Impact of an artificial intelligence-aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy: Prospective, randomized, multicenter study

ObjectiveThis study was performed to evaluate whether the use of CAD EYE (Fujifilm, Tokyo, Japan) for colonoscopy improves colonoscopy quality in gastroenterology trainees. MethodsThe patients in this multicenter randomized controlled trial were divided into Group A (observation using CAD EYE) and Group B (standard observation). Six trainees performed colonoscopies using a back-to-back method in pairs with gastroenterology experts. The primary end-point was the trainees' adenoma detection rate (ADR), and the secondary end-points were the trainees' adenoma miss rate (AMR) and Assessment of Competency in Endoscopy (ACE) tool scores. Each trainee's learning curve was evaluated using a cumulative sum (CUSUM) control chart. ResultsWe analyzed data for 231 patients (Group A, n = 113; Group B, n = 118). The ADR was not significantly different between the two groups. Group A had a significantly lower AMR (25.6% vs. 38.6%, P = 0.033) and number of missed adenomas per patient (0.5 vs. 0.9, P = 0.004) than Group B. Group A also had significantly higher ACE tool scores for pathology identification (2.26 vs. 2.07, P = 0.030) and interpretation and identification of pathology location (2.18 vs. 2.00, P = 0.038). For the CUSUM learning curve, Group A showed a trend toward a lower number of cases of missed multiple adenomas by the six trainees. ConclusionCAD EYE did not improve ADR but decreased the AMR and improved the ability to accurately locate and identify colorectal adenomas. CAD EYE can be assumed to be beneficial for improving colonoscopy quality in gastroenterology trainees. Trial registrationUniversity Hospital Medical Information Network Clinical Trials Registry (UMIN000044031).