Background: With increased use of robotic surgery in specialties including urology, development of training methods has also intensified. However, current approaches lack the ability to discriminate between operational and surgical skills.
Methods: An automated recording system was used to longitudinally (monthly) acquire instrument motion/telemetry and video for four basic surgical skills - suturing, manipulation, transection, and dissection. Statistical models were then developed to discriminate the human-machine skill differences between practicing expert surgeons and trainees.
Results: Data from six trainees and two experts was analyzed to validate the first ever statistical models of operational skills, and demonstrate classification with very high accuracy (91.7% for masters, and 88.2% for camera motion) and sensitivity.
Conclusions: The paper reports on a longitudinal study aimed at tracking robotic surgery trainees to proficiency, and methods capable of objectively assessing operational and technical skills that would be used in assessing trainee progress at the participating institutions.
Copyright © 2011 John Wiley & Sons, Ltd.