View clinical trials related to Colonic Polyps.
Filter by:The goal of this observational study is to assess the correlation between the artificial intelligence (AI) derived effective withdrawal time (EWT) during colonoscopy and endoscopists' baseline adenoma detection rate (ADR). The association between the AI derived EWT with ADR during the prospective colonoscopy series would also be determined. The colonoscopy video of participants will be monitored by the AI
To investigate the role of different types of laxatives (compounded polyethylene glycol electrolyte dispersions and compounded sodium pico-sulfate) on the composition, evolution and recovery of the gut microbiome of patients with colonic polyps undergoing bowel preparation.
Background: Accurate labeling of obstruction site on upright abdominal radiograph is a challenging task. The lack of ground truth leads to poor performance on supervised learning models. To address this issue, self-supervised learning (SSL) is proposed to classify normal, small bowel obstruction (SBO), and large bowel obstruction (LBO) radiographs using a few confirmed samples. Methods: A few number of confirmed and a large number of unlabeled radiographs were categorized based on the ground truth. The SSL model was firstly trained on the unlabeled radiographs, and then fine-tuned on the confirmed radiographs. ResNet50 and VGG16 were used for the embedded base encoders, whose weights and parameters were adjusted during training process. Furthermore, it was tested on an independent dataset, compared with supervised learning models and human interpreters. Finally, the t-SNE and Grad-CAM were used to visualize the model's interpretation.
The aim of the clinical trial is to investigate whether the use of a new multichannel endoscopic transanal access device (named UNI-VEC) is safe and effective in the resection of a rectal polyp or tumor that sits in the distal part of the colon (up to about 20 cm from the anal margin). This is the first study to test the device in humans, after proving its good performance in preclinical development (preclinical development has included functional laboratory tests and an animal trial).
Large (≥20mm) colorectal polyps often harbor areas of advanced neoplasia, making them immediate colorectal cancer (CRC) precursors. Such polyps have to be completely removed to prevent CRC and to avoid surgery and/or adjuvant therapy. The laterally spreading lesions (LSLs) are removed via endoscopic mucosal resection (EMR). However, recurrence is common. Recent studies have found that the use of hybrid argon plasma coagulation (h-APC) for the ablation of the margin and base of resection post-EMR could significantly reduce the recurrence rate, and complete closure of the post-EMR defect can prevent other adverse pre- and post-procedure outcomes such as bleeding. We hypothesize that performing hybrid argon plasma coagulation (h-APC) margin and base ablation post-EMR for large (≥20mm) colorectal LSLs will demonstrate a lower recurrence rate compared to Snare Tip Soft Coagulation (STSC) margin ablation. We also hypothesize that performing complete closure of the EMR defect will result in lower rates of adverse events compared to cases where no defect closure is performed.
The aim of the study is to assess whether the use of artificial intelligence improves polyp detection in a segment of the colon (the right colon). To achieve this objective, patients will be divided into two groups: one will undergo a standard colonoscopy, the other a colonoscopy with the artificial intelligence software connected to the machine. This software does not modify the colonoscopy technique in any way, and does not require the administration of any product to the patient. The study will compare the detection rate of right colon polyps between the group of patients who underwent standard colonoscopy and those who underwent colonoscopy with artificial intelligence. If this number does not differ between the two groups, the investigators can conclude that there is no point in using artificial intelligence.
OBJECTIVES The aim of the study is to compare the efficacy of cold snare EMR versus hot snare EMR for non-pedunculated polyps 10-20mm in size with respect to complete resection rates and adverse events. DESIGN : A Randomised interventional study. Sample size: 330
The goal of this clinical trial is to evaluate the diagnostic yield of CADe in a consecutive population undergoing colonoscopy. The main question it aims to answer is the Adenoma Detection Rate (ADR). Participants undergoing colonoscopy for follow-up in a screening setting will be randomized in a 1:1 ratio to either receive Computer-Aided Detection (CADe) colonoscopy or a conventional colonoscopy (CC). GI Genius is the AI software that will be used in the present trial and is intended to be used as an adjunct to colonic endoscopy procedures to help endoscopists to detect in real time mucosal lesions (such as polyps and adenomas, including those with flat (non-polypoid) morphology) during standard screening and surveillance endoscopic mucosal evaluations. It is not intended to replace histopathological sampling as a means of diagnosis.Researchers will compare the CADe group and the CC-group to see if CAD-e can increase the ADR significantly.
Purpose & Research Questions The purpose of this study is to evaluate whether artificial intelligence (AI) improves the detection of polyps and whether the system can classify the type and severity of detected changes. The investigators will also assess if there are any differences between the various AI systems and whether the polyps that may be missed are benign or malignant.
The goal of this observational study is to assess the correlation between the artificial intelligence (AI) derived effective withdrawal time (EWT) during colonoscopy and endoscopists' baseline adenoma detection rate (ADR). The association between the AI derived EWT with ADR during the prospective colonoscopy series would also be determined. The colonoscopy video of participants will be monitored by the AI and the result of EWT will be blinded to the endoscopists