Quantitative Endosurgery Process Analysis by Machine Learning Method
Bojan Nokovic, Andrew Lambe
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Bojan Nokovic
McMaster University, Hamilton, Ontario, Canada
Andrew Lambe
Able Data Science Consulting, Mississauga, Ontario, Canada
Cite this paper as:Nokovic, B., Lambe, A. (2025). Quantitative Endosurgery Process Analysis by Machine Learning Method.
Journal of Systemics, Cybernetics and Informatics, 23(3), 1-7. https://doi.org/10.54808/JSCI.23.03.1
Online ISSN (Journal): 1690-4524
Abstract
This study demonstrates how endoscopic surgical data can be analyzed using a supervised machine learning (ML) classifier. Before the process begins, a computer-generated 3D image representing a safe zone is inserted into the endoscopic view. During surgery, the Laparo- Guard Augmented Reality System collects positional data. We perform two types of analysis on the collected data. First, we analyze how the surgeon handles laparoscopic surgical tools based on the angular velocity and angular acceleration of the tool. Next, we examine the risk associated with the entire surgical process in relation to the safe zone using all collected data, including the average linear and angular speeds of the surgical tool.
