Alanna Ebigbo, Robert Mendel, Markus W. Scheppach, Andreas Probst, Neal Shahidi, Friederike Prinz, Carola Fleischmann, Christoph Römmele, Stefan Karl Gölder, Georg Braun, David Rauber, Tobias Rückert, Luis Antonio de Souza Jr., João Paulo Papa, Michael F. Byrne, Christoph Palm, Helmut Messmann
- 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.
