An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data

Background: Assessment of surgical skill plays a crucial role in determining competency, monitoring educational programs, and providing trainee feedback. With the changing health care environment, it will likely play an important role in credentialing and maintenance of certification. The ideal skill assessment tool should be unbiased, objective, and accurate. We hypothesize that tool-motion data-how a surgeon moves his/her instruments-and eye-gaze data-what a surgeon looks at when he/she operates-contain sufficient information to quantitatively and objectively evaluate surgical skill. We investigate this hypothesis by developing a statistical model of surgery and testing the model experimentally in the context of endoscopic sinus surgery (ESS). Methods: A total of 378 trials were recorded from 7 expert and 13 novice surgeons while they were performing a series of 9 different ESS tasks. Data was collected using an electromagnetic tracker to record the surgeon's tool and endoscope motions. In addition, the location of surgeon's eye gaze was recorded using an infrared eye tracker camera. This data was fit to the statistical model and used to test the accuracy of skill assessment. Results: The skill of expert surgeons was identified correctly for 94.6% of tasks. For surgeries performed by novice surgeons the proposed model properly recognizes the skill level with 88.6% accuracy. Conclusion: We present an objective and unbiased method for assessing the skill of endoscopic sinus surgeons. Experimental results show that the proposed method successfully identifies the skill levels of both expert and novice surgeons.