View clinical trials related to Colonic Polyps.
Filter by:This study will be a prospective analysis conducted by Geneoscopy Inc. to evaluate the Colosense test, which is a multi-target stool RNA test for colorectal screening.
COLO-DETECT is a clinical trial to evaluate whether an Artificial Intelligence device ("GI Genius", manufactured by Medtronic) can identify more polyps (pre-cancerous growths of the bowel lining) during colonoscopy (large bowel camera test) than during colonoscopy without it.
The colonoscopy procedure involves insertion of a thin, flexible tube with a tiny camera inside (colonoscope) passed inside the bowel. To allow passage of the colonoscope and adequate visualisation of the lining of the bowel wall a range of techniques can be used. During colonoscopy, you can distend the colon with water, CO2 and air. Air is no longer recommended for gas insufflation during colonoscopy as it causes pain and excess bowel distention. So the options are water and/or CO2 but it is not entirely clear which combination is the best and at what point during the colonoscopy. In practice, a hybrid technique where both CO2 and water are used during the colonoscopy in used. Here, water is exclusively used to help navigate the sigmoid colon with air pockets suctioned and turbid water exchanged with clean water. From splenic flexure to caecum a mixture of water and CO2 is used. The aim of this study is to assess procedure comfort and efficiency of two different colonoscopy insertion techniques: water-alone insertion of the colonoscope (gas insufflation not allowed on insertion; water exchange technique) versus water-CO2 hybrid insertion (water used predominately to splenic flexure with water/CO2 used to caecum; modified water immersion technique).
This is a prospective feasibility study. The aim of this work is to assess the acceptability and feasibility of optical diagnosis-led care in bowel cancer screening patients undergoing colonoscopy. This study will determine whether bowel cancer screening colonoscopists are able to consistently record and diagnose diminutive adenomas suitable for a resect and discard strategy allowing assignment of surveillance intervals according to Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) criteria. A practical quality assurance program around optical diagnosis will be introduced. The use of a CAD polyp-detection system will also be evaluated (AI-DETECT).
PURPOSE OF PROTOCOL Objective: To evaluate if the speedometer based on the real-time image analysis can help endoscopists increase their withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, during colonoscopy. Hypothesis: The trial hypothesis is that use of the speedometer during colonoscopy will increase the average withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, by 1.6 minutes, possibly increasing the performance of the participating endoscopists. Our objective is to clarify the clinical benefits of this digital tool in colonoscopy. Endpoint: Withdrawal time difference between colonoscopies done without the speedometer (control period) and colonoscopies done with the speedometer (intervention period).
Colonoscopy is the gold standard for detection and removal of precancerous lesions, and has been amply shown to reduce mortality. However, the miss rate for polyps during colonoscopies is 22-28%, while 20-24% of the missed lesions are histologically confirmed precancerous adenomas. To address this shortcoming, the investigators propose a new polyp detection system based on deep learning, which can alert the operator in real-time to the presence and location of polyps during a colonoscopy. The investigators dub the system DEEP: (DEEP) DEtection of Elusive Polyps. The DEEP system was trained on 3,611 hours of colonoscopy videos derived from two sources, and was validated on a set comprising 1,393 hours of video, coming from a third, unrelated source. For the validation set, the ground truth labelling was provided by offline gastroenterologist annotators, who were able to watch the video in slow-motion and pause/rewind as required; two or three specialist annotators examined each video. This is a prospective, non-blinded, non-randomized pilot study of patients undergoing elective screening and surveillance colonoscopies using DEEP. The aim of the study is to: Assess the: 1. Number of additional polyps detected by the DEEP system in real time colonoscopy. 2. Safety by prospective assessment of the rate of adverse events during the study period attributed or not to the use of the DEEP system. 3. Stability of the DEEP system by measuring the rate of false positives (False Alarms) per colonoscopies 4 And to examine its feasibility and usefulness of in clinical practice by assessing the colonoscopist user experience while using the DEEP system in a 5 point scale.
The Italian screening program invites the resident population aged 50-74 for Fecal Immunochemical Test (FIT) every 2 years. Subjects who test positive are referred for colonoscopy. Maximizing adenoma detection during colonoscopy is of paramount importance in the framework of an organized screening program, in which colonoscopy represent the key examination. Initial studies consistently show that Artificial iIntelligence-based systems support the endoscopist in evaluating colonoscopy images potentially increasing the identification of colonic polyps. However, the studies on AI and polyp detection performed so far are mostly focused on technical issues, are based on still images analysis or recorded video segments and includes patients with different indications for colonoscopy. At the best of our knowledge, data on the impact on AI system in adenoma detection in a FIT-based screening program are lacking. The present prospective randomized controlled trial is aimed at evaluating whether the use of an AI system increases the ADR (per patient analysis) and/or the mean number of adenomas per colonoscopy in FIT-positive subjects undergoing screening colonoscopy. Therefore Patients fulfilling the inclusion criteria are randomized (1:1) in two arms: A) patients receive standard colonoscopy (with high definition-HD endoscopes) with white light (WL) in both insertion and withdrawal phase; all polyps identified are removed and sent for histopathology examination; B) patients receive colonoscopy examinations (with HD endoscopes) equipped with an AI system (in both insertion and withdrawal phase); all polyps identified are removed and sent for histopathology examination. In the present study histopathology represents the reference standard.
Deep learning technology has an increasing role in medical image applications and, recently, an artificial intelligence device has been developed and commercialized by Medtronic for identification of polyps during colonoscopy (GI-GENIUS). This kind of computer-aided detection (CADe) devices have demonstrated its ability for improving polyp detection rate (PDR) and the adenoma detection rate (ADR). However, this increase in PDR and ADR is mainly made at the expense of small polyps and non advanced adenomas. Colonoscopies after a positive fecal immunochemical test (FIT) could be the scenario with a higher prevalence of advanced lesions which could be the ideal situation for demonstrating if these CADe systems are able also to increase the detection of advanced lesions and which kind of advanced lesions are these systems able to detect. The CADILLAC study will randomize individuals within the population-based Spanish colorectal cancer screening program to receive a colonoscopy where the endoscopist is assisted by the GI-GENIUS device or to receive a standard colonoscopy. If our results are positive, that could suppose a big step forward for CADe devices, in terms of definitive demonstration of being of help for efectively identify also advanced lesions.
Complete polypectomy is one of the major factors for effectiveness of colonoscopy to prevent colon cancer. Given the prevalence of the 4-6 mm polyp, and the concern about interval cancers at polypectomy sites, there is a clear and significant need to determine which technique(s) are most appropriate for clinical practice. This study was to compare the three commonly used polypectomy techniques in terms of efficacy and efficiency.
This prospective observational study will evaluate the performance of the DiLumen C2 Endolumenal Interventional Platform and its instruments. Up to 100 subjects will be enrolled at up to 5 clinical sites. Patient data will be collected before the procedure, during the procedure, and up until the patient is discharged from the hospital.