An analysis about the function of a new artificial intelligence, CAD EYE with the lesion recognition and diagnosis for colorectal polyps in clinical practice

Objectives Recently, CAD EYE (Fujifilm, Tokyo, Japan), an artificial intelligence for the lesion recognition (CADe) and the optical diagnosis (CADx) of colorectal polyps, was released. We evaluated the function of CADe and CADx of CAD EYE. Methods In this single-center retrospective study, we examined consecutive polyps ≤ 10 mm detected from March to April 2021 to determine whether CAD EYE could recognize them live with both normal- and high-speed observation using white-light imaging (WLI) and linked-color imaging (LCI). We then examined whether the polyps were neoplastic or hyperplastic live with magnified or non-magnified blue-laser imaging (BLI-LASER) or blue-light imaging (BLI-LED) under CAD EYE, comparing the retrospective evaluations with 5 experts and 5 trainees using still images. All polyps were histopathologically examined. Results We analyzed 100 polyps (mean size 3.9 ± 2.6 mm; 55 neoplastic and 45 hyperplastic lesions) in 25 patients. Regarding CADe, the respective detection rates of CAD EYE with normal- and high-speed observation were 85.0% and 67.0% for WLI ( p = 0.002) and 89.0% and 75.0% for LCI ( p = 0.009). Regarding CADx for differentiating neoplastic and hyperplastic lesions, the diagnostic accuracy values of CAD EYE with non-magnified and magnified BLI-LASER/LED were 88.8% and 87.8%. Regarding magnified BLI-LASER/LED, the diagnostic accuracy value of CAD EYE was not significantly different from that of experts (92.0%, p = 0.17), but that of trainees (79.0%, p = 0.04). We also found no significant differences in CADe or CADx between LED (53 lesions) and LASER (47 lesions). Conclusions CAD EYE was a helpful tool for CADe and CADx in clinical practice.