Abstract
This research aims at investigating intermediate-scale workflows using the surgical staff’s movement pattern. In this study, we have introduced an ultrasonic location aware system to monitor intraoperative movement trajectories on surgical staffs for the workflow analysis. And we developed trajectory data mining for surgical workflow segmentation, and analyzed trajectory data with multiple cases. As a result, in 77.18% of total time, a kind of current operation stage could be correctly estimated. With high accuracy 85.96%, the estimation using trajectory data was able to distinguish whether a current 5 minutes was transition time from one stage to another stage or not.. Based on these results, we are implementing the surgery safe support system that promotes safe & efficient surgical operations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bhatia, B., Oates, T., Xiao, Y., Hu, P.: Real-time identification of operating room state from video. In: Proceedings of Innovative Applications of Artificial Intelligence, pp. 1761–1766 (2007)
Cleary, K., Chung, H.Y., Mun, S.K.: OR 2020: The operating room of the future. Laparoendoscopic and Advanced Surgical Techniques 15(5), 295–500 (2005)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)
Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory clustering: A partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604 (2007)
Nambu, K., Iseki, H.: Surgical Strategic Desk and Surgical Safety Support System. Japanese Society for Medical and Biological Engineering 44(2), 257–264 (2001)
Ohnuma, K., Masamune, K., Yoshimitsu, K., Sadahiro, T., Vain, J., Fukui, Y., Miyazaki, F.: Timed-automata-based model for laparoscopic surgery and intraoperative motion recognition of a surgeon as the interface connecting the surgical and the real operating room. International Journal of Computer Assisted Radiology and Surgery 1, 442–445 (2006)
Padoy, N., Blum, T., Essa, I., Feussner, H., Berger, M.-O., Navab, N.: A boosted segmentation method for surgical workflow analysis. In: Proceedings of Medical Image Computing and Computer-Assisted Intervention, pp. 102–109 (2007)
Shoda, R., Matsuda, T., Yoshida, T., Motoda, H., Washio, T.: Graph Clustering with Structure Similarity. In: Proceedings of the 17th Annual Conference of the Japanese Society for Artificial Intelligence (2003) (in Japanese)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Xiao, Y., Hu, P., Hu, H., Ho, D., Dexter, F., Mackenzie, C.F., Seagull, F.J., Dutton, R.P.: An algorithm for processing vital sign monitoring data to remotely identify operating room occupancy in real-time. Anesthesia and Analgesia 101(3), 823–829 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nara, A., Izumi, K., Iseki, H., Suzuki, T., Nambu, K., Sakurai, Y. (2011). Surgical Workflow Monitoring Based on Trajectory Data Mining. In: Onada, T., Bekki, D., McCready, E. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2010. Lecture Notes in Computer Science(), vol 6797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25655-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-25655-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25654-7
Online ISBN: 978-3-642-25655-4
eBook Packages: Computer ScienceComputer Science (R0)