Quantitative Endosurgery Process Analysis by Machine Learning Method

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...

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Bibliographic Details
Published inJournal of systemics, cybernetics and informatics Vol. 23; no. 3; pp. 1 - 7
Main Authors Nokovic, Bojan, Lambe, Andrew
Format Journal Article
LanguageEnglish
Published 01.06.2025
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Summary: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.
ISSN:1690-4524
1690-4524
DOI:10.54808/JSCI.23.03.1