Gastroenterology

A V-Net Based Deep Learning Model for Segmentation and Classification of Histological Images of Gastric Ablation.

Gastric motility disorders are associated with bioelectrical abnormalities in the stomach. Recently, gastric ablation has emerged as a potential therapy to correct gastric dysrhythmias. However, the tissue-level effects of gastric ablation have not yet been evaluated. In this study, radiofrequency ablation was performed in vivo in pigs (n=7) at temperature-control mode (55-80°C, 5-10 s per point)....

Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks.

The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound images of gastrocnemius medialis using a convolutional neural network(CNN). In the provided dataset images with both normal and increased echogenicity are present. The manually annotated dataset consisted of 591 images, from 200 subjects, 400 relative to ...

Assisting the Non-invasive Diagnosis of Liver Fibrosis Stages using Machine Learning Methods.

Fibrosis is a significant indication of chronic liver diseases often due to hepatitis C Virus. It is becoming a global concern as a result of the rapi...

Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology.

Artificial intelligence (AI) could change the practice of gastroenterology through its ability to both acquire and analyze information with speed, rep...

[Integration of production-university-research based on artificial intelligence for technological innovation and transformation in gastrointestinal surgery].

Medical artificial intelligence is a deeply integrated field of production-university-research. The collaborative innovation system of "industry, educ...

[Application of convolutional neural network to risk evaluation of positive circumferential resection margin of rectal cancer by magnetic resonance imaging].

To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection m...

Ontologies for Liver Diseases Representation: A Systematic Literature Review.

Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is conside...

Concordance Study in Hepatectomy Recommendations Between Watson for Oncology and Clinical Practice for Patients with Hepatocellular Carcinoma in China.

BACKGROUND: With the improvement in diagnostic imaging, perioperative care and surgical technique, the indications and complexity of liver resections ...

The Impact of Robotics in Learning Roux-en-Y Gastric Bypass: a Retrospective Analysis of 214 Laparoscopic and Robotic Procedures : Robotic Vs. Laparoscopic RYGB.

BACKGROUND: Proximal Roux-en-Y gastric bypass is commonly used to manage obesity, performed using laparoscopic or robot-assisted minimally invasive su...

Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation.

BACKGROUND: The diagnostic performance of CT for pancreatic cancer is interpreter-dependent, and approximately 40% of tumours smaller than 2 cm evade ...

Study on intelligent syndrome differentiation neural network model of stomachache in traditional Chinese medicine based on the real world.

Stomachache is not only disease name of Traditional Chinese medicine (TCM) but also the clinical symptom. It is a common and multiple diseases. TCM ha...

Comparative outcomes of robot-assisted minimally invasive versus open esophagectomy in patients with esophageal squamous cell carcinoma: a propensity score-weighted analysis.

Robots are increasingly used in minimally invasive surgery. We evaluated the clinical benefits of robot-assisted minimally invasive esophagectomy (RAM...

Emerging imaging techniques for acute pancreatitis.

Acute pancreatitis (AP) is caused by acute inflammation of the pancreas and adjacent tissue and is a common source of abdominal pain. The current CT a...

Advanced imaging techniques for chronic pancreatitis.

MRI and MRCP play an important role in the diagnosis of chronic pancreatitis (CP) by imaging pancreatic parenchyma and ducts. MRI/MRCP is more widely ...

[A fusion network model based on limited training samples for the automatic segmentation of pelvic endangered organs].

When applying deep learning to the automatic segmentation of organs at risk in medical images, we combine two network models of Dense Net and V-Net to...

Artificial intelligence in inflammatory bowel disease: current status and opportunities.

The rise and fall of the model for end-stage liver disease score and the need for an optimized machine learning approach for liver allocation.

PURPOSE OF REVIEW: The Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates since 2002, and at the time bringing...

Radiomics and Deep Learning: Hepatic Applications.

Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the p...

Use of Artificial Intelligence to Reduce Radiation Exposure at Fluoroscopy-Guided Endoscopic Procedures.

OBJECTIVES: Exposure to ionizing radiation remains a hazard for patients and healthcare providers. We evaluated the utility of an artificial intellige...

Identification of Individuals at Increased Risk for Pancreatic Cancer in a Community-Based Cohort of Patients With Suspected Chronic Pancreatitis.

OBJECTIVES: We lack reliable methods for identifying patients with chronic pancreatitis (CP) at increased risk for pancreatic cancer. We aimed to iden...