Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognitio
Recruiting
- Conditions
- Colorectal cancer, colorectal polyps
- Registration Number
- NL-OMON26387
- Lead Sponsor
- Amsterdam UMC, location AMC
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- Not specified
- Target Recruitment
- 292
Inclusion Criteria
Patients older than 18 years undergoing a screening colonoscopy.
- Signed informed consent
Exclusion Criteria
- Boston bowel preparation score < 6
- Incomplete colonosopy
- Diagnosis of inflammatory bowel disease, Lynch syndrome or (serrated) polyposis syndrome.
Study & Design
- Study Type
- Observational non invasive
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The primary outcome of the study is the accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions. <br><br>Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and/or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomised as neoplastic polyps, while HPs and other non-neoplastic histology are considered non-neoplastic.<br>
- Secondary Outcome Measures
Name Time Method