Colonic Polyp

Management of unexpected malignant colorectal polyps removed endoscopically can be challenging due to the risk of residual tumor and lymphatic spread. International studies have shown that in patients choosing surgical management instead of watchful waiting, 54-82% of bowel resections are without evidence of residual tumor or lymphatic spread. As surgical management entails risks of complications and watchful waiting management entails risks of residual disease or recurrence, a clinical dilemma arises when choosing a management strategy. Shared decision making (SDM) is a concept that can be used in preference sensitive decision making to facilitate patient involvement, empowerment, and active participation in the decision making process. This is a clinical multicenter, non-randomized, interventional phase II study involving Danish surgical departments planned to commence in the first quarter of 2024. The aim of the study is to examine whether shared decision making and using a patient decision aid (PtDA) in consultations affects patients' choice of management compared with historical data. The secondary aim is to investigate Patient Reported Experience Measures (PREMs) and Patient Reported Outcome Measures (PROMs) using questionnaire feedback directly from the patients.

Background: Removal of adenomatous polyps during colonoscopy is associated with long-term prevention of colorectal cancer-related deaths. Recently, there have been much interest in the use of artificial intelligence (AI) platforms to augment the routine endoscopic assessment of the colon to enhance adenoma detection rate (ADR). To date, computer assisted detection of polyps (CADe) have been shown to be safe, with a significant increase in ADR, without any concomitant increase in post-procedural complications. Aims: The investigators aim to evaluate the use of GI GeniusTM Intelligent Endoscopy Module in a multi-ethnic Asian population (Singapore) to increase in ADR and adenoma detected per colonoscopy (ADPC)to justify its effectiveness as an adjunct in polyp detection and training for colonoscopy. Methods: This study will be a single-institution cohort study, conducted over a 2-year period. Sengkang General Hospital (SKH) does an estimated 12,500 colonoscopies per year, with an average of 1,040 colonoscopies performed every month. Thus, given the case volume, the investigators expect to detect differences in ADR amongst endoscopists if any during this study period. As part of the subgroup analysis, the investigators also aim to compare the ADR rates of trainee endoscopists with and without the GI GeniusTM Intelligent Endoscopy Module to ascertain its utility as an education tool/training adjunct

The goal of this prospective, randomized, controlled study is to compare remimazolam besilat/sufentanyl and propofol/sufentanyl in patients during colonoscopic polypectomies procedures. Patients undergoing colonoscopic polypectomies in procedural sedation using remimazolam besylate/sufentanyl are circulatory and respiratory as or more stable when compared with propofol/sufentanyl sedation.

This study is a multi-center, randomized controlled trial designed to evaluate whether an artificial intelligence (AI) system can assist endoscopists to improve the detection rate of colorectal adenomas and cancers during colonoscopy compared to standard colonoscopy. Early screening and diagnosis are key to reducing the burden of colorectal cancer, but current colonoscopy has limitations, including the risk of missed lesions. This trial aims to determine if AI can enhance screening quality and diagnostic accuracy.

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.

Training in endoscopy is essential for the early detection of precursors of colorectal cancer. Up to now, this training has been carried out with image collections of findings and in practice when working on patients. The investigators want to use artificial intelligence (AI) to better train doctors to recognise these precursors. By using generative AI, the investigators were able to create realistic images that comply with data protection regulations and whose content can be predefined. Parts of the image can also be regenerated so that it is possible to create different precancerous stages in the same place in the image. In this study the investigators want to train physicians using real images or artificial images in order to compare which version helps classify polyps better.

Cancer can develop in the colon, or large bowel. Examination of the colon with a tube fitted with a camera is called a colonoscopy. Colonoscopy allows detection of small growths in the colon, called "polyps". Polyps can often be removed during colonoscopy. Some of these polyps are called adenomas and can become cancer after several years. A good colonoscopy aims to find and take out as many of these polyps as possible. A quality indication of colonoscopy is the "adenoma detection rate" (ADR). It should be high, meaning many polyps are detected and taken out. New artificial intelligence devices to assist colonoscopy seem to increase the ADR, and maybe help prevent cancer even better than normal colonoscopy. The goal of this clinical trial is to compare the ADR when using standard colonoscopy to the ADR with artificial intelligence (AI)-assisted colonoscopy.

Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care. Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.

This protocol describes a prospective cohort study. It addresses an important challenge in the prevention of colorectal cancer and duodenal cancer: how to safely and effectively remove large polyps.

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.

Colonoscopic removal of polyps is an important and well-established tool in the prevention of colorectal cancers. However, high polyp recurrence rates after endoscopic resection, with resultant development of interval cancers, remains a problem; this most commonly stems from unrecognised incomplete polyp resection. Thus, a standardised endoscopic technique is needed that will allow endoscopists to consistently achieve a clear margin of resection. The investigators believe the Cap Assisted Resection Margin Assessment (CARMA) technique will address this problem. This novel technique focuses on a standardised assessment of the resection margin after endoscopic polypectomy utilising available standard high-definition video endoscopes with imaging features including narrow band imaging (NBI) and magnification endoscopy.