Force-Sensing Enhanced Simulation Environment (ForSense) for laparoscopic surgery training and assessment.

Area of Science:

• Minimally Invasive Surgery
• Surgical Simulation
• Biomedical Engineering

Background:

• Excessive force during laparoscopic surgery contributes to errors, particularly in robotic procedures.
• Objective measurement of tool-tissue interface force is crucial for assessing surgical skill in simulators.
• Existing box trainers lack force-sensing capabilities.

Purpose of the Study:

• Develop an integrated force-sensing unit for laparoscopic box trainers.
• Validate force variables as objective measures of laparoscopic skill.
• Determine the concurrent validity of a revised scoring metric incorporating force error.

Main Methods:

• A force transducer-equipped base plate was retrofitted to a box trainer.
• Participants (3 experience levels) performed peg transfer and suture tasks.
• Force variables and a revised scoring metric with force error penalties were assessed.

Main Results:

• Significant differences in mean, maximum, and overall force magnitudes and force error were observed across experience levels.
• Experts exhibited lower force and faster task completion times than novices.
• Overall force magnitude strongly correlated with experience and task completion time.

Conclusions:

• Box trainers can be enhanced with force sensing for objective skill assessment.
• Force-based outcomes are significantly influenced by expertise and impact surgical safety.
• Integrating force metrics with time-based standards provides a more accurate reflection of surgical skill quality.