Background and study aims: Multiple computer-aided systems for polyp detection (CADe) are currently introduced into clinical practice, with an unclear effect on examiner behavior. In this work, we aimed to measure the influence of a CADe system on reaction time, mucosa misinterpretations, and changes in visual gaze pattern. Patients and methods: Participants with variable levels of experience in colonoscopy examined video sequences while eye movement was tracked. Using a crossover design, videos were presented in two assessments with and without CADe (GI Genius, Medtronic) support. Reaction time for polyp detection and eye-tracking metrics were evaluated. Results: 21 Participants performed 1218 experiments. CADe was with a median of 1.16sec significantly faster in detecting polyps compared to the users with 2.97sec (99%CI;0.40-3.43 and 2.53-3.77sec, respectively). However, the reaction time of the user with the use of CADe with a median of 2.9sec (99%CI;2.55-3.38sec) was similar than without its use. CADe increased the misinterpretations of normal mucosa and reduced the eye travel distance. Conclusions: This work confirms that CADe systems detect polyps faster than humans. In addition, they led to increased misinterpretations of normal mucosa and decreased eye travel distance. Possible consequences of these findings might be prolonged examination time and deskilling.

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.

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Background: Colorectal cancer (CRC) is still a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. During this procedure, the colonoscopist searches for polyps. However, there is a potential risk of polyps being missed by the examiner. Here the automated detection of polyps helps assist the examiner during coloscopy. In the literature, there are already publications examining the problem of polyp detection. Nevertheless, most of these systems are only used in the research context and do not attain clinical application. Therefore, we introduce a system scoring best on current benchmarks and implementing it fully for clinical-ready applications. Methods: To create the polyp detection system (ENDOMIND-Advanced), we combined our own collected data from different hospitals and practices in Germany with open-source data sets to create a data set with over 500.000 annotated images. Furthermore, we show different techniques for training a CNN on polyp detection that involves preprocessing, data augmentation and hyperparameter optimization. Additionally, we developed a post-processing technique based on video detection to work in real-time with a stream of images. This allows us to leverage the incoming stream context of the endoscope while maintaining real-time performance. Furthermore, the polyp detection system is integrated into a prototype ready for application in clinical interventions. Results: First, we show that our polyp detection system is state of the art by evaluating it on the CVC-VideoClinicDB benchmark with a F1-score of 90.24%. We compare the polyp detection system to the best system in the literature and achive better results in speed and accuracy. Additionally, we show its performance on our own data and introduce a new metric called the time to the first detection. This metric is given in seconds and shows how long AI systems need to detect a polyp for the first time. Finally, we further elaborate on the explainability of our system by showing heatmaps of the neural network explaining neural activations. Conclusion: Overall we introduce a fully assembled real-time system for polyp detection with application in clinical practice and show that the system outperforms current systems on benchmark data sets with real-time performance.