Mining Colonoscopy Videos to Measure Quality of Colonoscopic Procedures

Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and studies of why this occurs are needed. Currently, there is no objective way to measure in detail what exactly is achieved during the procedure (i.e., quality of the colonoscopic procedure). In this paper, we present new algorithms that analyze a video file created during colonoscopy and derive quality measurements of how the colon mucosa is inspected. The proposed algorithms are unique applications of existing data mining techniques: decision tree and support vector machine classifiers applied to videos from medical domain. The algorithms are to be integrated into a novel system aimed at automatic analysis for quality measures of colonoscopy.