Gastroenterology

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In th...

Improving CNN training on endoscopic image data by extracting additionally training data from endoscopic videos.

In this work we present a technique to deal with one of the biggest problems for the application of convolutional neural networks (CNNs) in the area of computer assisted endoscopic image diagnosis, the insufficient amount of training data. Based on patches from endoscopic images of colonic polyps with given label information, our proposed technique acquires additional (labeled) training data by tr...

[Artificial intelligence in gastroenterology].

Artificial intelligence (AI) is currently transforming all aspects of our daily life, including the practice of medicine. Artificial neural networks a...

A review of water exchange and artificial intelligence in improving adenoma detection.

Water exchange (WE) and artificial intelligence (AI) have made critical advances during the past decade. WE significantly increases adenoma detection ...

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map.

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. As...

Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images.

PURPOSE: The main purpose of this study is to construct a system to track the tumor position during radiofrequency ablation (RFA) treatment. Existing ...

Robotic Hepatectomy Is a Safe and Cost-Effective Alternative to Conventional Open Hepatectomy: a Single-Center Preliminary Experience.

Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.

Type II diabetes mellitus (T2DM) is a significant public health concern with multiple known risk factors (, body mass index (BMI), body fat distributi...

A deep learning nomogram kit for predicting metastatic lymph nodes in rectal cancer.

BACKGROUND: Preoperative diagnoses of metastatic lymph nodes (LNs) by the most advanced deep learning technology of Faster Region-based Convolutional ...

Peginterferon and Entecavir Combination Therapy Improves Outcome of Non-Early Response Hepatitis B e Antigen-Positive Patients.

BACKGROUND: The efficacy of nucleot(s)ide analogs (NAs) and pegylated interferon (PegIFN) combination therapy for hepatitis B e antigen-positive (HBeA...

A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy.

BACKGROUND : Accurate identification of the differentiation status and margins for early gastric cancer (EGC) is critical for determining the surgical...

Comments on: Lower Incidence of Postoperative Pulmonary Complications Following Robot-Assisted Minimally Invasive Esophagectomy for Esophageal Cancer: Propensity Score-Matched Comparison to Conventional Minimally Invasive Esophagectomy.

Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches.

BACKGROUND: Differentiating between ulcerative colitis (UC), Crohn's disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We...

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer v...

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lun...

The relation of CT quantified pancreatic fat index with visceral adiposity and hepatic steatosis.

OBJECTIVES: The purpose of this study was to investigate the relation between pancreatic steatosis and visceral adiposity. Furthermore, the study soug...

Robot-assisted laparoscopic extravesical versus conventional laparoscopic extravesical ureteric reimplantation for pediatric primary vesicoureteric reflux: a systematic review and meta-analysis.

To perform a systematic review and meta-analysis comparing the outcomes of robotic-assisted laparoscopic extravesical ureteric reimplantation (RALUR) ...

An invited commentary on "Robotic versus laparoscopic gastrectomy for gastric cancer: The largest meta-analysis".

Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach.

A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of micro...

A near-infrared fluorescence probe for imaging of pantetheinase in cells and mice .

Pantetheinase is an amidohydrolase that cleaves pantetheine into pantothenic acid and cysteamine. Functional studies have found that ubiquitous expres...