Introduction:
Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel AI system, compared to standard HD colonoscopy, for APC measurement.
Methods:
This was a U.S. based, multi-center, prospective randomized trial examining a novel AI detection system (EW10-EC02) that enables a real-time colorectal polyp detection enabled with the colonoscope (CAD-EYE™). Eligible average risk subjects (≥45 years) undergoing screening or surveillance colonoscopy were randomized to undergo either CAD-EYE-assisted colonoscopy (CAC) or conventional colonoscopy (CC). Modified Intention-to-treat (mITT) analysis was performed for all patients that completed colonoscopy with primary outcome of APC. Secondary outcomes included positive predictive value (PPV, total number of adenomas divided by total polyps removed) and adenoma detection rate (ADR).
Results:
In mITT analysis, of 1031 subjects (age: 59.1+/-9.8; 49.9% male), 510 underwent CAC vs. 523 underwent CC with no significant differences in age, gender, ethnicity, or colonoscopy indication between the 2 groups. CAC led to a significantly higher APC compared to CC: 0.99± 1.6 vs. 0.85±1.5, p=0.02, Incidence Rate Ratio 1.17 (1.03-1.33, p=0.02) with no significant difference in the withdrawal time: 11.28±4.59 min vs. 10.8±4.81 min; p=0.11 between the 2 groups. Difference in PPV of a polyp being an adenoma among CAC and CC was less than 10% threshold established: 48.6% vs 54%, 95% CI -9.56%, -1.48%. There were no significant differences in ADR (46.9% vs. 42.8%), advanced adenoma (6.5% vs. 6.3%), sessile serrated lesion detection rate (12.9% vs. 10.1%) and polyp detection rate (63.9% vs 59.3%) between the 2 groups. There was a higher polyp per colonoscopy with CAC compared to CC: 1.68 ± 2.1 vs. 1.33 ± 1.8 (incidence rate ratio 1.27; 1.15-1.4; p<0.01).
Conclusion:
Use of a novel AI detection system showed to a significantly higher number of adenomas per colonoscopy compared to conventional HD colonoscopy without any increase in colonoscopy withdrawal time, thus supporting use of AI-assisted colonoscopy to improve colonoscopy quality. (Clinicaltrials.gov NCT04979962)