Computer-aided diagnosis for colonoscopy.

Abstract

Several studies have shown that colonoscopy is associated with a reduction in colorectal cancer mortality. This benefit is based on the detection and resection of any neoplastic polyps; however, polyps can be missed during screening colonoscopy and endoscopists may not be able to differentiate between neoplastic and non-neoplastic polyps. Polyp miss rates as high as 20 % have been reported for high definition resolution colonoscopy 1 , while a large prospective trial of optical biopsy of small colon polyps using narrow-band imaging (NBI) showed that the accuracy of physicians was only 80 % in diagnosing detected polyps as adenomas, even after a physician training program 2 . To overcome these limitations, computer-aided diagnosis (CAD) is attracting more attention because it may help endoscopists to avoid missing and mischaracterizing polyps. CAD for colonoscopy is generally designed to extract various features from a colonoscopic image/movie and output the predicted polyp location or pathology based on machine learning. The term “machine learning” refers to a fundamental function of artificial intelligence, whereby a computer can be trained to learn (in this case, recognize or characterize polyps) through repetition and experience (exposure to a large number of annotated polyp images). Ideally, the output of CAD is expressed in real time on the monitor, immediately assisting the endoscopist’s decision-making.