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CC BY 4.0 · Endosc Int Open
DOI: 10.1055/a-2615-8008
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Feasibility of real time artificial intelligence assisted anatomical structure recognition during endoscopic submucosal dissection

Markus Wolfgang Scheppach 
1   Internal Medicine III - Gastroenterology, University of Augsburg Faculty of Medicine, Augsburg, Germany (Ringgold ID: RIN531257)
2   Department of Surgery, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
3   Institute of Digestive Disease, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
,
Hon Chi Yip 
2   Department of Surgery, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
3   Institute of Digestive Disease, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
,
Yueyao Chen
4   Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong (Ringgold ID: RIN26451)
,
Hongzheng Yang
4   Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong (Ringgold ID: RIN26451)
,
Jianfeng Cao
4   Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong (Ringgold ID: RIN26451)
,
Tiffany Chua
5   Division of Gastroenterology, Hepatology & Nutrition, Department of Medicine, College of Medicine, University of Florida, Gainesville, United States (Ringgold ID: RIN3463)
3   Institute of Digestive Disease, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
,
Qi Dou
4   Department of Computer Science and Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong (Ringgold ID: RIN26451)
,
Helen Mei Ling Meng
6   Department of Mechanical and Automation Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong (Ringgold ID: RIN26451)
,
Yeung Yam
7   Department of Systems Engineering and Engineering Management, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong (Ringgold ID: RIN26451)
,
Philip W Chiu 
2   Department of Surgery, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
3   Institute of Digestive Disease, Chinese University of Hong Kong Faculty of Medicine, New Territories, Hong Kong (Ringgold ID: RIN71024)
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Endoscopic submucosal dissection (ESD) is a challenging minimally invasive resection technique with a long training period and relevant operator dependent complications. Real time artificial intelligence (AI) orientation support may improve safety and intervention speed. 1011 endoscopic still images from 30 ESDs were annotated for relevant anatomical structures and used for training of a deep learning algorithm. After internal and external validation, this algorithm was applied to 12 ESDs performed by either one expert or one novice in ESD using an in vivo porcine model. External validation yielded mean Dice Scores of 88%, 60%, 58% and 92% for background, submucosal layer, submucosal blood vessels and muscle layer, respectively. The system was successfully applied during all 12 ESDs. All resections were completed en bloc and without complications. In this proof-of-concept study, feasibility of a real time AI algorithm for anatomical structure delineation and orientation support during ESD was evaluated. The application proved safe and apt for routine procedures in humans. Further studies are needed to elucidate a potential clinical benefit of this new technology.



Publication History

Received: 11 August 2024

Accepted after revision: 04 April 2025

Accepted Manuscript online:
19 May 2025

© . The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

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Bibliographical Record
Markus Wolfgang Scheppach, Hon Chi Yip, Yueyao Chen, Hongzheng Yang, Jianfeng Cao, Tiffany Chua, Qi Dou, Helen Mei Ling Meng, Yeung Yam, Philip W Chiu. Feasibility of real time artificial intelligence assisted anatomical structure recognition during endoscopic submucosal dissection. Endosc Int Open ; 0: a26158008.
DOI: 10.1055/a-2615-8008