1. Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm.
- Author
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Ebigbo A, Mendel R, Scheppach MW, Probst A, Shahidi N, Prinz F, Fleischmann C, Römmele C, Goelder SK, Braun G, Rauber D, Rueckert T, de Souza LA Jr, Papa J, Byrne M, Palm C, and Messmann H
- Subjects
- Humans, Artificial Intelligence, Endoscopy, Gastrointestinal, Deep Learning, Endoscopic Mucosal Resection
- Abstract
In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training., Competing Interests: Competing interests: NS: speaker honorarium, Boston Scientific and Pharmascience. MB: CEO and founder, Satisfai Health. HM: consulting fees, Olympus., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
- Published
- 2022
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