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Robotic path planning for surgeon skill evaluation in minimally-invasive sinus surgery
Narges Ahmidi1, Gregory D Hager, Lisa Ishii
1Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. nahmidil@jhu.edu
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
Summary
Expert surgeons minimize tool paths in minimally invasive surgery (MIS). A new surgical path planner (SPP) and descriptive curve coding (DCC) accurately predict surgical skill, achieving 93% accuracy.
Area of Science:
- Surgical Technology
- Medical Simulation
- Robotic Surgery
Background:
- Expert surgeons performing minimally invasive surgery (MIS) optimize tool paths.
- Minimizing tool path length and avoiding collisions are key indicators of surgical expertise.
- Predicting surgical skill from tool path data could enhance training and assessment.
Purpose of the Study:
- To develop and validate computational methods for predicting surgical skill.
- To test the hypothesis that optimized tool paths correlate with higher surgical skill.
- To introduce a surgical path planner (SPP) and descriptive curve coding (DCC) for skill assessment.
Main Methods:
- Developed a surgical path planner (SPP) for functional endoscopic sinus surgery (FESS).
- Created a descriptive curve coding (DCC) method for coordinate-independent motion analysis.
- Evaluated SPP and DCC on recorded FESS training tasks, comparing generated paths to expert motions.
Main Results:
- The SPP accurately predicted Objective Structured Assessment of Technical Skill (OSATS) scores with 88% accuracy.
- The DCC method predicted OSATS scores with 90% accuracy.
- The combined SPP and DCC approach achieved 93% accuracy in identifying surgical skill.
Conclusions:
- Computational analysis of surgical tool paths can effectively predict surgeon skill.
- The SPP and DCC methods offer objective and accurate tools for surgical skill assessment.
- These methods have the potential to improve surgical training and performance evaluation in MIS.
