View clinical trials related to Polyp of Colon.
Filter by:Accurate classification of growths in the large bowel (polyps) identified during colonoscopy is imperative to inform the risk of colorectal cancer. Reliable identification of the cancer risk of individual polyps helps determine the best treatment option for the detected polyp and determine the appropriate interval requirements for future colonoscopy to check the site of removal and for further polyps elsewhere in the bowel. Current advanced endoscopic imaging techniques require specialist skills and expertise with an associated long learning curve and increased procedure time. It is for these reasons that despite being introduced in clinical practice, uptake of such techniques is limited and current methods of polyp risk stratification during colonoscopy without Artificial intelligence (AI) is suboptimal. Approximately 25% of bowel polyps that are removed by major surgery are analysed and later proved to be non-cancerous polyps that could have been removed via endoscopy thus avoiding anatomy altering surgery and the associated risks. With accurate polyp diagnosis and risk stratification in real time with AI, such polyps could have been removed non-surgically (endoscopically). Current Computer Assisted Diagnosis (CADx, a form of AI) platforms only differentiate between cancerous and non cancerous polyps which is of limited value in providing a personalised patient risk for colorectal cancer. The development of a multi-class algorithm is of greater complexity than a binary classification and requires larger training and validation datasets. A robust CADx algorithm should also involve global trainable data to minimise the introduction of bias. It is for these reasons that this is a planned international multicentre study. The Investigators aim to develop a novel AI five class pathology prediction risk prediction tool that provides reliable information to identify cancer risk independent of the endoscopists skill. These 5 categories are chosen because treatment options differ according to the polyp type and future check colonoscopy guidelines require these categories
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.
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. It is hypothesized that 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. It is also hypothesized 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 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.
The present trial is aimed at evaluating whether in individuals scheduled for colonoscopy in the framework of a structured FIT (Fecal Immunochemical stool test)-based colorectal cancer screening program, the combination of an AI (artificial intelligence) system (CADEYE) with a mucosal exposure device (G-EYE 760R endoscope) increases the identification of subjects at high risk to develop colorectal cancer (according to recent ESGE-European Society of Gastrointestinal Endoscopy guidelines subjects are labelled as "high-risk" if harboring at least 1 adenoma ≥ 10 mm or with high grade dysplasia, or ≥ 5 adenomas, or any serrated polyp ≥ 10 mm or with dysplasia) when compared to colonoscopy performed with the support of AI only. Individuals fulfilling inclusion criteria are randomized (1:1) to two different arms (Control arm and Interventional arm, see below). Randomization is based on a computer-generated randomized block sequence, stratified according to age (50-61 vs. 62-74) and gender (male vs. female); size of the blocks (10 individuals) is not communicated to the investigator. Allocation is concealed and kept in a sealed envelope, which is opened just before starting colonoscopy. Individuals randomized in the Intervention arm receive colonoscopy examination with G-EYE 760R colonoscopes; once the cecum is reached the balloon is inflated, and the endoscope is withdrawn with the inflated balloon; the colonoscopy is performed with the support of the CADEYE system for polyp detection in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination. Individuals randomized in the Control arm (CADEYE only) receive colonoscopy with G-EYE 760R colonoscope but the balloon remains deflated for the entire procedure; the colonoscopy is performed with the support of the CADEYE system for polyp detection in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination. The main outcome measure is the rate of "high risk" individuals across the two study arms.
Ultivision AI is a computer-assisted detection (CADe) device intended to aid endoscopists in the real-time identification of colonic mucosal lesions (such as polyps and adenomas). Ultivision AI CADe is indicated for white light colonoscopy only.
The EAGLE study is a prospective randomized controlled multicenter parallel design trial, for the assessment of clinical performance of the CADDIE device and to confirm that the device performs as expected.
This is a prospective, multicenter, randomized study to evaluate the clinical performance of a novel CADe device, WISE VISION® Endoscopy System, in patients undergoing high-definition white light (HDWL) colonoscopy for screening or surveillance of colorectal Cancer (CRC). Eligible subjects who meet the study inclusion/exclusion criteria will be randomized in a 1:1 ratio to undergo colonoscopy : - Experimental: CADe colonoscopy procedure with WISE VISION® Endoscopy (CADe Group) - Control: Standard Colonoscopy without CADe (Standard Colonoscopy Group)
Size of colorectal polyps is important to decide on appropriate surveillance intervals and treatment modality, as well as carrying out optical diagnosis strategies. However, polyp size measurement is often prone to inter-observer variability. An easy and accurate tool to assist in polyp size measurement is required. Recently, a virtual scale function for size measurement during endoscopy (SCALE EYE), operating in real-time without the use of any additional devices, has been developed. The aim of this study is to assess whether use of the SCALE EYE for polyp size measurement can reduce inter-observer variability.
Colonoscopy completion by caecal intubation seldom represents a significant effort for the endoscopist. In this situation, additional techniques are necessary to achieve this goal: patients' manual abdominal compression, postural changes, and endoscopist relay. To date, no tool allows colonoscopy technical difficulty grading. This study pursues to describe the frequency of additional techniques for caecal intubation in a large sample of Argentinians in different centres who undergo colonoscopy for attending purposes, to develop a novel score for assessing colonoscopy technical difficulty.