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The EndoRings device is a distal attachment consisting of two layers of circular flexible rings that evert mucosal folds. The aim of this study was to investigate whether EndoRing assisted colonoscopy (ER) improves polyp and adenoma detection compared to standard colonoscopy (SC).Multicenter, parallel group, randomized controlled trial.Total of 556 patients randomized to ER (n = 275) or SC (n = 281). Colonoscopy completed in 532/556 (96%) cases. EndoRings removed in 74/275 (27%) patients. Total number of polyps in ER limb 582 vs. 515 in SC limb, P = 0.04. Total number of adenomas in ER limb 361 vs. 343 for SC limb, P = 0.49. A statistically significant difference in the mean number of polyps per patient in both the intention to treat (1.84 SC vs. 2.10 ER, P = 0.027) and per protocol (PP) (1.84 SC vs. 2.25 ER, P = 0.004).Our study shows promise for the EndoRings device to improve polyp detection.

Background Endoscopic differentiation between intramucosal and submucosal Barrett's neoplasia has several important implications but remains challenging even for expert endoscopists. Recent studies demonstrated promising results on AI-assisted detection of Barrett's neoplasia, but data on AI-assisted staging is limited. We aimed to develop and validate an AI system for classification of Barrett's neoplasia into intramucosal or submucosal, and compare its performance to expert endoscopists. Methods The model, based on VGG-16 architecture, was trained on 117 images of prospectively collected and annotated Barrett's neoplastic lesions. Rotation and random flip were used for data augmentation. The ground truth was the histological staging of endoscopically resected specimens performed by two pathologists with expertise in Barrett's neoplasia. Images comprised of WLI, enhanced imaging, and magnification views. The model was designed to classify images as either intramucosal (pT1a) or submucosal (pT1b). Performance of the AI system was compared to a group of three experts. Results The AI model was tested on an independent dataset of 90 images. The accuracy, sensitivity and specificity and AUC of the AI model in differentiating between intramucosal and submucosal neoplasia was 70.9%, 72.5%, 65.7%, and 0.781 respectively. Mean accuracy, sensitivity and specificity of experts were 73.3%, 63.3% and 83.3% respectively. Processing speed of the AI system was 5 ms/image. Conclusions This study demonstrates the feasibility of AI-assisted staging of Barrett's neoplasia on endoscopic images. The AI model's performance was comparable to that of experts. More work is needed to further develop this early model and validate its use on real-time video sequences.

Background The European Society of Gastrointestinal Endoscopy (ESGE) has developed a core curriculum for high quality optical diagnosis training for practice across Europe. The development of easy-to-measure competence standards for optical diagnosis can optimize clinical decision-making in endoscopy. This manuscript represents an official Position Statement of the ESGE aiming to define simple, safe, and easy-to-measure competence standards for endoscopists and artificial intelligence systems performing optical diagnosis of diminutive colorectal polyps (1 – 5 mm). Methods A panel of European experts in optical diagnosis participated in a modified Delphi process to reach consensus on Simple Optical Diagnosis Accuracy (SODA) competence standards for implementation of the optical diagnosis strategy for diminutive colorectal polyps. In order to assess the clinical benefits and harms of implementing optical diagnosis with different competence standards, a systematic literature search was performed. This was complemented with the results from a recently performed simulation study that provides guidance for setting alternative competence standards for optical diagnosis. Proposed competence standards were based on literature search and simulation study results. Competence standards were accepted if at least 80 % agreement was reached after a maximum of three voting rounds. Recommendation 1 In order to implement the leave-in-situ strategy for diminutive colorectal lesions (1–5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 90 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1–5 mm in the rectosigmoid. Histopathology is used as the gold standard.Level of agreement 95 %. Recommendation 2 In order to implement the resect-and-discard strategy for diminutive colorectal lesions (1–5 mm), it is clinically acceptable if, during real-time colonoscopy, at least 80 % sensitivity and 80 % specificity is achieved for high confidence endoscopic characterization of colorectal neoplasia of 1–5 mm. Histopathology is used as the gold standard.Level of agreement 100 %. Conclusion The developed SODA competence standards define diagnostic performance thresholds in relation to clinical consequences, for training and for use when auditing the optical diagnosis of diminutive colorectal polyps.