Development of a Novel Real Time Computer Assisted Colonoscopy Diagnostic Tool for Colorectal Polyps: Lesion Diagnosis and Personalised Patient Management

简要总结

详细描述

The use of artificial intelligence in computer-assisted detection (CADe) to detect polyps (pre-cancerous growths) during colonoscopy is gaining increasing interest and acceptance with multiple devices already in the mainstream market. The Investigator know already from work in other countries that detecting more polyps results in a reduced risk of bowel cancer for the patient having the procedure, in the years following their colonoscopy (ie. pre-cancerous growths were detected and removed). This has formed the basis of national bowel cancer screening programmes. With increased detection of colorectal polyps, there is a growing need to correctly identify the nature of the polyp to inform the risk of colorectal cancer with the polyp detected and also the potential future risk to the patient. Accurate polyp diagnosis is also required to determine the correct mode or removal-whether this does require removal at all (leading to conservation of costs and resources in a challenging current climate), whether endoscopic removal is possible and if so by what procedure, whether surgery is required. Published data demonstrates that approximately one quarter of surgically removed colorectal polyps with patients undergoing major surgery were benign and therefore major surgery could have been avoided with these polyps removed endoscopically reducing the risk of complication and organ preservation for the patient. Current polyp diagnosis techniques involve the use and interpretation of specialist dyes and magnification endoscopes which come with gaining expertise expertise with an associated 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 AI is suboptimal. Current polyp diagnosis AI (CADx) algorithms are limited to smaller classification Current CADx platforms 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 multiclass 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 Prospective collection of data: This study will be conducted alongside usual patient care, but will require research staff to enter data onto a secure web-based report form (REDCAP database). This means that participants will undergo exactly the same procedure, with no differences and no extra visits or data, than would have otherwise have occurred. Participants will be those patients that have been scheduled to have a colonoscopy for the standard reasons. Patients will be invited in the usual way for colonoscopy. They may - where possible - be sent the PIS with their appointment letter (up to 6 weeks in advance). On arrival in the endoscopy unit, they will be approached by a member of the research team and given a copy of the PIS to read - up to an hour before their procedure. They will be provided face-to-face information and explanation, prior to written consent to allow their data to be collected in the database. As the study does not require any change or additional procedures, The investigator feel that an initial approach on arrival into the endoscopy unit will provide sufficient, appropriate time to consent, even if the PIS has not been read in advance (although it will be sent if possible). The only additional consideration will be the consent to recording of the video (no patient identifiable data will be transferred as part of this aspect). Once the colonoscopy has been completed, there will be no additional visits.

主要结局

次要结局

• Interobserver agreement of the endoscopists' prediction of histology of polyps during the annotation process.(36 months)
• Sub-analysis of the polyp characteristics focused on different gender and ethnicity.(36 months)
• This will be a sub analysis of an AI algorithm that is trained to predict polyp histology using the prospective data cohort.(36 months)
• Learned effects of AI augmented endoscopy on endoscopist optical diagnosis(36 months)