We developed and validated a deep-learning algorithm for polyp detection. We used a YOLOv2 to develop the algorithm for automatic polyp detection on 8,075 images (503 polyps). We validated the algorithm using three datasets: A: 1,338 images with 1,349 polyps; B: an open, public CVC-clinic database with 612 polyp images; and C: 7 colonoscopy videos with 26 polyps. To reduce the number of false positives in the video analysis, median filtering was applied. We tested the algorithm performance using 15 unaltered colonoscopy videos (dataset D). For datasets A and B, the per-image polyp detection sensitivity was 96.7% and 90.2%, respectively. For video study (dataset C), the per-image polyp detection sensitivity was 87.7%. False positive rates were 12.5% without a median filter and 6.3% with a median filter with a window size of 13. For dataset D, the sensitivity and false positive rate were 89.3% and 8.3%, respectively. The algorithm detected all 38 polyps that the endoscopists detected and 7 additional polyps. The operation speed was 67.16 frames per second. The automatic polyp detection algorithm exhibited good performance, as evidenced by the high detection sensitivity and rapid processing. our algorithm may help endoscopists improve polyp detection. Colonoscopy is an important colorectal cancer (CRC) screening test worldwide. Colonoscopy has several advantages, such as the removal of lesions and visualization in a single test. Recent studies indicated that having a colonoscopy was associated with a 60% reduction in CRC mortality 1 and a 70% reduction in the incidence of late-stage CRCs 2. Colonoscopy quality assurance is of paramount importance for effective prevention of CRC and reduction of mortality due to CRC. Accurate detection of adenomas is the most critical issue during a colonoscopy. The adenoma detection rate is an essential quality indicator during colonoscopy. Evidence suggests that a 1.0% increase in the adenoma detection rate leads to a 3.0% decrease in the risk of interval CRC 3. The adenoma detection rate varies from 17% to 47% because the characteristics of colonoscopy are highly operator-dependent 4. Therefore, it is important to increase the adenoma detection rate for adequate CRC screening via colonoscopy. Although many efforts have been directed toward improving the detection of adenoma, such as improving the bowel preparation, spending enough time to inspect the colonic mucosa, and developing several novel technologies, such as wide-angle cameras and cap-assisted techniques to flatten colonic folds 5 , the problem of missing polyps remains. A previous study indicated that endoscopists with wider visual gaze patterns achieved a higher polyp detection rate than those with centralized visual gaze patterns 6. Several studies have indicated that the participation of an experienced nurse during the colonoscopy examination as a "second observer" increased the adenoma detection rates by up to 30-50% 7,8 and increased the detection performance of inexperienced endoscopists 7. A real-time automatic pol...

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