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Computerized model for objectively evaluating cutting performance using a laparoscopic box trainer simulator.

Amir Handelman1, Shani Schnaider2, Adva Schwartz-Ossad2

  • 1Department of Electrical Engineering, Faculty of Engineering, Holon Institute of Technology, Holon, Israel. handelmana@hit.ac.il.

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This summary is machine-generated.

This study introduces an automated tool using image processing to objectively assess laparoscopic surgical skills in trainees. The system effectively evaluates cutting performance and differentiates between experienced and novice surgeons.

Keywords:
Boundary extractionImage-processingLaparoscopic box trainer simulatorSignatureSimulationSurgery education

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Area of Science:

  • Medical Education
  • Surgical Simulation
  • Image Processing

Background:

  • Laparoscopic box trainer simulators are increasingly used for surgical skill assessment and training.
  • There is a need for reliable, objective methods to evaluate trainee performance in laparoscopic surgery.
  • This study addresses this need by developing an automated assessment tool.

Purpose of the Study:

  • To introduce an automated tool for assessing laparoscopic cutting performance using image-processing algorithms.
  • To develop a scoring system that objectively evaluates surgical skills based on quantitative parameters.
  • To provide feedback for trainees to improve their laparoscopic proficiency.

Main Methods:

  • Twenty-seven surgical interns participated in training sessions using a laparoscopic box trainer.
  • Trainees performed circular cutting tasks analyzed by custom software.
  • Performance was quantified using standard deviation, circle-cutout area, skewness, and number of peaks.

Main Results:

  • The automated system generated a composite score for laparoscopic circle-cutout performance.
  • Experienced interns achieved higher scores on average than inexperienced interns.
  • Novice trainees showed score improvements across training sessions.

Conclusions:

  • The developed scoring system objectively evaluates laparoscopic cutting performance.
  • The tool can differentiate surgical residents based on their experience levels.
  • Image-processing algorithms provide valuable feedback for skill enhancement in surgical training.