An Interaction-Based Bayesian Network Framework for Surgical Workflow Segmentation

Nana Luo, Atsushi Nara and Kiyoshi Izumi
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Nana Luo: School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102612, China
Atsushi Nara: Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA
Kiyoshi Izumi: Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan

IJERPH, 2021, vol. 18, issue 12, 1-13

Abstract: 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.

Keywords: surgical phases prediction; individual interaction measurement; bayesian network; zone position system (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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