Feasibility of real-time artificial intelligence-assisted anatomical structure recognition during endoscopic submucosal dissection.

Abstract

Background and study aims: 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.

Methods: A total of 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.

Results: 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.

Conclusions: 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 appropriate for routine procedures in humans. Further studies are needed to elucidate a potential clinical benefit of this new technology.