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Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Emergency Undocking in Robotic Surgery: A Simulation Curriculum

Published on: May 20, 2018

Affordable, web-based surgical skill training and evaluation tool.

Gazi Islam¹, Kanav Kahol², Baoxin Li³

¹Department of Biomedical Informatics, Arizona State University, 13212 E Shea Blvd, Scottsdale, AZ 85259, United States.

Journal of Biomedical Informatics

|November 12, 2015

Summary

This study introduces an automated, video-based system for assessing surgical skills in minimally invasive surgery (MIS) training. It objectively evaluates trainee performance, offering real-time feedback and trend analysis to improve surgical education.

Keywords:

Computer vision Feature detection Skill assessment Surgery Web-based evaluation tool

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Area of Science:

Medical Simulation and Training
Surgical Skill Assessment
Minimally Invasive Surgery (MIS)

Background:

Traditional surgical training requires constant supervision, leading to subjective skill evaluations.
Simulator-based training is increasingly adopted but still needs objective assessment methods.
Current methods for evaluating surgical dexterity are limited and often rely on expert opinion.

Purpose of the Study:

To develop an efficient, effective, and economical video-based skill assessment technique for MIS.
To provide objective, quantitative feedback on trainee performance in surgical skills learning.
To create a platform for secure video upload, performance scoring, and trend analysis.

Main Methods:

Analysis of surgeon's hand and surgical tool movements from training videos.
Detection and quantification of key performance features: smoothness, efficiency, and preciseness.
Development of a web-based tool for video submission and automated performance evaluation.

Main Results:

The system provides objective, real-time on-screen feedback during simulated surgeries.
Automated performance scores are generated at the end of each surgical session.
The web tool enables secure storage of training videos and analysis of performance trends over time.

Conclusions:

The proposed video-based technique offers an objective and scalable solution for MIS skill assessment.
This approach enhances surgical resident training by providing consistent, data-driven feedback.
The system supports continuous improvement in surgical education through performance trend monitoring.