Artificial intelligence in endoscopy: transition from academic development to integration into daily endoscopic practice

Abstract

Over the past decade we have seen a rapid increase in the use of artificial intelligence (AI) systems in the endoscopic literature, most of which has been focused on colonic applications. The field is now moving forward rapidly, with implementation of the first clinical studies and an expansion of the research focus to a variety of endoscopic applications. Given its subtle endoscopic appearance and the corresponding diagnostic challenge, early gastric cancer is a logical application of AI. Several systems have recently been described, mainly focusing on primary detection systems [1][2][3].In this issue of Endoscopy, Niikura and colleagues present results of a retrospective study in which they evaluated a previously trained computer-aided detection system for early gastric cancer [4]. For this evaluation, they applied an interesting experimental set-up using stratified matching of retrospectively collected imagery in a noninferiority comparison of AI versus expert assessment. The AI system was designed to first detect and then localize lesions on endoscopic white-light images by displaying a bounding box. The per-patient detection rate of 100 % by the AI system is impressive, as is its increased per-image detection rate when compared to expert endoscopists.