COMPUTER-AIDED DETECTION OF POLYPS DOES NOT IMPROVE COLONOSCOPIST PERFORMANCE IN A PRAGMATIC IMPLEMENTATION TRIAL

Introduction: Although colonoscopy is considered the gold standard for detection and removal of premalignant polyps, up to 26% of lesions are missed in tandem studies. Computer-aided polyp detection (CADe) has shown promise in increasing polyp detection rates. The aim of this study was to evaluate a novel CADe system, ‘Magentiq Eye Automatic Polyp Detection System’ (ME-APDS), in a non-iFOBT screening and surveillance colonoscopy population.

Methods: A multicenter, randomized, controlled (RCT) trial was conducted at 10 hospitals in Europe, US and Israel. Patients (18-90 years), referred for screening (non-iFOBT) or surveillance colonoscopy, were included. Patients were randomized (1:1) to undergo CADe-assisted colonoscopy or conventional colonoscopy (CC). In each arm, a subset of patients was further randomized to undergo tandem colonoscopy; CADe followed by CC or CC followed by CADe. Primary objective was adenoma per colonoscopy (APC). Secondary objectives were adenoma detection rate (ADR) and adenoma miss rate (AMR). Outcomes were also evaluated by colonoscopy indication (screening and surveillance), adenoma location, and adenoma size.

Results: In total, 950 patients were enrolled, of which 916 completed the assigned colonoscopy, 449 in the CADe-assisted group and 467 in the CC group. APC was higher in CADe-arm compared to CC (0.70 vs. 0.51, p=0.015; total adenomas, 314 vs. 238). Overall, ADR was higher in CADe compared to CC (37% vs. 30%, p=0.014). Apart from diminutive (0-5mm) adenomas, use of CADe also increased the detection of small (6-9mm) adenomas compared to CC (14.3% vs. 9.9%, p=0.036). Moreover, an increase in proximal adenoma detection was observed in CADe-assisted colonoscopy compared to CC (46.6% vs. 31.1%, p=0.006). A total of 127 (61 CADe first, 64 CC first) patients completed tandem colonoscopy. AMR was 19% in CADe first compared to 36% in CC first (p=0.024). Use of ME-CADe had no impact on withdrawal times (p=0.861).

Conclusion: ME-APDS increased adenoma detection (both APC and ADR) in non-iFOBT screening and surveillance colonoscopies, and reduced AMR by two-fold compared to CC. Apart from diminutive lesions, ME-APDS increased the detection of 6-9mm adenomas suggesting that this novel CADe system is also able to detect more clinically relevant lesions.

Background:
Artificial intelligence (AI) could revolutionize endoscopy. The first US-approved computer-aided detection (CADe) device (GI Genius) significantly increased adenoma detection rate (ADR) and adenomas per colonoscopy (APC) in randomized controlled trials (RCTs). We sought to replicate the RCT findings in a pragmatic implementation trial.

Methods:
We evaluated CADe for 3 months in a pragmatic trial that leveraged our Colonoscopy Quality Assurance Program and performed a difference-in-difference analysis on quality metrics (with 95% CI) during “Implementation” vs “Pre-implementation” periods in the endoscopy site equipped with CADe (CADe site) vs. 5 non-CADe Control sites, accounting for age, sex, colonoscopy indication and within-endoscopist correlation. Endoscopists at Control sites were not made aware about the CADe trial, and we used a minimalist deployment strategy (no specific expectations/encouragement).

Results:
CADe was used in 1,008/1,037 (97.2%) eligible colonoscopies, with 619 done for screening/surveillance by 24 endoscopists. Study cohort demographics and indications were comparable across sites and periods.

In the CADe site, ADR was 40.1% (36.2-44.0%) and mean APC was 0.78 (0.68-0.90) during Implementation vs 41.8% (37.9-45.8%)(p=0.44) and 0.89 (0.77-1.02)(p=0.23) during Pre-Implementation without CADe (FigA,B). Sessile serrated lesion (SSL), advanced adenoma/SSL, and lesion multiplicity detection rates were also comparable across periods (Table). In Control sites, Implementation vs Pre-Implementation results were comparable (FigA,B; Table).

No effect of CADe on ADR (OR 1.14 [0.83-1.56], p=0.41), APC (OR 1.08 [0.80-1.45], p=0.63) or any other metric was detected (FigA-E, Table). No effects of CADe on procedure times and non-neoplastic lesion resection were seen (Table).

CADe use did not mitigate differences in ADR or APC (FigD,E), or any other metric between lower vs. higher tertiles of performance.

Discussion:
Our results contrast sharply with those of RCTs. Why?

We were interested in real-world CADe implementation, and thus we simply deployed CADe with basic start-up training but no attempt to influence performance or discuss hypotheses. Endoscopists may have dismissed adenomas/SSLs not highlighted by CADe, or true positive CADe prompts. Most concerning would be if CADe led to an unconscious degradation in the quality of mucosal exposure (false sense of comfort?).

In contrast, RCT endoscopists knew the study design/hypotheses and could not be blinded, and they must have known they could influence use of a nascent technology (we do not in any way imply conscious bias or inappropriate conduct).

We remain optimistic about AI, but a minimalist deployment strategy may not ensure success. Better understanding of subtle AI/endoscopist interactions (e.g. mucosal exposure) could promote uniformly high quality in endoscopy.