View clinical trials related to Colonic Polyps.
Filter by:The primary goal of this study is to estimate the effectiveness of a medical decision support system based on artificial intelligence in the endoscopic diagnosis of benign tumors. Researchers will compare Adenoma detection rate between "artificial intelligence - assisted colonoscopy" and "conventional colonoscopy" groups to evaluate the clinical effectiveness of artificial intelligence model.
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
Colonoscopy is the current standard method for evaluation of colonic disorders such as colorectal cancer, IBD, polyps, and other conditions.
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. 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 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