Development of an artificial intelligence model to identify duodenal polyps in patients with familial adenomatous polyposis

A model to identify duodenal polyps in FAP was successfully created. Although the Dice coefficient is modest compared with that of colon polyp models, duodenal anatomy creates a challenging background for human and computer detection. Rate of polyp detection, likely a superior marker of goal achievement, was >75%, with a low false polyp rate (mean <1/image). This prototype model is the first step toward a refined algorithm to assist in identification of duodenal polyps with a need for larger prospective studies.