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

Area of Science:

• Robotics in Surgery
• Motor Learning Theory
• Surgical Training Simulation

Background:

• Robot-assisted minimally-invasive surgery (RAMIS) presents advantages over open surgery but requires optimized training methods.
• Current motor learning theories offer potential for RAMIS training, yet a gap exists between simple movement studies and complex surgical tasks.
• Understanding how surgeons adapt to dynamic challenges is crucial for effective RAMIS training.

Purpose of the Study:

• To investigate the impact of time-dependent force and motion perturbations on learning a surgical pattern-cutting task.
• To evaluate the effectiveness of perturbation-based training in enhancing surgical skill acquisition and adaptability.

Main Methods:

• Utilized the da Vinci Research Kit for a circular pattern-cutting task with 54 participants.
• Two experiments were conducted: one with force perturbations and another with motion perturbations.
• Participants were divided into control (no perturbations) and experimental (1 Hz perturbations) groups.

Main Results:

• Training with perturbations allowed participants to overcome challenges and improve performance during practice.
• Post-training performance was not negatively impacted after perturbations were removed.
• Motion perturbation training conferred an advantage in adapting to subsequent perturbations.

Conclusions:

• Periodic perturbations can effectively enhance robot-assisted minimally-invasive surgery (RAMIS) training.
• This training approach improves adaptability without compromising the learning of the core surgical task.