About this trial
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
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
Unable to provide informed consent.
Colitis Associated Dysplasia
Polyps at surgical anastomosis sites
Pregnancy
Trial population
All adult (over 18 years) presenting for screening or symptomatic colonoscopy
Trial design
Cohort
Prospective
Treatments tested in this trial
Colonoscopy
Diagnostic testNo intervention required from this study, however images will be obtained from patient presenting for colonoscopy
Treatment groups
Trial outcomes
Primary outcomes
To achieve an overall accuracy of 85% for the five-classification lesion prediction algorithm.
Sensitivity and Specificity
Positive and negative predicted value
Assess the accuracy to the trained device
Secondary outcomes
Interobserver agreement of the endoscopists' prediction of histology of polyps during the annotation process.
We will analyse the calculate the histology agreement between the advance endoscopist
Sub-analysis of the polyp characteristics focused on different gender and ethnicity.
To assess if the prediction of patient gender, ethnicity, and age is possible with use of the developed CADx model.
This will be a sub analysis of an AI algorithm that is trained to predict polyp histology using the prospective data cohort.
A qualitative analysis of CADx incorrect diagnoses will also be conducted by a multidisciplinary panel to evaluate potential impact
Learned effects of AI augmented endoscopy on endoscopist optical diagnosis
We will evaluate if the use of AI during colonoscopy can be a learning tools for endoscopist
Sponsors and contacts
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