  Endoscopic Sinus and Skull Base Surgeries (ESSBSs) is a challenging and
potentially dangerous surgical procedure, and objective skill assessment is the
key components to improve the effectiveness of surgical training, to
re-validate surgeons' skills, and to decrease surgical trauma and the
complication rate in operating rooms. Because of the complexity of surgical
procedures, the variation of operation styles, and the fast development of new
surgical skills, the surgical skill assessment remains a challenging problem.
This work presents a novel Gaussian Process Learning-based heuristic automatic
objective surgical skill assessment method for ESSBSs. Different with classical
surgical skill assessment algorithms, the proposed method 1) utilizes the
kinematic features in surgical instrument relative movements, instead of using
specific surgical tasks or the statistics to assess skills in real-time; 2)
provide informative feedback, instead of a summative scores; 3) has the ability
to incrementally learn from new data, instead of depending on a fixed dataset.
The proposed method projects the instrument movements into the endoscope
coordinate to reduce the data dimensionality. It then extracts the kinematic
features of the projected data and learns the relationship between surgical
skill levels and the features with the Gaussian Process learning technique. The
proposed method was verified in full endoscopic skull base and sinus surgeries
on cadavers. These surgeries have different pathology, requires different
treatment and has different complexities. The experimental results show that
the proposed method reaches 100\% prediction precision for complete surgical
procedures and 90\% precision for real-time prediction assessment.
