Impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective propensity score-matched study (with video)

Background and Aims: Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists. Methods: This was a propensity score-matched prospective study that took place at a university hospital between July 2020 and December 2020. We recruited patients aged >= 20 years who were scheduled for colonoscopy. Patients with polyposis, inflammatory bowel disease, or incomplete colonoscopy were excluded. We used a regulatory-approved CADe system and conducted a propensity score matching-based comparison of the ADR between patients examined with and without CADe as the primary outcome. Results: During the study period, 2261 patients underwent colonoscopy with the CADe system or routine colonoscopy, and 172 patients were excluded in accordance with the exclusion criteria. Thirty endoscopists (9 non-experts and 21 experts) were involved in this study. Propensity score matching was conducted using 5 factors, resulting in 1836 patients included in the analysis (918 patients in each group). The ADR was significantly higher in the CADe group than in the control group (26.4% vs 19.9%, respectively; relative risk, 1.32; 95% confidence interval, 1.12-1.57); however, there was no significant increase in the advanced neoplasia detection rate (3.7% vs 2.9%, respectively). Conclusions: The use of the CADe system for colonoscopy significantly increased the ADR in a large-scale prospective study including 30 endoscopists

Automated summary

This study found that using CADe system for colonoscopy significantly increased the adenoma detection rate in a large-scale prospective evaluation, while there was no significant increase in the detection rate of advanced neoplasia.