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Article

An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation

by 1,2,3, 2,3,* and 4
1
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, China
2
Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA
3
Center for Human Dynamics in the Mobile Age, San Diego State University, San Diego, CA 92182-4493, USA
4
Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: David Gil
Int. J. Environ. Res. Public Health 2021, 18(12), 6401; https://doi.org/10.3390/ijerph18126401
Received: 3 April 2021 / Revised: 3 June 2021 / Accepted: 8 June 2021 / Published: 13 June 2021
Recognizing and segmenting surgical workflow is important for assessing surgical skills as well as hospital effectiveness, and plays a crucial role in maintaining and improving surgical and healthcare systems. Most evidence supporting this remains signal-, video-, and/or image-based. Furthermore, casual evidence of the interaction between surgical staff remains challenging to gather and is largely absent. Here, we collected the real-time movement data of the surgical staff during a neurosurgery to explore cooperation networks among different surgical roles, namely surgeon, assistant nurse, scrub nurse, and anesthetist, and to segment surgical workflows to further assess surgical effectiveness. We installed a zone position system (ZPS) in an operating room (OR) to effectively record high-frequency high-resolution movements of all surgical staff. Measuring individual interactions in a closed, small area is difficult, and surgical workflow classification has uncertainties associated with the surgical staff in terms of their varied training and operation skills, patients in terms of their initial states and biological differences, and surgical procedures in terms of their complexities. We proposed an interaction-based framework to recognize the surgical workflow and integrated a Bayesian network (BN) to solve the uncertainty issues. Our results suggest that the proposed BN method demonstrates good performance with a high accuracy of 70%. Furthermore, it semantically explains the interaction and cooperation among surgical staff. View Full-Text
Keywords: surgical phases prediction; individual interaction measurement; bayesian network; zone position system surgical phases prediction; individual interaction measurement; bayesian network; zone position system
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MDPI and ACS Style

Luo, N.; Nara, A.; Izumi, K. An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation. Int. J. Environ. Res. Public Health 2021, 18, 6401. https://doi.org/10.3390/ijerph18126401

AMA Style

Luo N, Nara A, Izumi K. An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation. International Journal of Environmental Research and Public Health. 2021; 18(12):6401. https://doi.org/10.3390/ijerph18126401

Chicago/Turabian Style

Luo, Nana, Atsushi Nara, and Kiyoshi Izumi. 2021. "An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation" International Journal of Environmental Research and Public Health 18, no. 12: 6401. https://doi.org/10.3390/ijerph18126401

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