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Updated: Feb 2, 2026

Subcostal Specimen Removal in Completely Portal Robotic Lobectomy
Published on: April 19, 2024
Validity assessment of a simulation module for robot-assisted thoracic lobectomy.
George Whittaker1, Abdullatif Aydin2, Sinthuri Raveendran3
11 Department of Cardiothoracic Surgery, Royal Papworth Hospital, Cambridge, UK.
This study validated a new virtual reality simulator module for robot-assisted thoracic lobectomy. The RobotiX Mentor module demonstrated feasibility and acceptability for surgical training, showing potential to reduce errors and shorten the learning curve for robotic surgery.
Area of Science:
- Robotic Surgery
- Surgical Simulation
- Virtual Reality
Background:
- Robot-assisted thoracic lobectomy training presents challenges.
- Virtual reality simulators offer a potential solution for surgical education.
- A new thoracic lobectomy module for the RobotiX Mentor simulator was developed.
Purpose of the Study:
- To assess the construct and face validity of a new robotic surgery simulator module for thoracic lobectomy.
- To determine the acceptability and feasibility of this module for surgical training.
Main Methods:
- A prospective, observational study involving novice, intermediate, and expert surgeons.
- Participants completed familiarization tasks and the Guided Robotic Lobectomy module.
- Evaluation included performance metrics and a questionnaire assessing realism, acceptability, and feasibility.
Main Results:
- The module demonstrated moderate construct validity, with significant differences in performance metrics between skill groups.
- Participants rated the simulator and module as realistic, acceptable, and feasible for training.
- Key performance metrics showing differences included time, tissue injury, and instrument collisions.
Conclusions:
- The thoracic lobectomy module for the RobotiX Mentor simulator established face validity, acceptability, and feasibility.
- Moderate construct validity suggests its utility in surgical training.
- This simulation tool has the potential to improve the initial learning curve and reduce errors in live robotic surgeries.
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