6041 2023 eng S53 S54 S02 55 conferencepresentation Thieme 0 2023-05-04 -- -- Aims  AI has proven great potential in assisting endoscopists in diagnostics, however its role in therapeutic endoscopy remains unclear. Endoscopic submucosal dissection (ESD) is a technically demanding intervention with a slow learning curve and relevant risks like bleeding and perforation. Therefore, we aimed to develop an algorithm for the real-time detection and delineation of relevant structures during third-space endoscopy. Methods  5470 still images from 59 full length videos (47 ESD, 12 POEM) were annotated. 179681 additional unlabeled images were added to the training dataset. Consequently, a DeepLabv3+ neural network architecture was trained with the ECMT semi-supervised algorithm (under review elsewhere). Evaluation of vessel detection was performed on a dataset of 101 standardized video clips from 15 separate third-space endoscopy videos with 200 predefined blood vessels. Results  Internal validation yielded an overall mean Dice score of 85% (68% for blood vessels, 86% for submucosal layer, 88% for muscle layer). On the video test data, the overall vessel detection rate (VDR) was 94% (96% for ESD, 74% for POEM). The median overall vessel detection time (VDT) was 0.32 sec (0.3 sec for ESD, 0.62 sec for POEM). Conclusions  Evaluation of the developed algorithm on a video test dataset showed high VDR and quick VDT, especially for ESD. Further research will focus on a possible clinical benefit of the AI application for VDR and VDT during third-space endoscopy. Endoscopy 10.1055/s-0043-1765128 ESGE Days 2023 peer-reviewed publish Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG Markus W. Scheppach Robert Mendel Andreas Probst David Rauber Tobias Rückert Michael Meinikheim Christoph Palm Helmut Messmann Alanna Ebigbo deu swd Speiseröhrenkrankheit deu swd Künstliche Intelligenz eng uncontrolled Artificial Intelligence Medizin und Gesundheit Informatik, Informationswissenschaft, allgemeine Werke Fakultät Informatik und Mathematik Research Center of Biomedical Engineering - RCBE Research Center of Health Sciences and Technology - RCHST Palm, Christoph (Prof. Dr.) - ReMIC Research Center for Artificial Intelligence - RCAI Labor Regensburg Medical Image Computing (ReMIC) Gesundheit und Soziales