Robotic Path Planning for Surgeon Skill Evaluation in Minimally-Invasive Sinus Surgery

We observe that expert surgeons performing MIS learn to minimize their tool path length and avoid collisions with vital structures. We thus conjecture that an expert surgeon's tool paths can be predicted by minimizing an appropriate energy function. We hypothesize that this reference path will be closer to an expert with greater skill, as measured by an objective measurement instrument such as objective structured assessment of technical skill (OSATS). To test this hypothesis, we have developed a surgical path planner (SPP) for functional endoscopic sinus surgery (FESS). We measure the similarity between an automatically generated reference path and surgical motions of subjects. We also develop a complementary similarity metric by translating tool motion to a coordinate-independent coding of motion, which we call the descriptive curve coding (DCC) method. We evaluate our methods on surgical motions recorded from FESS training tasks. The results show that the SPP reference path predicts the OSATS scores with 88% accuracy. We also show that motions coded with DCC predict OSATS scores with 90% accuracy. Finally, the combination of SPP and DCC identifies surgical skill with 93% accuracy.