Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions

Area of Science:

• Robotics
• Surgical Technology
• Human-Computer Interaction

Background:

• Robotic assisted minimally invasive surgery (RAMIS) offers advantages but lacks crucial haptic feedback.
• Haptic feedback is vital for surgical procedures, yet its role in RAMIS remains a challenge.
• Understanding haptic feedback's impact on performance and learning aids in developing better control systems.

Purpose of the Study:

• To examine how different haptic feedback conditions affect surgical needle driving performance and learning.
• To develop and utilize novel metrics for assessing needle driving quality and tissue interaction forces.
• To compare performance in open surgery, various teleoperated haptic feedback conditions, and post-teleoperation open surgery.

Main Methods:

• Participants performed a surgical needle driving task on artificial tissue under open and teleoperated conditions.
• Three teleoperation feedback conditions were tested: no feedback, position exchange, and force sensor recording.
• Novel and classical metrics were used to analyze movement trajectories, applied forces, and learning effects.

Main Results:

• No significant difference in final performance or aftereffects was observed across the three haptic feedback conditions.
• Statistically significant improvements during teleoperation were noted in conditions with force feedback, but direct comparisons lacked significance.
• The study developed new metrics for surgical needle driving quality.

Conclusions:

• The advantage of state-of-the-art force feedback for tasks involving homogeneous soft tissue interaction is questionable.
• Developed metrics provide detailed insights into surgical needle driving quality.
• Further research is needed to fully understand and optimize haptic feedback in RAMIS.