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Evaluation of a 3D-Printed Transoral Robotic Surgery Simulator Utilizing Artificial Tissue
Alexander T Murr1, Catherine J Lumley1, Richard H Feins2
1Department of Otolaryngology-Head and Neck Surgery, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.
The Laryngoscope
|December 9, 2021
Summary
This study validates a new, low-cost simulator for transoral robotic surgery (TORS) training. The simulator effectively distinguished between novice and experienced surgeons, supporting its use in robotic surgery education.
Area of Science:
- Robotics in Surgery
- Surgical Simulation
- Medical Education
Background:
- Transoral robotic surgery (TORS) training presents unique challenges due to limited robot access and specialized skill requirements.
- Existing virtual reality simulators lack head and neck simulations, creating a gap in TORS training.
- There is a need for accessible and effective simulation tools for TORS procedures.
Purpose of the Study:
- To evaluate the construct validity of a novel, low-cost transoral robotic surgery (TORS) simulator.
- To assess the simulator's ability to differentiate between surgeons with varying levels of robotic experience.
- To provide a foundation for further validation and development of TORS training modules.
Main Methods:
- A modular TORS simulator was developed using 3D-printed oral structures and artificial tissues.
- Sixteen surgeons with diverse robotic experience participated in simulated tonsil and tongue base tumor resections using the da Vinci SI robot.
- Performance was assessed using a modified Global Evaluative Assessment of Robotic Surgery (GEARS) criterion, with blinded evaluation of video recordings.
Main Results:
- Surgeons with prior robotic training or experience achieved significantly higher GEARS scores than novices (32 vs. 20.5; P < .001).
- GEARS scores correlated with experience: novices scored 54%, prior experience 82.3%, and robotically trained surgeons 97.1% of total points.
- The simulator demonstrated clear differentiation based on operator experience levels.
Conclusions:
- The developed TORS simulator successfully differentiates novice from experienced and robotically trained surgeons.
- Findings support the construct validity of this low-cost TORS simulator prototype.
- This simulator provides a viable platform for future research into predictive validity for TORS training.
