The Augmented Colonoscopy With Computer-Aided Polyp Characterization Study: Prospective Study Comparing the...

Optical diagnosis (OD) is an essential part of a high-quality colonoscopy, but highly experience-dependent. Artificial intelligence (AI) in the form of computer-aided diagnosis (CADx) may bridge the gap between trainee endoscopists and experts. The aim of this study was to evaluate the diagnostic reliability of OD of trainee endoscopists with the help of AI compared with experts.

This prospective, observational study included patients undergoing trainee-performed CADx-supported (GI Genius) colonoscopy. Resected polyps were recorded and video-reviewed without CADx information by experts. The primary outcome was the negative predictive value (NPV) for adenomatous histology of diminutive (≤5 mm) rectosigmoid polyps of trainees vs experts and CADx output alone. Secondary outcomes were the NPV for rectosigmoid polyps of any size and sensitivities and specificities of adenomas in the entire colon.

Overall, 630 polyps were resected in 225 patients (48.9% male, mean age 63.8 (SD 12.7) years). In the rectosigmoid, 252 lesions (40%) were found, 223 (88.5%) of which were ≤5 mm. The NPV for diminutive rectosigmoid polyps of trainees using CADx was 90.2% (95% confidence interval [CI] 0.85-0.94), NPV of the experts without CADx was 90.3% (95% CI 0.84-0.94). There was no statistically significant difference in NPV between these 2 groups. The NPV of CADx alone was 93.2% (95% CI 0.88-0.97). The NPV for rectosigmoid polyps of any sizes were 90.1% (95% CI 0.85-0.94) for trainees, 90.4% (95% CI 0.85-0.95) for experts, and 93.4% (95% CI 0.88-0.97) for CADx alone.

OD of rectosigmoid polyps by trainee endoscopists with CADx is highly accurate, fulfilling PIVI 2 "diagnose-and-leave" strategy.