Active clinical trials for "Polyps"

Topical and systemic steroids constitute the first choice in medical treatment for nasal polyps. Glucocorticoids sensitivity is significantly correlated with CST1 level in nasal secretions. The goal of this single-arm clinical trial based on a multicenter platform is to test CST1 in patients with chronic rhinosinusitis and nasal polyps before and after oral glucocorticoid therapy. Endoscopic polyp score, Total Nasal Symptom Score(TNSS), SNOT-22 score and other biomarkers are also evaluated before and after the treatment. Researchers will develop a CST1 predictive model of oral glucocorticoid therapy for Chronic Rhinosinusitis with Polyps.

Endoscopic resection of pedicled polyps mainly focuses on how to prevent bleeding, and also needs to pay attention to the convenience of resection and the integrity of resection, which means that different endoscopic resection strategies should be adopted for pedicled polyps with different pedicle sizes. Small pedicled polyps with heads smaller than 20mm and pedicles smaller than 5mm are defined as having a relatively small risk of bleeding. Preliminary studies in recent years suggest that the use of cold snare polypectomy for small pedicled polyps may also be a safe resection strategy. However, for small pedicled polyps, ASGE and ESGE guidelines currently recommend hot snare polypectomy in the middle and lower pedicles (evidence level medium). Therefore, the provision of high-quality clinical evidence related to cold resection techniques in the resection strategy of small pedicled polyps may provide a basis for revision of guidelines.

Colonoscopic removal of polyps is an important and well-established tool in the prevention of colorectal cancers. However, high polyp recurrence rates after endoscopic resection, with resultant development of interval cancers, remains a problem; this most commonly stems from unrecognised incomplete polyp resection. Thus, a standardised endoscopic technique is needed that will allow endoscopists to consistently achieve a clear margin of resection. The investigators believe the Cap Assisted Resection Margin Assessment (CARMA) technique will address this problem. This novel technique focuses on a standardised assessment of the resection margin after endoscopic polypectomy utilising available standard high-definition video endoscopes with imaging features including narrow band imaging (NBI) and magnification endoscopy.

Hypoxia is the most common adverse event in gastrointestinal endoscopes sedated with propofol and sufentanil, especially in elderly people. The aim of this randomized study was to determine whether intervention based on additional capnographic monitoring reduces the incidence of hypoxia in gastrointestinal endoscopes procedures for elderly patients.

The aim of this study is to investigate if the use of artificial intelligence (AI) in colonoscopy improves the polyp detection rate, and if the use of AI has a learning effect.

Nowadays, colonoscopy is considered the gold standard for the detection of lesions in the colorectal mucosa. However, around 25% of polyps may be missed during the conventional colonoscopy. Based on this, new technological tools aimed to improve the quality of the procedures, diminishing the technical and operator-related factors associated with the missed lesions. These tools use artificial intelligence (AI), a computer system able to perform human tasks after a previous training process from a large dataset. The DiscoveryTM AI-assisted polyp detector (Pentax Medical, Hoya Group, Tokyo, Japan) is a newly developed detection system based on AI. It was designed to alert and direct the attention to potential mucosal lesions. According to its remarkable features, it may increase the polyp and adenoma detection rates (PDR and ADR, respectively) and decrease the adenoma miss rate (AMR). Based on the above, the investigators aim to assess the real-world effectiveness of the DiscoveryTM AI-assisted polyp detector system in clinical practice and compare the results between expert (seniors) and non-expert (juniors) endoscopists.

Colonoscopy is the gold standard for colorectal screening. The diagnostic accuracy of colonoscopy highly depends on the quality of inspection of the colon during the procedure. To increase detection new polyp detection systems based on artificial intelligence (AI) have been developed. However, these systems still depend on the ability of the endoscopist to adequately visualize the complete colonic mucosa, especially to detect smaller and more subtle lesions, or lesions hidden behind folds in the colon. With this study we want to combine a device to flatten the folds in the colon combined with an artificial intelligence system to further improve the detection rate of lesions during colonoscopy.

Evaluation of the colonic mucosa with a high definition colonoscope (EPKi7010 video processor). The endoscopy images will be seen on a 27inch, flat-panel, high-definition LCD monitor (Radiance™ ultraSC-WU27-G1520 model) only by one expert endoscopist, randomly assigned. The number, location, and polyps' features (Paris classification) will be recorded by the operator. If a polyp is detected, the endoscopist will remove the polyp endoscopically with a cold snare. The same patient will be submitted to a second, the same session, computed aided real-time colonoscopy using the DISCOVERY, AI-assisted polyp detector. Colonoscopy will be performed by a same-level-of-expertise operator in comparison to the initial procedure. Any polyp or lesion detected with the AI system will be recorded and endoscopically removed and considered as a missed lesion from standard colonoscopy.

This study will evaluate the safety and effectiveness of the CapsoCam Colon (CV-3) endoscope system for the detection of colonic polyps.

Colonoscopy is the gold standard modality for the detection of colonic polyp. However, miss polyp occurs especially in right sided colon. Artificial intelligence (AI) is one of the modality to improve polyp detection but the benefit of AI in operators with different endoscopic experience is still limited. This study aimed to evaluate the efficacy of AI in the detection of right sided colonic polyp in operators with different endoscopic experience by using double insertion of right side colon, back-to-back basis.