quantitatively and objectively assessing robotic surgical performance as
well the development and application of a fourth. These four tools are
used to assess the hypothesis that a certain surgical warm-up protocol
improves performance of surgeons on a da Vinci robotic surgical system.
In the protocol, surgeons perform a brief warm-up task on the Mimic
dV-Trainer virtual reality simulator prior to performing one of two
robotic surgery practice tasks.
Of the four techniques used for performance assessment, the three
established techniques consist of basic measures (task time, tool path
length, economy of motion and errors), algorithmic assessment (using
trained Hidden Markov Model machine learning algorithms) and surgeon
assessment (using the Global Evaluative Assessment of Robotics Surgery).
The newly proposed technique called Crowd-Sourced Assessment of
Technical Skill (C-SATS) draws on crowds of people on the Internet to
assess the surgical performance. The evidence that warm-up improves
surgical performance is presented as well as an analysis of the strong
agreement between C-SATS and grades provided by a group of surgeons
trained to assess surgical performance.