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what discrete and fundamental motions (or “surgemes”) make up a simple vertical
suturing task [ 25 ]. They found eight surgemes for each throw with the needle, but
not all of the eight were utilized by every surgeon [ 25 ]. For example, a surgeon
would not need to tighten the suture after a throw with their left hand and their right
hand. Knowing which surgemes are distinctive to training level would help to model
a truly proficient surgeon. As this study only tested one expert and one intermediate
surgeon (albeit over a number of trials), the definition cannot be made yet.
An important note is that the API’s data stream is only one way. Data moves from
the robot to an external computer, never the opposite direction.
ProMIS
ProMIS is a surgical simulator with known validity evidence for laparoscopy but
can be adapted for assessing robotic performance by punching an extra hole in the
simulated abdomen and adding tracking tape to da Vinci instruments [ 26 ]. Already
equipped with optical tracking software and objective performance metrics, ProMIS
proved readily adaptable to robotic applications. ProMIS had already been docu-
mented to show validity evidence as a laparoscopic training tool, and as such was a
useful benchmark in assessing how VR simulators (like the dV-Trainer or da Vinci
Skill Simulator) can train surgeons.
A primary challenge in RALS training as it currently exists is that there is not yet
an established “gold standard.” Therefore, VR simulators may be useful devices, but
without a baseline to which their progress can be compared, data showing user
improvements isn’t as elucidating as it could be. McDonough et al. sought to resolve
this problem using ProMIS, pictured docked to the da Vinci below in Fig. 5.21.
They were able to confirm evidence for face, content, and construct validity for the
Fig. 5.21 ProMIS
simulator with docked da
Vinci [ 27 ]
E.I. George et al.