CADe has been claimed to marginalise polyp miss rate due to failed polyp recognition due to its high accuracy in identifying and localising a polyp present on an endoscopic frame. CADe validation has been mainly performed in an artificial setting by standalone performance against expert endoscopists. The main limitation of this methodology is not only the lack of human–machine interaction as in a clinical setting, but also the fact that the ground truth is represented by polyps recognised only by humans, preventing polyps missed by the endoscopists but CADe detected to become reference standard.

We validated the diagnostic performance of a new CADe system that is novel and innovative in its design since it has a temporal algorithm integrated, resulting in a ‘memory of previous processed frames’ leading to a lower false-positive rate and is developed by using a propagation tool to increase the number of allocated images. In addition, we introduce a novel study design with real-time unblinding. By using a composite reference standard represented by both human and CADe detection, we showed in real-time a very high clinical accuracy of 96.5% of our CADe system for polyp detection. In a challenging setting with real-time endoscopy, our system was not inferior to endoscopists with a high adenoma detection rate (>35%). Unexpectedly, we found that the miss rate due to failure of polyp recognition in high detectors is in fact very low (<2%) and lower than expected from the literature (20%–25%).

The confirmation of clinical accuracy for polyp detection is reassuring to use this new technological background for CADe development and may set a new standard. Our study design with real-time unblinding allows to set a new gold standard for testing the real additional clinical value of CADe over human detection in a real-life clinical setting.