View clinical trials related to Polyps.
Filter by:Prevalence of aspirin-exacerbated respiratory disease (AERD) is 16% amongst patients suffering from chronic rhinosinusitis with nasal polyps (CRSwNP). The mechanisms underlying the observed dysregulation of pro and anti-inflammatory pathways in AERD are still not fully understood. To address this and also to identify potential factors characterizing the disease the investigators plan to prospectively collect blood samples, nasal secretions as well as nasal biopsies from allergic, non-allergic and AERD patients suffering from CRSwNP. Initially, polyps of aforementioned patients will be subjected to RNA sequencing analysis using microarray technology. Once distinct factors are identified in nasal polyp tissue, their presence will be assessed in nasal secretions and serum of the respective patients to investigate their potential role as biomarkers. Furthermore presence of these parameters will be confirmed in situ in biopsies by confocal microscopy. Knowledge about factors differently upregulated in polyp tissue from AERD may contribute to a better understanding of the underlying mechanism of the disease.
This will be a prospective randomized controlled trial comparing CO2 insufflation and WE in terms of right colon combined adenoma miss rate (AMR) and hyperplastic polyp miss rate (HPMR) by tandem inspection. It will be a single-site study conducted in Taiwan.
Bowel cancer is one of the most common cancers and the best method of diagnosing it is through endoscopic examination of the bowel (colonoscopy). Pre-cursors of bowel cancer are called polyps which can be detected and removed at the time of the colonoscopy. This reduces the chance of developing bowel cancer. There are different types of polyp ranging from completely harmless to those that may develop into cancer over time. Advances in technology mean more polyps are being detected and it is possible to predict the type of polyp. Therefore there is a new strategy in endoscopy whereby when a small polyp is detected, a prediction of polyp type is made, the polyp removed and then discarded rather than sending to the laboratory, thereby reducing costs to health services. In the hands of experts, accuracies in predicting polyp type is similar to when the polyp is removed and sent to the lab for analysis. Whilst experts can do this, non-experts cannot reach these standards and there is a need for effective training. The aim of the study is to compare the effectiveness of two training methods: Didactic face-to-face training and computer-based self-learning on the ability of trainees at predicting polyp type.
This clinical trial is being conducted to compare the efficacy and safety of two standard methods of polypectomy，Conventional Endoscopic Mucosal Resection（EMR）and Underwater Endoscopic Mucosal Resection（UEMR），for small colorectal polyps.
Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking. Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard. Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2) Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.
Colonoscopy screening is proven to reduce mortality rates for colorectal cancer, which relies on early detection and removal of colonic polyps. AmplifEYE is a FDA-approved device with a row of flexible detection arms attached to the tip of colonoscope which can separate colonic folds during scope withdrawal and is believed to improve polyp detection. Real-life clinical data on this relatively new device is lacking and this study aims to compare the adenoma and polyp detection rates in AmplifEYE-assisted colonoscopy versus standard colonoscopy.
This study is intended to observe the therapeutic effect of dydrogesterone on endometrial polyps and provide a reference for clinical treatment.
Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastrointestinal mucosa during ongoing endoscopy examination. It can predict the classification of Colorectal Polyps accurately. However this requires much experience, which limits the application of pCLE. The investigators designed a computer program using deep neural networks to differentiate hyperplastic from neoplastic polyps automatically in pCLE examination.
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown. Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.