: Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (...

Evaluation of the learning curve of robot-assisted laparoscopic ventral mesh rectopexy.

BACKGROUND: The current standard treatment for external rectal prolapse and symptomatic high-grade internal rectal prolapse is surgical correction wit...

Hepatic Alveolar Echinococcosis: Predictive Biological Activity Based on Radiomics of MRI.

BACKGROUND: To evaluate the role of radiomics based on magnetic resonance imaging (MRI) in the biological activity of hepatic alveolar echinococcosis ...

Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance.

Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detec...