Effect of a novel artificial intelligence–based cecum recognition system on adenoma detection metrics in a screening colonoscopy setting

Abstract

Background and Aims

Cecal intubation in colonoscopy relies on self-reporting. We developed an artificial intelligence–based cecum recognition system (AI-CRS) for post-hoc verification of cecal intubation and explored its impact on adenoma metrics.

Methods

Quality metrics, including cecal intubation rate (CIR), adenoma detection rate (ADR), and other ADR-related metrics, were compared both before (2015-2018) and after (2019-2022) the implementation of the AI-CRS.

Results

Although the CIR did not change significantly after the implementation of the AI-CRS, the ADR and advanced ADR significantly increased. Although the ADR significantly increased in all segments, the most significant increase in advanced ADR was observed in the proximal colon. Implementation of the AI-CRS was associated with a higher likelihood of detecting adenoma (adjusted odds ratio, 1.35; 95% confidence interval, 1.26-1.45) and advanced adenoma (adjusted odds ratio, 1.23; 95% confidence interval, 1.07-1.41), respectively.

Conclusions

Implementation of a post-hoc verification of cecal intubation using an AI-CRS significantly improved various adenoma metrics in screening colonoscopy.