Mining Colonoscopy Videos to Measure Quality of Colonoscopic Procedures

D. Liu, Y. Cao, W. Tavanapong, J. Wong, J.H. Oh, and P.C. de Groen (USA)


Medical video analysis, data mining, endoscopy, quality control


Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon. Colonoscopy is the accepted screening method for detec tion of colorectal cancer or its precursor lesions, colorec tal polyps. Indeed, colonoscopy has contributed to a de cline 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 meas ure in detail what exactly is achieved during the proce dure (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 measure ments of how the colon mucosa is inspected. The pro posed algorithms are unique applications of existing data mining techniques: decision tree and support vector ma chine 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.

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