Gastroenterology

Minimally invasive (laparoscopic and robot-assisted) versus open approach for central pancreatectomies: a single-center experience.

BACKGROUND: There continues to be an interest in minimally invasive approaches to pancreatic surgery...

Automatic liver segmentation using 3D convolutional neural networks with a hybrid loss function.

PURPOSE: Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamenta...

Risk of chyle leak after robotic versus video-assisted thoracoscopic esophagectomy.

BACKGROUND: We investigate the incidence and risk factors for post-operative outcomes including chyl...

High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis.

Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a ...

A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease.

Gastroesophageal reflux disease (GERD) is a common disease with high prevalence, and its endoscopic ...

Performance of a deep learning-based identification system for esophageal cancer from CT images.

BACKGROUND: Because cancers of hollow organs such as the esophagus are hard to detect even by the ex...

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer.

OBJECTIVE: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) i...

R-JaunLab: Automatic Multi-Class Recognition of Jaundice on Photos of Subjects with Region Annotation Networks.

Jaundice occurs as a symptom of various diseases, such as hepatitis, the liver cancer, gallbladder o...

Artificial intelligence for colonoscopy: the new Silk Road.

Establishment and clinical application value of an automatic diagnosis platform for rectal cancer T-staging based on a deep neural network.

BACKGROUND: Colorectal cancer is harmful to the patient's life. The treatment of patients is determi...

A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.

BACKGROUND: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). ...

Prediction of Recurrence in Patients with Stage III Colon Cancer Using Conventional Clinicopathological Factors and Peripheral Blood Test Data: A New Analysis with Artificial Intelligence.

BACKGROUND: Survival rate may be predicted by tumor-node-metastasis staging systems in colon cancer....

Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies.

Digital pathology platforms with integrated artificial intelligence have the potential to increase t...

Performance of a convolutional neural network for automatic detection of blood and hematic residues in small bowel lumen.

Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed?

The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and...

Propensity matched analysis of short term oncological and perioperative outcomes following robotic and thoracolaparoscopic esophagectomy for carcinoma esophagus- the first Indian experience.

Thoracolaparoscopic esophagectomy (TLE) for carcinoma esophagus has better short-term outcomes compa...

Deep-learning system for real-time differentiation between Crohn's disease, intestinal Behçet's disease, and intestinal tuberculosis.

BACKGROUND AND AIM: Pattern analysis of big data can provide a superior direction for the clinical d...

Surgical training model and safe implementation of robotic pancreatoduodenectomy in Japan: a technical note.

BACKGROUND: Growing evidence for the advantages of robotic pancreatoduodenectomy (RPD) has been demo...

A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution.

Colon carcinoma is one of the leading causes of cancer-related death in both men and women. Automati...

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent p...

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be h...