Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm. Issue 12 (15th September 2022)

• Record Type: Journal Article
• Authors: Ebigbo, Alanna; Mendel, Robert; Scheppach, Markus W; Probst, Andreas; Shahidi, Neal; Prinz, Friederike; Fleischmann, Carola; Römmele, Christoph; Goelder, Stefan Karl; Braun, Georg; Rauber, David; Rueckert, Tobias; de Souza, Luis A; Papa, Joao; Byrne, Michael; Palm, Christoph; Messmann, Helmut
• Is Part Of: Gut. Volume 71:Issue 12(2022)
• Page Start: 2388
• Page End: 2390
• Publication Date: 2022-09-15
• Subjects: ENDOSCOPIC PROCEDURES -- ENDOSCOPY -- SURGICAL ONCOLOGY; Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2021-326470
• Languages: English
• ISSNs: 0017-5749

Abstract

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.