Abstract
The rise of intraoperatively available information threatens to outpace our abilities to process data and thus cause informational overload. Context-aware systems, filtering information to match the current situation in the OR, will be necessary to reap all benefits of integrated and computerized surgery. To interpret surgical situations, such systems need a robust set of knowledge to make sense of intraoperative measurements. Building on our own ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections and developed a novel, rule-based situation interpretation algorithm based on OWL and SWRL to recognize phases of these surgeries. The system was evaluated on ground truth data from 19 manually annotated surgeries with an average recognition rate of 89%.
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References
Woods, D.D., Patterson, E.S., Roth, E.M.: Can We Ever Escape from Data Overload? A Cognitive Systems Diagnosis Cognition, Technology and Work 4(1), 22–36 (2002)
Joyce, J.P., Lapinski, G.W.: A history and overview of the safety parameter display system concept. IEEE Nuclear Science NS-30 (1983)
Blum, T., Feußner, H., Navab, N.: Modeling and Segmentation of Surgical Workflow from Laparoscopic Video. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 400–407. Springer, Heidelberg (2010)
Cleary, K., Chung, H., Mun, S.: Or 2020: The operating room of the future. Laparoendoscopic and Advanced Surgical Techniques (2005)
Kranzfelder, M., Staub, C., Fiolka, A., Schneider, A., Gillen, S., Wilhelm, D., Friess, H., Knoll, A., Feussner, H.: Toward increased autonomy in the surgical OR: needs, requests, and expectations. Surg. Endosc. 27(5), 1681–1688 (2013)
Blum, T., Padoy, N., Feussner, H., Navab, N.: Workflow mining for visualization and analysis of surgeries. Int. J. Comput. Assisted. Radiol. Surg. 3(5), 379–386 (2008)
Bouarfa, L., Jonker, P.P., Dankelman, J.: Discovery of high-level tasks in the operating room. J. Biomed. Inform. 44(3), 455–462 (2010)
Ahmadi, S.-A., Sielhorst, T., Stauder, R., Horn, M., Feussner, H., Navab, N.: Recovery of surgical workflow without explicit models. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 420–428. Springer, Heidelberg (2006)
Reiley, C., Lin, H., Varadarajan, B., Vagolgyi, B., Khudanpur, S., Yuh, D., Hager, G.: Automatic Recognition of Surgical Motions Using Statistical Modeling for Capturing Variability Medicine Meets Virtual Reality (2008)
Lalys, F., Riffaud, L., Morandi, X., Jannin, P.: Surgical phases detection from microscope videos by combining SVM and HMM. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 54–62. Springer, Heidelberg (2011)
Padoy, N., Blum, T., Ahmadi, S.A., Feussner, H., Berger, M.O., Navab, N.: Statistical modeling and recognition of surgical workflow. Med. Image Anal. 16(3), 632–641 (2010)
Suzuki, T., Sakurai, Y., Yoshimitsu, K., Nambu, K., Muragaki, Y.: Iseki. H.: Intraoperative multichannel audio-visual information recording and automatic surgical phase and incident detection. In: 32nd Annual International Conference of the IEEE EMBS, pp. 1190–1193 (2010)
Neumuth, T., Jannin, P., Schlomberg, J., Meixensberger, J., Wiedemann, P., Burgert, O.: Analysis of Surgical Intervention Populations Using Generic Surgical Process Models. International Journal of CARS (2010)
Burgert, O., Neumuth, T., Lempp, F., Mudunuri, R., Meixensberger, J., Strauss, G., Dietz, A., Jannin, P., Lemke, H.U.: Linking top-level ontologies and surgical workflows. Int. J. Comput. Assisted. Radiol. Surg. 1(1), 437–438 (2006)
Jannin, P., Morandi, X.: Surgical models for computer-assisted neurosurgery. Neuroimage 37(3), 783–791 (2007)
Lalys, F., Bouget, B., Riffaud, R., Jannin, P.: Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures. Int. J. CARS 8, 39–49 (2013)
Oropesa, I., Sánchez-González, P., Lamata, P., Chmarra, M.K., Pagador, J.B., Sánchez-Margallo, J.A., Sánchez-Margallo, F.M., Gómez, E.: Methods and tools for objective assessment of psychomotor skills in laparoscopic surgery. J. Surg. Res. 171(1), 81–95 (2011)
Adam, E.C.: Fighter cockpits of the future. In: Proceedings of 12th IEEE/AIAA Digital Avionics Systems Conference (DASC), pp. 318–323 (1993)
Baader, F., Calvanese, D., McGuiness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation, Applications. Cambridge University Press, Cambridge (2003) ISBN 0-521-78176-0
SWRL: A Semantic Web Rule Language Combining OWL and RuleML W3C Member Submission (2004)
Katic, D., Sudra, G., Speidel, S., Castrillon-Oberndorfer, G., Eggers, G., Dillmann, R.: Knowledge-based Situation Interpretation for Context-aware Augmented Reality in Dental Implant Surgery. In: Proc. Medical Imaging and Augmented Reality (2010)
Katic, D., Wekerle, A.L., Görtler, J., Spengler, P., Bodenstedt, S., Röhl, S., Suwelack, S., Kenngott, H.G., Wagner, M., Mueller-Stich, B.P., Dillmann, R., Speidel, S.: Context-aware Augmented Reality in laparoscopic surgery. Comp. Med. Imag. and Graph. 37(2), 174–182 (2013)
Neumann, B., Moeller, R.: On scene interpretation with description logics. Image and Vision Computing 26 (2008)
Speidel, S., Benzko, J., Sudra, G., Azad, P., Mütich, B.P., Gutt, C., Dillmann, R.: Automatic classification of minimally invasive instruments based on endoscopic image sequences. In: SPIE Medical Imaging, vol. 7261 (2009)
Neumuth, T., Strauß, G., Meixensberger, J., Lemke, H.U., Burgert, O.: Acquisition of process descriptions from surgical interventions. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 602–611. Springer, Heidelberg (2006)
Kripalani, S., Bengtzen, R., Henderson, L., Jacobson, T.: Clinical Research in Low-Literacy Populations: Using Teach-Back to Assess Comprehension of Informed Consent and Privacy Information IRB: Ethics and Human Research (2008)
Shearer, R., Motik, B., Horrocks, I.: HermiT: A Highly-Efficient OWL Reasoner. In: 5th Int. Workshop on OWL: Experiences and Directions (2008)
Neumuth, T., Jannin, P., Strauss, G., Meixensberger, J., Burgert, O.: Validation of knowledge acquisition for surgical process models. J. Am. Med. Inform. Assoc. (2009)
Smith, E.E., Langston, C., Nisbett, R.E.: The case for rules in reasoning. Cognitive Science 16(1), 1–40 (1992)
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Katić, D. et al. (2014). Knowledge-Driven Formalization of Laparoscopic Surgeries for Rule-Based Intraoperative Context-Aware Assistance. In: Stoyanov, D., Collins, D.L., Sakuma, I., Abolmaesumi, P., Jannin, P. (eds) Information Processing in Computer-Assisted Interventions. IPCAI 2014. Lecture Notes in Computer Science, vol 8498. Springer, Cham. https://doi.org/10.1007/978-3-319-07521-1_17
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DOI: https://doi.org/10.1007/978-3-319-07521-1_17
Publisher Name: Springer, Cham
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