Using Automated Continuous Instrument Tracking to Benchmark Simulated Laparoscopic Performance and Personalize...

Area of Science:

• Surgical Education
• Medical Simulation
• Performance Analytics

Background:

• Laparoscopic simulation is crucial for surgical training, but assessing performance objectively is challenging.
• Traditional observation methods are time-consuming and subjective.
• SurgTrac software offers continuous, automated, real-time, objective feedback for laparoscopic box-trainers.

Purpose of the Study:

• To assess the relationship between the number of task attempts and performance in laparoscopic simulation.
• To determine if performance improvement with repetition can be modeled using learning curves for benchmarking.

Main Methods:

• Retrospective analysis of anonymized SurgTrac data (10/2016-05/2019).
• Assessed the thread transfer task, analyzing task duration and instrument metrics (distance, speed, acceleration, hand ratio).
• Used curve estimation regression to model the relationship between attempt number and performance metrics.

Main Results:

• Analysis of 13,027 thread transfer attempts by 578 users.
• Performance metrics significantly improved with practice across the cohort.
• Task duration and instrument tip travel distance decreased, while ambidexterity increased with more attempts.
• Individual performance aligned with predicted learning curves.

Conclusions:

• The largest database of simulated laparoscopic task performances confirms that practice leads to improved skills.
• Learning curves derived from peer-group data can serve as objective benchmarks.
• Objective assessment supports personalized surgical training and skill development.