SurgTrac Metrics

Objective performance metrics

SurgTrac uses innovative instrument tracking technology to measure the movement of your instruments as you perform each task:

 

The tracking data is converted into performance metrics for each instrument.
SurgTrac then generates natural language feedback to help you understand the metrics and highlight areas for improvement to refine your technique (green and red text in the screenshot below). 
The metrics are automatically uploaded to your SurgTrac cloud portfolio - giving you an online record of your skills progression.

 

Metrics are reported for left (blue), right (red) and both instruments combined (grey) - with averages from your previous performances at that task shown in square brackets. 
Time: The less time it takes to complete a task, the more efficient you have been at completing it. Precision shouldn't be traded for speed, but the three target times for each Module have been set by experts able to complete each task with both precision and speed.  
Instrument Path Distance: This is a measure of precision of control of the instruments. Experts are able to complete the Modules with a significantly lower instrument path distance than trainees. 
Handedness: Experts utilise both hands effectively to efficiently complete the Modules. Aim for a ratio of 65:35% or better for most tasks to ensure you are becoming as ambidextrous as possible. 
% Time off screen: Keeping the instruments within the operative field of view is a key skill minimally invasive surgery. Aim to keep this figure as low as possible for all tasks. 
Distance between instruments: This is a measure of 'economy of area'. The lower this figure, the closer the control of your instruments and the less they have been moving around without purpose. 
Speed, Acceleration & Motion Smoothness: These metrics are interesting. Studies are yet to conclusively demonstrate their value. SurgTrac is a tool which will allow academics to help elucidate this. 

 

Evidence Base:

Mason, J. D., Ansell, J., Warren, N., & Torkington, J. (2013). Is motion analysis a valid tool for assessing laparoscopic skill? Surgical Endoscopy, 27(5), 1468–1477. http://doi.org/10.1007/s00464-012-2631-7

Zendejas, B., Brydges, R., Hamstra, S. J., & Cook, D. A. (2013). State of the Evidence on Simulation-Based Training for Laparoscopic Surgery. Annals of Surgery, 257(4), 586–593. http://doi.org/10.1097/SLA.0b013e318288c40b

Oropesa, I. I., Sánchez-González, P. P., Chmarra, M. K. M., Lamata, P. P., Fernández, A. A., Sánchez-Margallo, J. A. J., et al. (2013). EVA: Laparoscopic Instrument Tracking Based on Endoscopic Video Analysis for Psychomotor Skills Assessment. Surgical Endoscopy, 27(3), 1029–1039. http://doi.org/10.1007/s00464-012-2513-z

Partridge, R. W., Hughes, M. A., Brennan, P. M., & Hennessey, I. A. M. (2014). Accessible Laparoscopic Instrument Tracking (“InsTrac”): Construct Validity in a Take-Home Box Simulator. Journal of Laparoendoscopic & Advanced Surgical Techniques, 24(8), 578–583. http://doi.org/10.1089/lap.2014.0015

Partridge, R. W., Brown, F. S., Brennan, P. M., Hennessey, I. A. M., & Hughes, M. A. (2015). The LEAPTM Gesture Interface Device and Take-Home Laparoscopic Simulators: A Study of Construct and Concurrent Validity. Surgical Innovation, 1553350615594734. http://doi.org/10.1177/1553350615594734