Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm - Ebigbo, Alanna; Mendel, Robert; Scheppach, Markus W.; Probst, Andreas; Shahidi, Neal; Prinz, Friederike; Fleischmann, Carola; Roemmele, Christoph; Goelder, Stefan Karl; Braun, Georg; Rauber, David; Rueckert, Tobias; et al. (Bmj Publishing Group, 2022)
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.
Keywords: Endoscopic procedures; Endoscopy; Surgical oncology