Publications
Brown, J. D., Kuchenbecker, K. J.
Effects of Automated Skill Assessment on Robotic Surgery Training.
Int. J. Med. Robot. 2022;e2492. https://doi.org/10.1002/rcs.2492. (Abstract, Links)
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 (GEARS) 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.
@article{https://doi.org/10.1002/rcs.2492,
author = {Brown, Jeremy D. and Kuchenbecker, Katherine J.},
title = {Effects of Automated Skill Assessment on Robotic Surgery Training},
journal = {The International Journal of Medical Robotics and Computer Assisted Surgery},
volume = {n/a},
number = {n/a},
pages = {e2492},
keywords = {Robotic Surgery, Automated Skill Assessment, Inanimate Training},
doi = {https://doi.org/10.1002/rcs.2492},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rcs.2492},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rcs.2492},
abstract = {Abstract 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 (GEARS) 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. This article is protected by copyright. All rights reserved.}
}