Background Multiple computer-aided systems for polyp detection (CADe) have been introduced into clinical practice, with an unclear effect on examiner behavior. This study aimed to measure the influence of a CADe system on reaction time, mucosa misinterpretation, and changes in visual gaze pattern. Methods Participants with variable levels of colonoscopy experience viewed video sequences (n = 29) while eye movement was tracked. Using a crossover design, videos were presented in two assessments, with and without CADe support. Reaction time for polyp detection and eye-tracking metrics were evaluated. Results 21 participants performed 1218 experiments. CADe was significantly faster in detecting polyps compared with participants (median 1.16 seconds [99 %CI 0.40–3.43] vs. 2.97 seconds [99 %CI 2.53–3.77], respectively). However, the reaction time of participants when using CADe (median 2.90 seconds [99 %CI 2.55–3.38]) was similar to that without CADe. CADe increased misinterpretation of normal mucosa and reduced the eye travel distance. Conclusions Results confirm that CADe systems detect polyps faster than humans. However, use of CADe did not improve human reaction times. It increased misinterpretation of normal mucosa and decreased the eye travel distance. Possible consequences of these findings might be prolonged examination time and deskilling. ^* These authors contributed equally to this work. Received: 19 September 2021 Accepted: 10 February 2022 Accepted Manuscript online: 14 February 2022 Article published online: 31 March 2022 © 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/) Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Background Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary waterjet as a measurement reference. Methods Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a computed tomography colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the waterjet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during routine clinical practice were compared with visual estimates. Results In the silicone model, visual estimation had the largest median percentage error of 25.1 % (95 %CI 19.1 %–30.4 %), followed by biopsy forceps-based estimation: median 20.0 % (95 %CI 14.4 %–25.6 %). Poseidon gave a significantly lower median percentage error of 7.4 % (95 %CI 5.0 %–9.4 %) compared with other methods. During routine colonoscopies, Poseidon presented a significantly lower median percentage error (7.7 %, 95 %CI 6.1 %–9.3 %) than visual estimation (22.1 %, 95 %CI 15.1 %–26.9 %). Conclusion In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.

Purpose: Local treatment of small well-differentiated rectal neuroendocrine tumors (NETs) is recommended by current guidelines. However, although several endoscopic methods have been established, the highest R0 rate is achieved by transanal endoscopic microsurgery (TEM). Since a recently published study about endoscopic full thickness resection (eFTR) showed a R0 resection rate of 100%, the aim of this study was to evaluate both methods (eFTR vs. TEM). Methods: We retrospectively analyzed all patients with rectal NET treated either by TEM (1999–2018) or eFTR (2016–2019) in two tertiary centers (University Hospital Wuerzburg and Ulm). We analyzed clinical, procedural, and histopathological outcomes in both groups. Results: Twenty-eight patients with rectal NET received local treatment (TEM: 13; eFTR: 15). Most tumors were at stage T1a and grade G1 or G2 (in the TEM group two G3 NETs were staged T2 after neoadjuvant chemotherapy). In both groups, similar outcomes for en bloc resection rate, R0 resection rate, tumor size, or specimen size were found. No procedural adverse events were noted. Mean procedure time in the TEM group was 48.9 min and 19.2 min in the eFTR group. Conclusion: eFTR is a convincing method for local treatment of small rectal NETs combining high safety and efficacy with short interventional time.

Background and study aims Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset. Methods 101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics. Results Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind. Conclusions This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.

Background: The efficiency of artificial intelligence as computer‐aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during interventions such as polypectomies. Those distracting CADe detections are often induced by the introduction of snares or biopsy forceps as the systems have not been trained for such situations. In addition, there are a significant number of non‐false but not relevant detections, since the polyp has already been previously detected. All these detections have the potential to disturb the examiner's work.Objectives: Development and evaluation of a convolutional neuronal network that recognizes instruments in the endoscopic image, suppresses distracting CADe detections, and reliably detects endoscopic interventions.Methods: A total of 580 different examination videos from 9 different centers using 4 different processor types were screened for instruments and represented the training dataset (519,856 images in total, 144,217 contained a visible instrument). The test dataset included 10 full‐colonoscopy videos that were analyzed for the recognition of visible instruments and detections by a commercially available CADe system (GI Genius, Medtronic).Results: The test dataset contained 153,623 images, 8.84% of those presented visible instruments (12 interventions, 19 instruments used). The convolutional neuronal network reached an overall accuracy in the detection of visible instruments of 98.59%. Sensitivity and specificity were 98.55% and 98.92%, respectively. A mean of 462.8 frames containing distracting CADe detections per colonoscopy were avoided using the convolutional neuronal network. This accounted for 95.6% of all distracting CADe detections.Conclusions: Detection of endoscopic instruments in colonoscopy using artificial intelligence technology is reliable and achieves high sensitivity and specificity. Accordingly, the new convolutional neuronal network could be used to reduce distracting CADe detections during endoscopic procedures. Thus, our study demonstrates the great potential of artificial intelligence technology beyond mucosal assessment.

Despite the increasing incidence and prevalence of Crohn’s Disease (CD), no curative options exist and treatment remains complex. While therapy has mainly focused on medical approaches in the past, growing evidence reveals that in cases of limited inflammation, surgery can suffice as an alternative primary treatment. We retrospectively assessed the disease course and outcomes of 103 patients with terminal Ileitis who underwent primary surgery (n = 29) or received primary medical treatment followed by surgery (n = 74). Primary endpoint was the need for immunosuppressive medication after surgical treatment (ileocecal resection, ICR) during a two-years follow-up. Rates for laparoscopic ICR were enhanced in case of early surgery, but no differences were seen for postoperative complications. In case of immunosuppressive medication, patients with ICR at an early state of disease needed significantly less anti-inflammatory medication during the two-year postoperative follow-up compared to patients who were primarily treated medically. Furthermore, in a subgroup analysis for patients with localized ileocecal disease manifestation, early surgery consistently resulted in a decreased amount of medical therapy postoperatively. In conclusion primary ICR is safe and effective in patients with limited CD, and the need for immunosuppressive medication during the postoperative follow-up is low compared to patients receiving surgery at a later stage of disease.