COLODETECT 1: comparative evaluation of endocuff with computer-aided detection versus computer-aided detection alone versus standard colonoscopy for enhancing adenoma detection rates during screening colonoscopy—a pilot study

International audience Background: Independent use of artificial intelligence with computer-aided detection (CADe) and Endocuff Vision (ECV) has demonstrated enhanced adenoma detection rates (ADRs). Objective: Our pilot study aimed to define the necessary participant number for future randomized controlled trials (RCTs) by comparing the ADR of combined CADe + ECV against CADe alone and standard colonoscopy. Design: This single-center pilot study retrospectively analyzed a prospectively maintained database, where patients underwent screening colonoscopies sequentially by standard method, CADe alone, and then CADe + ECV. Method: The allocation of the technique depended on the study period. Patients were randomly selected from the cohort to form three groups of 30 patients, with stratification based on factors influencing the ADR. The primary endpoint was the ADR. Results: From April to June 2021, 244 patients underwent screening colonoscopy. 198 were eligible, and after randomization, 90 patients were included across three groups (colonoscopy n = 30, CADe n = 30, CADe + ECV = 30). The ADR was higher in the CADe + ECV group compared to the CADe and colonoscopy groups: 60% versus 40%, and 30%, respectively ( p = 0.03). The number of polyps ⩽3 mm detected was greater in the CADe + ECV group ( n = 23) versus CADe ( n = 7) and colonoscopy ( n = 12) groups, respectively ( p = 0.03). CADe + ECV identified more polyps in the cecum/right colon ( n = 26) compared to CADe ( n = 18) and colonoscopy ( n = 12) groups ( p = 0.04), and in the left colon/sigmoid ( n = 14) compared to CADe ( n = 5) and colonoscopy ( n = 2) ( p = 0.02). Conclusion: These findings underscore the synergic potential of combining CADe with ECV to enhance ADR and enable us to perform sample size calculations for future RCTs.