View clinical trials related to Endoscopy.
Filter by:This is an observational study in which images (photos and/or videos of lesions found during endoscopy) will be collected and the associated data about these lesions (size, location in the body, outcome of histology, if resected and examined). The images will be taken while performing a diagnostic or therapeutic endoscopy. This footage can be recorded in different light modalities, and various lesions can be removed during one procedure. It is of importance that the images are recorded in the best possible image quality. The images can be either endoscopic images or endoscopic images combined with room view (this means that the endoscopic room can be filmed, for example, to visualize the endoscopist and thus show techniques of the procedure, without the patient being identified). The GDPR regulations will be respected at all times during the video recordings. The purpose of this registry is to create a database with images in the form of photos and video clips, histopathological data, demographic data of patients and data about the endoscopic procedure. The recording of this data is standard with every endoscopic procedure that patients undergo at UZ Gent. The data will be used for the following purposes: linking procedural outcomes with procedural data and patient data to improve endoscopic technique and to guarantee quality in our endoscopic unit. In this way we will also be able to identify trends and link adverse events back to the patient data with the aim of informing the patient. In addition, as a university center, we can train doctors in training with this educational material. The footage can be shared via online and/or live presentations that are only accessible to/by healthcare workers and without the patient being identifiable. Such material and its dissemination is essential to improve (the safety of) techniques (such as those of today), to share knowledge about techniques, and in this way to train the next generation of doctors and nurses.
The purpose of this pilot clinical trial is to confirm the efficacy and safety of Nexpowder™ for hemostasis in pilot cohort of patients with NVUGIB in Singapore
In the stomach, the ghrelin-containing cells are more abundant in the fundus than in the pylorus originally termed X/A-like cells. These X/A-like cells account for approximately 20 % of the endocrine cell population in adult oxyntic glands. Ghrelin enhances the secretion of growth hormone, the stimulation of appetite and food intake, the modulation of gastric acid secretion & motility and the endocrine and exocrine pancreatic secretions.
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.
Various complications are the leading cause of morbidity in sedation practices in endoscopic procedures, and guidelines recommend continuous monitoring of circulation, respiratory function, and ventilation. Integrated Pulmonary Index (IPI), one of the methods that can be used in this monitoring, gives a single numerical value obtained by continuous and simultaneous joint mathematical analysis of Oxygen saturation, End-tidal carbon dioxide concentration, respiratory rate, and heart rate values and is a good monitorization in these interventions. and provides tracking. In this study, the aim is to determine the role of IPI in the diagnosis and follow-up of respiratory complications in patients who were sedated during gastroscopy procedures.
This study is intended to document clinical effectiveness and safety pertaining to use of a new endoscopic clipping device MANTIS™ when used for hemostasis, closure, anchoring and marking.
In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for endoscopy report quality in endoscopists. The subjects would be divided into two groups. For the collected endoscopic videos, group A would complete the endoscopy report with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the upper gastrointestinal tract is divided into 26 parts). Group B would complete the endoscopy report without special prompts. After a period of forgetting, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the endoscopy report. Then, the completeness of the report lesion, the accuracy of the lesion location, the completeness of the lesion and the standard part in the captured images, and so on were compared with or without AI assistance.
The operative link on gastric intestinal metaplasia assessment (OLGIM) staging systems using biopsy specimens were commonly used for histological assessment of gastric cancer risk. But its clinical application is limited for at least biopsy samples. The endoscopic grading system (EGGIM) has been shown a significant correlation with the OLGIM. The investigators designed a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores in endoscopy examination. This study is aimed at exploring the relevance of the EGGIM scores automatically evaluated by Artificial Intelligence and OLGIM scores.
Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). With technical advance of image-enhanced endoscopy (IEE), studies have demonstrated IEE has high accuracy for diagnosis of GIM. The endoscopic grading system (EGGIM), a new endoscopic risk scoring system for GC, have been shown to accurately identify a wide range of patients with GIM. However, the high diagnostic accuracy of GIM using IEE and EGGIM assessments performed all require much experience, which limits the application of EGGIM. The investigators aim to design a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores.
A growing number of Chinese breast cancer patients are diagnosed at a young age. The quality of life of young breast cancer patients has been a critical issue. Breast-conserving surgery (BCS) not only removes the tumor but also maintains the appearance of breast. The Breast Tumor Center of Sun Yat-sen Memorial Hospital is one of the first departments in China to perform breast-conserving surgeries. Endoscopic breast surgery has emerged as a promising surgical approach. However, it is hard to delineate the tumor margins in endoscopic BCS, which restrains its development. In traditional BCS, surgeons determine the tumor border by palpation, which is impossible in endoscopic BCS. For the first time, we performed the intra-operative navigation system-assisted endoscopic breast-conserving surgery, in which the tumor border was accurately delineated using the navigation system.