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Effects of automated skill assessment on robotic surgery training
Jeremy D Brown1, Katherine J Kuchenbecker2
1Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Background:
Several automated skill-assessment approaches have been proposed for robotic surgery, but their utility is not well understood. This article investigates the effects of one machine-learning-based skill-assessment approach on psychomotor skill development in robotic surgery training.
Methods:
N = 29 trainees (medical students and residents) with no robotic surgery experience performed five trials of inanimate peg transfer with an Intuitive Surgical da Vinci Standard robot. Half of the participants received no post-trial feedback. The other half received automatically calculated scores from five Global Evaluative Assessment of Robotic Skill domains post-trial.
Results:
There were no significant differences between the groups regarding overall improvement or skill improvement rate. However, participants who received post-trial feedback rated their overall performance improvement significantly lower than participants who did not receive feedback.
Conclusions:
These findings indicate that automated skill evaluation systems might improve trainee self-awareness but not accelerate early stage psychomotor skill development in robotic surgery training.
