Automated real time peg and tool detection for the FLS trainer box

Area of Science:

• Medical Simulation
• Surgical Training Technologies
• Computer Vision in Medicine

Background:

• Laparoscopic surgery requires specialized training and objective skill assessment.
• The Fundamentals of Laparoscopic Surgery (FLS) peg transfer task is a standard benchmark for surgical proficiency.
• Current assessment methods may lack real-time, objective feedback on tool manipulation.

Purpose of the Study:

• To develop and validate a real-time tracking system for laparoscopic surgical tools and pegs.
• To enable objective, real-time assessment of the FLS peg transfer task.
• To provide a non-invasive tracking solution compatible with existing training setups.

Main Methods:

• Utilized custom Python code integrated with OpenCV libraries for image processing.
• Implemented image filtration sequences, thresholding functions, and Haar training for object detection.
• Tracked trocar tool and peg positions in real-time within the FLS trainer box environment.

Main Results:

• Achieved accurate real-time tracking of both trocar tool and peg positions.
• Demonstrated the system's effectiveness without altering the standard FLS trainer box setup.
• Validated the feasibility of objective, real-time performance assessment for the peg transfer task.

Conclusions:

• The proposed method offers an effective and non-invasive solution for real-time assessment in laparoscopic surgical training.
• This technology can enhance the objectivity and efficiency of skill evaluation in surgical education.
• Future work may involve integration into broader surgical simulation platforms.