Computer-aided feedback of surgical knot tying using optical tracking.

BACKGROUND

Quantifying the information content of hand motion during surgical knot tying using information theory based entropy measures enables the comparison of different groups: novice and expert. We hypothesized that complexity would differ between the 2 groups and predicted based on motor learning models that complexity/information would reduce with increased expertise.

METHODS

Six degrees of freedom hand-motion data during surgical knot tying were acquired using an infrared optical hand tracking device. Multiple data samples were obtained from 2 groups: novice (third-year medical students) and expert (attending surgeons). After preprocessing each knot tying data sample into a binary symbolic time series, 3 nonlinear complexity measures were calculated: Lempel Ziv complexity, Shannon entropy, and Renyi entropy. The Shannon and Renyi entropies were calculated using a word length of 6. A Student t test was used to test whether the 2 groups were from the same population when using these entropy measures, applying a p value of 0.05 to reject the null hypothesis.

RESULTS

The expert surgeons were found to have less complex patterns of motion compared with the novice group. This finding was statistically significant using Lempel Ziv complexity (p = 0.004), Shannon entropy (p = 0.006), and Renyi entropy with q = 2 (p = 0.006). Using Renyi entropy with q = 0.5, the 2 groups were not significantly different (p = 0.26).

CONCLUSIONS

The ability to separate novice from expert populations during surgical knot tying using information theory entropy measures could form the basis of a low-cost educational tool to provide feedback and to assess skill acquisition using low-fidelity bench models.