The Effect of Novel AI-based Cecum Recognition System on Adenoma Detection Metrics at Screening Colonoscopy Setting.

ABSTRACT

BACKGROUND AND AIMS: Cecal intubation of 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 AI-CRS. RESULTS: While CIR did not change significantly after the implementation of AI-CRS, ADR and AADR significantly increased. While ADR significantly increased in all segments, the most significant increase in AADR was observed in the proximal colon. Implementation of AI-CRS was associated with a higher likelihood of detecting adenoma (aOR=1.35, 95%CI=1.26-1.45) and advanced adenoma (aOR=1.23, 95%CI=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.