Procedural phase recognition in endoscopic submucosal dissection (ESD) using artificial intelligence (AI)

  • Aims Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD. Methods A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames. Results The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters. Conclusions The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.

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Author:Markus W. ScheppachORCiD, Danilo Weber Nunes, X. Arizi, David Rauber, Andreas Probst, Sandra Nagl, Christoph Römmele, Michael Meinikheim, Christoph PalmORCiDGND, Helmut Messmann, Alanna EbigboORCiD
Parent Title (English):Endoscopy
Place of publication:Stuttgart
Document Type:conference proceeding (presentation, abstract)
Year of first Publication:2024
Release Date:2024/05/15
Issue:S 02
First Page:S439
Konferenzangabe:ESGE Days 2024
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG