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We have previously developed a system for deconstructing robotic tissue dissection into its most basic elements (i.e., surgical gestures). Our objective was to investigate the efficacy of these gestures (ineffective, ideal, or erroneous) during a robotic dissection task.

Expert surgeons (>100 robotic cases) and novices (no cases) performed a dissection task using a clementine model (participants were instructed to peel the skin and remove a wedge of fruit). Videos of this task were reviewed, and each gesture was manually classified as a type of dissection gesture (e.g. cold cut, two-hand spread; Figure 1a) or supporting gesture (e.g. retraction, camera move) using a previously described dissection gestures classification system. Each gesture was subsequently labeled as: a) ineffective (did not affect the tissue as intended), b) ideal (impacted tissue as intended), or c …