Augmenting Robot-Assisted Pattern Cutting With Periodic Perturbations – Can We Make Dry Lab Training More Realistic?

2024

Article

hi

Objective: Teleoperated robot-assisted minimally-invasive surgery (RAMIS) offers many advantages over open surgery, but RAMIS training still requires optimization. Existing motor learning theories could improve RAMIS training. However, there is a gap between current knowledge based on simple movements and training approaches required for the more complicated work of RAMIS surgeons. Here, we studied how surgeons cope with time-dependent perturbations. Methods: We used the da Vinci Research Kit and investigated the effect of time-dependent force and motion perturbations on learning a circular pattern-cutting surgical task. Fifty-four participants were assigned to two experiments, with two groups for each: a control group trained without perturbations and an experimental group trained with 1Hz perturbations. In the first experiment, force perturbations alternatingly pushed participants' hands inwards and outwards in the radial direction. In the second experiment, the perturbation constituted a periodic up-and-down motion of the task platform. Results: Participants trained with perturbations learned how to overcome them and improve their performances during training without impairing them after the perturbations were removed. Moreover, training with motion perturbations provided participants with an advantage when encountering the same or other perturbations after training, compared to training without perturbations. Conclusion: Periodic perturbations can enhance RAMIS training without impeding the learning of the perturbed task. Significance: Our results demonstrate that using challenging training tasks that include perturbations can better prepare surgical trainees for the dynamic environment they will face with patients in the operating room.

@article{Sharon24-TBME-Perturbations,
  title = {Augmenting Robot-Assisted Pattern Cutting With Periodic Perturbations – Can We Make Dry Lab Training More Realistic?},
  author = {Sharon, Yarden and Nevo, Tifferet and Naftalovich, Daniel and Bahar, Lidor and Refaely, Yael and Nisky, Ilana},
  journal = {IEEE Transactions on Biomedical Engineering},
  month = aug,
  year = {2024},
  doi = {10.1109/TBME.2024.3450702},
  month_numeric = {8}
}