  During arthroscopic surgeries, surgeons are faced with challenges like
cognitive re-projection of the 2D screen output into the 3D operating site or
navigation through highly similar tissue. Training of these cognitive processes
takes much time and effort for young surgeons, but is necessary and crucial for
their education. In this study we want to show how to recognize states of
confusion of young surgeons during an arthroscopic surgery, by looking at their
eye and head movements and feeding them to a machine learning model. With an
accuracy of over 94\% and detection speed of 0.039 seconds, our model is a step
towards online diagnostic and training systems for the perceptual-cognitive
processes of surgeons during arthroscopic surgeries.
