Optimizing Colorectal Polyp Screening: A Novel Artificial Intelligence‐Assisted Colonoscopy Diagnostic System Based on NICE Classification

Background This study aims to evaluate the diagnostic performance of an enhanced artificial intelligence‐assisted colonoscopy system, CAD‐N‐Pro, based on the NICE (Narrow‐band Imaging International Colorectal Endoscopic) classification. Methods Compared to the previous CAD‐N system, this study optimized the algorithm into a segmentation network to comprehensively assess the diagnostic performance of the CAD‐N‐Pro model. A total of 14 675 images from 5 hospitals were classified using the NICE classification for training, internal and external validation. The model's performance was also compared with the previous CAD‐N model. To validate the clinical applicability, 200 colonoscopy videos were prospectively collected and analyzed, with comparisons made among endoscopists of different seniority. Results In external image validation, CAD‐N‐Pro demonstrated excellent diagnostic accuracy across polyp types, with an overall AUC of 0.979. The system achieved accuracies of 0.966 for type 1 polyps and type 2 polyps (95% CI 0.956–0.975), and 0.997 for type 3 polyps (95% CI 0.993–0.999), 0.994 for normal background (95% CI 0.990–0.997). In the video validation, the performance of CAD‐N‐Pro was demonstrated to be superior to that of endoscopists with different years of experience, particularly in the diagnosis of type 1 and type 2 polyps. Moreover, CAD‐N‐Pro exhibited superior performance to endoscopists in detecting colorectal polyps of different sizes, especially those < 10 mm. For polyps larger than 10 mm, its performance was comparable to that of endoscopists with > 3 years of experience. Conclusion The optimized CAD‐N‐Pro model enhances optical diagnostic accuracy for colorectal polyps, providing a robust tool for clinical decision‐making in real‐time colonoscopy examinations.