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Adenoma detection rate (ADR) represents the most widely accepted quality metric of colonoscopy and it has been validated as an independent predictor of the risk of interval colorectal cancer (CRC) after screening colonoscopy (1). Besides, it has also been demonstrated that increasing ADR translates into a reduced risk of interval CRC by individual endoscopist, with an approximately 6% relative reduction for each 1% increase in ADR (2). As ADR largely varies among different endoscopists (1,3), reflecting differences in colonoscopy performance, many studies in the literature have evaluated new technologies to improve it, showing conflicting and sometimes disappointing results. The s tudy by Urban e t a l . ( 4 ) , pub l i shed on Gastroenterology, makes a further step in the future by evaluating the application of a deep learning model for computer-assisted image analysis [convolutional neural networks (CNNs)] to increase polyp detection, as a surrogate of ADR. In detail, the Authors developed and trained CNNs to detect polyps using a representative set of 8,641 polyp pictures from screening colonoscopies identified from more than 2,000 patients, achieving an accuracy of 96.4%. The models were subsequently tested on 20 colonoscopy videos with an overall duration of five hours. Furthermore, highly-experienced endoscopists were asked to detect all polyps in 9 de-identified colonoscopy videos, selected from archived video studies, with or without benefit of the CNN overlay; their performances were compared with CNN ones using CNN-assisted expert review as the reference. In the analysis of colonoscopy videos in which 28 polyps were removed, 4 expert reviewers identified 8 additional polyps without CNN assistance that had not been removed and identified during live examinations and 9 more polyps with CNN assistance. CNN identified all polyps detected by expert reviewers, with a false-positive rate of 7%. The Authors concluded that CNN was able to identify polyps with a very high cross-validation accuracy in a set of colonoscopy pictures and also to real-time detect and localize polyps using an ordinary desktop equipment with a contemporary graphics processing unit. According to the Authors the use of artificial intelligence for polyp detection may represent a great promise in helping to close the gap between ADR and true adenoma prevalence, especially for colonoscopists with low baseline ADR. So, if these results will be confirmed in real-time validation studies, will it be possible to disregard the human touch? Probably not, or at least not at all. Actually, the polyp and, even more, the adenoma detection during colonoscopy is a multistep process mainly depending on three main human contributors, which all concur to the quality of the examination. First, the skill to bring the polyp into the field of view, which mainly depends on withdrawal technique, namely the ability of carefully inspecting the mucosa during the scope withdrawal by an adequate lumen distension, mucosal cleansing and mucosal exposure behind colonic folds. Technique, albeit difficult to be objectively assessed, is strictly endoscopist-related and represents a powerful indicator in differentiating high and low adenoma detectors, even more important than withdrawal time (5). Second, the ability to focus attention on a lesion that is Editorial

Artificial intelligence and the endoscopist’s skill and proficiency for polyp detection: no winner one without the other!