Background and study aims: Polypectomy-related costs could potentially be reduced through optical diagnosis strategies, such as 'diagnose-and-leave' and 'resect-and-discard.' Artificial intelligence, using computer-aided diagnosis (CAD), may provide a reproducible optical diagnosis of colorectal lesions. This study aimed to assess the performance of the CAD-EYE® system in the real-time characterization of colonic polyps.
Methods: We conducted a cross-sectional, multicenter study evaluating the CAD-EYE® system in patients undergoing screening colonoscopies at five French centers. CAD-EYE® predictions and assessments by endoscopists (hyperplastic vs. neoplastic) were compared to histopathology results. The primary outcome was the sensitivity of CAD-EYE® for predicting neoplastic polyps, compared to the predefined threshold of 85%. The secondary outcomes were the specificity, positive predictive value (PPV), negative predictive value (NPV), endoscopists' performance, and polyp detection rates.
Results: Of 398 polyps analyzed, 343 were included in the primary analysis. CAD-EYE® characterization was feasible in 96% of cases. The sensitivity was 0.80 (95% confidence interval, 0.74-0.85), which failed to achieve the predefined threshold of 85% (p = 0.064). The specificity, NPV, and PPV were 0.79, 0.64 and 0.90, respectively. Performance was higher for diminutive rectosigmoid polyps (DRSPs). Endoscopists showed higher sensitivity than CAD-EYE® (0.90 vs. 0.80, p = 0.001). CAD-EYE®-assisted colonoscopies detected more polyps per procedure (3.3 vs. 2.3, p < 0.001) than endoscopists alone.
Conclusion: The performance of CAD-EYE® was insufficient for the characterization of neoplastic colonic polyps. CAD-EYE® performed better for DRSPs. AI appears to be beneficial for polyp detection.
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