Artificial Intelligence-Automated Assessment of Colonoscopy Quality Metrics

There is a significant intraprovider variability in colonoscopy performance, but this is difficult to measure in routine practice. We describe an artificial intelligence colonoscopy quality (AI-CQ) tool that measures colonoscopy quality through analysis of recorded colonoscopy procedures and compare AI-CQ assessment of quality metrics with manual measurement in a large cohort of colonoscopists.

Colonoscopy procedures were performed at 1 of 2 endoscopy locations at a single academic medical center. Select analyses were restricted to higher volume screening colonoscopists performing ≥100 screening or surveillance colonoscopies over the 11-month study period. Colonoscopy quality metrics were calculated from recorded colonoscopy videos using the AI-CQ tool and compared with manually calculated metrics (through nurse documentation) including adenoma detection rate (ADR) and withdrawal time (WT).

A total of 18,597 colonoscopy procedures performed by 55 unique attendings were recorded with 31 higher volume screening colonoscopists performing 12,456 screening or surveillance colonoscopies (median colonoscopist ADR 43.2%). AI-calculated insertion time and WT strongly correlated with manually calculated insertion time ( r = 0.60) and WT ( r = 0.91), respectively. AI polyps per colonoscopy was 1.47 (SD ± 0.54) and strongly correlated with ADR (0.54) and serrated detection rate (0.67). The AI-CQ accurately measured performance of any polypectomy and cold snare polypectomy with a mean cold snare polypectomy rate of 84.0% (range 63.0%-95.3%).

The AI-CQ can accurately measure commonly used quality metrics using recorded colonoscopy videos. Use of this AI tool provides a novel feasible approach to reliably measuring colonoscopy quality.