Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI)

  • 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.

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Author:Markus W. ScheppachORCiD, Robert MendelORCiD, Andreas Probst, David Rauber, Tobias Rueckert, Michael Meinikheim, Christoph PalmORCiDGND, Helmut Messmann, Alanna EbigboORCiD
Parent Title (English):Endoscopy
Document Type:conference proceeding (presentation, abstract)
Year of first Publication:2023
Release Date:2023/05/08
Tag:Artificial Intelligence
GND Keyword:Speiseröhrenkrankheit; Künstliche Intelligenz
First Page:S53
Last Page:S54
Konferenzangabe:ESGE Days 2023
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG