Gastroenterology

Entirely Robot-assisted Total Colectomy/Total Proctocolectomy Compared With a Laparoscopic Approach.

PURPOSE: Previous robot-assisted total colectomy/total proctocolectomy (TC/TPC) has been mostly confined to completion proctectomy combined with open or laparoscopic colon dissection. This study describes an integrative method of entirely robot-assisted maintaining anatomical dissection with single docking and one-off arm placement.

Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.

OBJECTIVES: To compare the diagnostic accuracy of texture analysis (TA)-derived parameters combined with machine learning (ML) of non-contrast-enhanced T1w and T2w fat-saturated (fs) images with MR elastography (MRE) for liver fibrosis quantification.

Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the ha...

Artificial intelligence-based detection of pharyngeal cancer using convolutional neural networks.

OBJECTIVES: The prognosis for pharyngeal cancer is relatively poor. It is usually diagnosed in an advanced stage. Although the recent development of n...

Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks.

Convolutional neural networks (CNNs), a popular type of deep neural network, have been actively applied to image recognition, object detection, object...

Comparison of Serum Concentrations of Ustekinumab Obtained by Three Commercial Assays in Patients with Crohn's Disease.

BACKGROUND: Data on the association of ustekinumab (UST) drug concentrations and clinical outcomes are conflicting. We assessed serum UST drug and ant...

Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.

Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt unde...

Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training.

High-quality annotations for medical images are always costly and scarce. Many applications of deep learning in the field of medical image analysis fa...

Deep learning in gastric tissue diseases: a systematic review.

BACKGROUND: In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable...

Robot-Assisted Laparoscopic Urachal Excision Using Hidden Incision Endoscopic Surgery Technique in Pediatric Patients.

Although laparoscopic urachal excision in children has been well reported, there are limited data on a robot-assisted surgery (RAS) approach. The hid...

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxi...

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.

Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-dise...

Hierarchical Analysis of Factors Associated with T Staging of Gastric Cancer by Endoscopic Ultrasound.

BACKGROUND: Size, ulcer, differentiation, and location are known to be factors affecting the T stage accuracy of EUS in gastric cancer. However, wheth...

Deeply self-supervised contour embedded neural network applied to liver segmentation.

OBJECTIVE: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomogra...

Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobioti...

Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure.

BACKGROUND: This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on...

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods throug...

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb.

The retention of a capsule endoscope (CE) in the stomach and the duodenal bulb during the examination is a troublesome problem, which can make the med...

Artificial intelligence as the next step towards precision pathology.

Pathology is the cornerstone of cancer care. The need for accuracy in histopathologic diagnosis of cancer is increasing as personalized cancer therapy...

A Novel Surgical Technique for Obstructed Megaureter: Robot-Assisted Laparoscopic Dismembered Extravesical Cross-Trigonal Ureteral Reimplantation-Short-Term Assessment.

To describe our experience with robot-assisted laparoscopic dismembered extravesical cross-trigonal ureteral reimplantation (RADECUR)-a novel minimal...