View clinical trials related to Polyps.
Filter by: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).
The aim of this research is to evaluate autophagy markers in patients with endometrial polyps
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.
This prospective case-control study aimed to analyze and summarize the high-risk factors and susceptible genes of patients with colorectal polyps. According to these high-risk factors, the investigators developed and validated a prediction model for colorectal polyps to identify high-risk individuals, in order to provide clinical basis for the etiology research and the establishment of effective preventive measures.
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.
Two Millimetres needlescopic instruments induce minimal damage to the abdominal wall and have excellent cosmetic results. However, these instruments are fragile and expensive with short weak jaws. The aim of this study is to present a novel needlescopic approach using 1.6-mm Suture Grasper Device [SGD], modified polypectomy snare and a home-made Snare (HMS) for the treatment of congenital inguinal hernias [CIH] in girls. Over a period of one year from March 2018 to March 2019 a prospective study was conducted in three tertiary centres on 53 girls presented with CIH. Preoperative inguinoscrotal U/S was done for all patients to confirm the diagnosis and to measure the diameter of internal inguinal ring [IIR]. All patients were repaired using needlescopic inversion and snaring of the hernia sac using 2-SGDs and a snare. Follow up period ranged from 12 to 24 (Median 16.5) months. Fifty-three girls with 74 hernias were included in this study. Their mean age was 37.8 months. Internal inguinal ring diameter (IIR) ranged between 8-15 mm with a mean of 11.8±2.8mm. Mean operative time was 15.5 minutes in bilateral and 11.4 minutes in unilateral cases. Mean operative time for inversion, snaring, and sac extraction was 4.2±1.3 minutes. All cases were completed successfully without conversion and without complications. Follow up period ranged from 12 to 24 (Median 16.5) months with non-visible scar and no recurrence among the studied patients. Needlescopic inversion and snaring of inguinal hernia using 1.6mm instruments is a safe, rapid and feasible method for CIH repair in girls with invisible scar and no short-term recurrence.
Recently, a CNN-based artificial intelligence (AI) system for polyp characterization has been developed by Fujifilm Co., Tokyo, Japan. It works in conjunction with BLI system. In the present study we prospectively evaluate whether the evaluation of the endoscopist combined with the CAD system output achieve > 90% accuracy in characterization (i.e. as adenomas or non-adenomas) of diminutive rectosigmoid polyps having histopathology as reference standard. Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (<5 mm) rectosigmoid polyp is detected are included. During endoscopic procedures all polyps identified by the endoscopist are documented for size, location and morphology. All diminutive polyps are characterized by a three sequential steps process: I) endoscopist prediction: the endoscopist evaluates the polyp by using BLI through the BASIC classification; the confidence level (high vs. low) in histology prediction is recorded; II) AI prediction: the AI system is switched on and the output of the automatic evaluation is recorded; this outcome is rated as stable or unstable, depending of the consistency over time of the outcome; III) combined prediction: a final classification is provided by endoscopist in light of the results of the first and of the second step; the confidence level is recorded. All polyps are resected and retrieved in separate jars and sent for pathology assessment. Only polyps characterized with high confidence will be included in the per-polyp analysis; the high-confidence characterization rate will be also calculated; the rate of polyps characterized with a CAD stable outcome will be calculated. Operative characteristics (sensitivity, specificity, positive and negative predictive value and accuracy) in distinguishing adenomatous from non-adenomatous polyps, evaluated with high confidence, will be calculated for each diminutive polyp and for each diminutive rectosigmoid polyp, having histopathology report as reference standard. The post-polypectomy surveillance intervals will be calculated on the basis of polyp histology (reference standard) in all patients according to both USMSTF and ESGE guidelines.
This is a randomized, double blind, placebo controlled, parallel group phase III study designed to assess the clinical efficacy and safety of 100 milligrams (mg) subcutaneous (SC) mepolizumab treatment in adults with CRSwNP/ECRS for the purpose of registration in Japan and China. Approximately 160 participants will be randomized in a 1:1 ratio to receive either 100 mg SC mepolizumab or placebo SC. The study will include a 4-week run-in period followed by randomization to a 52-week treatment period, where participants will be administered 4-weekly doses of mepolizumab or placebo via a pre-filled safety syringe device (SSD) injection.
The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).