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A Novel Drill Set for the Enhancement and Assessment of Robotic Surgical Performance

Ro, Charles Y.; Toumpoulis, Ioannis K.; Ashton, Jr., Robert C.; Imielinska, Celina Z.; Jebara, Tony; Shin, Seung H.; Zipkin, J. D.; McGinty, James J.; Todd, George J.; DeRose, Jr., Joseph J.

Background: There currently exist several training modules to improve performance during video-assisted surgery. The unique characteristics of robotic surgery make these platforms an inadequate environment for the development and assessment of robotic surgical performance.

Methods: Expert surgeons (n=4) (greater than 50 clinical robotic procedures and greater than 2 years of clinical robotic experience) were compared to novice surgeons (n=17) (less than 5 clinical cases and limited laboratory experience) using the da Vinci Surgical System. Seven drills were designed to simulate clinical robotic surgical tasks. Performance score was calculated by the equation Time to Completion + (minor error) x 5 + (major error) x 10. The Robotic Learning Curve (RLC) was expressed as a trend line of the performance scores corresponding to each repeated drill.

Results: Performance scores for experts were better than novices in all 7 drills (p less than 0.05). The RLC for novices reflected an improvement in scores (p less than 0.05). In contrast, experts demonstrated a flat RLC for 6 drills and an improvement in one drill (p=0.027).

Conclusion: This new drill set provides a framework for performance assessment during robotic surgery. The inclusion of particular drills and their role in training robotic surgeons of the future awaits larger validation studies.

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Also Published In

Title
Medicine Meets Virtual Reality 13 : The Magical Next Becomes the Medical Now
Publisher
IOS

More About This Work

Academic Units
Biomedical Informatics
Computer Science
Published Here
September 24, 2014
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