Gastroenterology

Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.

OBJECTIVES: In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as EP-SM1, whereas ESCC cases deeper than SM2 are treated by surgical operation or chemoradiotherapy. Therefore, it is crucial to determine the invasion depth of ESCC via preoperative endoscopic examination. Recently, rapid progress in the utilization ...

Deep pancreas segmentation with uncertain regions of shadowed sets.

Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have poor contrast and blurred boundaries. Second, there exist large variations in gray scale, texture, location, shape and size among pancreas images. It becomes even worse with cases of pancreatic cancer. Besides, as an inevitable phenomenon, some of t...

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.

Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the dis...

Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem.

Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming and studies do not reflect the complexity...

Stability of Pentobarbital Hydrogel for Rectal Administration in Pediatric Procedural Sedation.

Pentobarbital is a sedative agent to limit children motion during computed tomography or magnetic resonance imaging (MRI) and ensures the successful ...

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by...

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound im...

Xuetonglactones A-F: Highly Oxidized Lanostane and Cycloartane Triterpenoids From Roxb. Craib.

Xuetonglactones A-F (-), six unreported highly oxidized lanostane- and cycloartane-type triterpenoids along with 22 known scaffolds (-) were isolated ...

Multiclass Classification of Hepatic Anomalies with Dielectric Properties: From Phantom Materials to Rat Hepatic Tissues.

Open-ended coaxial probes can be used as tissue characterization devices. However, the technique suffers from a high error rate. To improve this techn...

Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database.

BACKGROUND: Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance o...

Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

OBJECTIVES: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

Liver tissue classification of en face images by fractal dimension-based support vector machine.

Full-field optical coherence tomography (FF-OCT) has been reported with its label-free subcellular imaging performance. To realize quantitive cancer d...

Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.

Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatments. To assess...

Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.

PURPOSE: To develop and evaluate the performance of a fully-automated convolutional neural network (CNN)-based algorithm to evaluate hepatobiliary pha...

Technique for Robotic Transhiatal Esophagectomy.

Minimally invasive esophagectomy is increasing performed for cancers of the esophagus and gastroesophageal junction. This video demonstrates the setup...

Use of Artificial Intelligence-Based Analytics From Live Colonoscopies to Optimize the Quality of the Colonoscopy Examination in Real Time: Proof of Concept.

Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video).

BACKGROUND AND AIMS: The visual detection of early esophageal neoplasia (high-grade dysplasia and T1 cancer) in Barrett's esophagus (BE) with white-li...

Experimental Investigation into the Dynamics of a Radially Contracting Actuator with Embedded Sensing Capability.

Dynamics, control, and sensing are still challenges for pneumatically actuated soft actuators. We consider feasible solutions based on a radially cont...

Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video).

BACKGROUND AND AIMS: We assessed the preliminary diagnostic accuracy of a recently developed computer-aided detection (CAD) system for detection of Ba...

Robotic total mesorectal excision for mid-rectal cancer using the Senhance robotic platform - a video vignette.