Pilot study of a new freely available computer-aided polyp detection system in clinical practice

Abstract

Purpose Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. Methods We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092). Results During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80–200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7–2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70–100). Conclusion EndoMind’s ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.

Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference

“…(Supplementary Figure 2). As a new feature of our polyp detection system EndoMind, Poseidon analyzed the images containing a water jet to determine the polyp size [19]. The final size of each polyp was calculated as the average of the determined sizes, but this was not presented to the endoscopists during the experiments.…”

“…bounding box [19]. The second AI algorithm localizes the water jet in the image and determines its diameter.…”

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting

“…Lastly, the algorithm was successfully integrated into our previously described real time polyp detection framework [12]. Figure 3 demonstrates the resulting video segmentation and photo-documentation generated after each examination.…”

“…For real-time application, the previously described EndoMind framework [12] was extended with the newly developed algorithm for multilabel classification and consecutive post-processing of the predictions. Real-time prediction is performed at a rate of 10 frames per second.…”

“…Lastly, the algorithm was successfully integrated into our previously described real-time polyp detection framework [12]. ▶ Fig.…”