Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy

Adequacy of manual endoscopic inspection of the upper and lower gastrointestinal mucosa is operator-dependent: it is common knowledge that the likelihood of finding lesions and the degree of cancer prevention of manual endoscopic procedures is dependent on the skillset and effort by endoscopists. Recent develop-ments in artificial intelligence allow measurement of skillset and effort during actual endoscopic procedures. Many endoscopic features, representing skillset and effort, such as clarity of image, absence of stool, looking sideways, performing retroflexion and obtaining all possible mucosal views, can be classified or counted, and the results presented in real-time to endoscopists and stored at the end of the procedure as an automated, objective report with representative images documenting adequacy of inspection. Real-time feedback pro-vides endoscopists the option, when measurements suggest inadequate or incomplete inspection, to change technique or repeat and expand inspection. However, responding to real-time feedback and obtaining best possible measurements or all possible mucosal views do not equate to careful inspection; endoscopists may be focused on obtaining best measurements instead of inspecting the mucosa. Therefore, prospective studies with long-term follow-up will be required to determine whether artificial intelligence driven real-time feed-back will lead not only to better intra-procedural measurements but also to improved patient outcome, for example, a decrease in gastrointestinal cancer incidence and mortality. (c) 2019 Elsevier Inc. All rights reserved.