Model-based formalization of medical knowledge for context-aware assistance in laparoscopic surgery

Darko Katić; Anna-Laura Wekerle; Fabian Gärtner; Hannes G. Kenngott; Beat P. Müller-Stich; Rüdiger Dillmann; Stefanie Speidel

doi:10.1117/12.2042240

12 March 2014 Model-based formalization of medical knowledge for context-aware assistance in laparoscopic surgery

Darko Katić, Anna-Laura Wekerle, Fabian Gärtner, Hannes G. Kenngott, Beat P. Müller-Stich, Rüdiger Dillmann, Stefanie Speidel

Author Affiliations +

Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 903603 (2014) https://doi.org/10.1117/12.2042240
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

The increase of technological complexity in surgery has created a need for novel man-machine interaction techniques. Specifically, context-aware systems which automatically adapt themselves to the current circumstances in the OR have great potential in this regard. To create such systems, models of surgical procedures are vital, as they allow analyzing the current situation and assessing the context. For this purpose, we have developed a Surgical Process Model based on Description Logics. It incorporates general medical background knowledge as well as intraoperatively observed situational knowledge. The representation consists of three parts: the Background Knowledge Model, the Preoperative Process Model and the Integrated Intraoperative Process Model. All models depend on each other and create a concise view on the surgery. As a proof of concept, we applied the system to a specific intervention, the laparoscopic distal pancreatectomy.

Citation Download Citation

Darko Katić, Anna-Laura Wekerle, Fabian Gärtner, Hannes G. Kenngott, Beat P. Müller-Stich, Rüdiger Dillmann, and Stefanie Speidel "Model-based formalization of medical knowledge for context-aware assistance in laparoscopic surgery", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 903603 (12 March 2014); https://doi.org/10.1117/12.2042240

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