Peptic Ulcer Disease

Test clamp procedure in robot-assisted partial nephrectomy: is it a safe procedure?

We performed test clamp procedure in robot-assisted partial nephrectomy (RAPN) to prevent massive bl...

Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial.

BACKGROUND: White light endoscopy is a pivotal first-line tool for the detection of gastric neoplasm...

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of ele...

Predicting intraoperative bleeding in patients undergoing a hepatectomy using multiple machine learning and deep learning techniques.

Artificial intelligence for the detection of gastric precancerous conditions using image-enhanced endoscopy: What kind of abilities are required for application in real-world clinical practice?

Self-assembly of diclofenac prodrug into nanomicelles for enhancing the anti-inflammatory activity.

Non-steroidal anti-inflammatory drugs (NSAIDs) are widely prescribed for the treatment of various ty...

Severe intraoperative bleeding predicts the risk of perioperative blood transfusion after robot-assisted radical prostatectomy.

To evaluate potential factors associated with the risk of perioperative blood transfusion (PBT) with...

A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial.

INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy qua...

Ensuring patient and public involvement in the transition to AI-assisted mental health care: A systematic scoping review and agenda for design justice.

BACKGROUND: Machine-learning algorithms and big data analytics, popularly known as 'artificial intel...

Kvasir-Capsule, a video capsule endoscopy dataset.

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule en...

Deep learning for registration of region of interest in consecutive wireless capsule endoscopy frames.

BACKGROUND AND OBJECTIVE: Functional gastrointestinal disorders (FGIDs) are reported as worldwide ga...

Enabling Autonomous Colonoscopy Intervention Using a Robotic Endoscope Platform.

OBJECTIVE: Robotic endoscopes have the potential to dramatically improve endoscopy procedures, howev...

Robot-assisted laparoscopic hysterectomy for endometrial cancer in a patient with Herlyn-Werner-Wunderlich syndrome.

Herlyn-Werner-Wunderlich syndrome, a rare Mullerian duct anomaly, includes a triad of uterine didelp...

Using machine-learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy.

BACKGROUND AND AIM: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions...

Recent progress of robotic head and neck surgery using a flexible single port robotic system.

We performed robotic neck surgery through a transoral or retroauricular approach (RA) using the DaVi...

Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.

IMPORTANCE: Anticipating the risk of gastrointestinal bleeding (GIB) when initiating antithrombotic ...

An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs.

OBJECTIVES: Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis,...

Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For h...

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning.

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how ...

State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020.

PURPOSE OF REVIEW: Recently numerous researchers have shown remarkable progress using convolutional ...

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation...