Gastroenterology

Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning.

BACKGROUND: Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment.

Real-Time Identification of Pancreatic Cancer Cases Using Artificial Intelligence Developed on Danish Nationwide Registry Data.

PURPOSE: Pancreatic cancer is expected to be the second leading cause of cancer-related deaths worldwide within few years. Most patients are not diagnosed in time for curative-intent treatment. Accelerating the time of diagnosis is a key component of reducing pancreatic cancer mortality. We developed and tested a dynamic algorithm aiming at proactively identifying patients with a substantially ele...

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials.

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is imp...

Initial learning curves of laparoscopic and robotic distal pancreatectomy compared with open distal pancreatectomy: multicentre analysis.

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.

PURPOSE: Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Rout...

Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.

Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis ...

US Quantification of Liver Fat: Past, Present, and Future.

Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical cost...

Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.

INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common ...

Mining Whole-liver Information with Deep Learning for Preoperatively Predicting HCC Recurrence-free Survival.

Hepatocellular carcinoma (HCC) is globally a leading cause of cancer death. Non-invasive pre-operative prediction of HCC recurrence-free survival (RFS...

Diagnostic Performance of Deep Learning Models for Gastric Intestinal Metaplasia Detection in Narrow-band Images.

Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endos...

A quick and reliable image-based AI algorithm for evaluating cellular senescence of gastric organoids.

OBJECTIVE: Organoids are a powerful tool with broad application prospects in biomedicine. Notably, they provide alternatives to animal models for test...

A Deep Learning Model for Classifying Histological Types of Colorectal Polyps.

In this study a deep learning architecture based on a convolutional neural network has been evaluated for the classification of white light images of ...

[Clinical effects of robot-assisted esophageal hiatal hernia repair and laparoscopic esophageal hiatal hernia repair: a retrospective comparative study].

To analyze the short-term clinical effects of robot-assisted and laparoscopic repair of the hiatal hernia. The clinical data of 56 patients underwen...

Artificial intelligence, machine learning, and deep learning in liver transplantation.

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predom...

An MRI Deep Learning Model Predicts Outcome in Rectal Cancer.

Background Deep learning (DL) models can potentially improve prognostication of rectal cancer but have not been systematically assessed. Purpose To de...

[The Digital Transformation(Dx)of Endoscopic Examinations].

Artificial intelligence(AI)and information and communication technology(ICT)are beginning to be used in the digital transformation of endoscopic image...

Learning curve for adoption of robot-assisted minimally invasive esophagectomy: a systematic review of oncological, clinical, and efficiency outcomes.

BACKGROUND: Robot-assisted minimally invasive esophagectomy (RAMIE) is gaining increasing popularity as an operative approach. Learning curves to achi...

Management of anastomotic leakage after robot-assisted minimally invasive esophagectomy with an intrathoracic anastomosis.

Anastomotic leakage is a feared complication after esophagectomy and associated with increased post-operative morbidity and mrotality. The aim of this...

[Network meta-analysis comparing the clinical outcomes and safety of robotic, laparoscopic, and transanal total rectal mesenteric resection for rectal cancer].

To methodically assess the clinical effectiveness and safety of robot-assisted total rectal mesenteric resection (RTME), laparoscopic-assisted total ...

Radiomics in colorectal cancer patients.

The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation...