A Comparison of Expert Ratings and Marker-Less Hand Tracking Along OSATS-Derived Motion Scales

Objective: This study creates linear and generalized additive models (GAMs) of video-recorded two-dimensional hand motion (synonymously referred to as hand movements or hand kinematics) to predict expert-rated performance along a series of surgical motion scales. Background: Surgical performance assessments are costly and time consuming. Automatically quantifying hand motion may offload some burden of surgical coaching and intervention by automatically collecting features of psychomotor performance. Methods: Five experts rated anonymized video clips of benchtop suturing and tying tasks (n = 219) along four visual-analog (0-10) performance scales: fluidity of motion, motion economy, tissue handling, and hand coordination. Custom software tracked both participant hands across successive video frames and populated a robust feature set to train a series of predictive models to reproduce the expert ratings. Results: A GAM (which accounts for nonlinear effects) predicted fluidity of motion ratings with slope = 0.71, intercept = 1.98, and R-2 = 0.77 for clinicians of different experience levels. Fluidity of motion and motion economy models outperformed those created to predict hand coordination and tissue handling ratings. Conclusions: Hand motion tracking may not address all contextual features of surgical tasks. Future work will explore how well simulation-based models extrapolate to more dynamic settings of the operating room.

Automated summary

This study used video-recorded hand motion data to create models predicting expert-rated performance on surgical motion scales, potentially enabling automated assessment and assistance in surgical training. The results showed that models predicting fluidity of motion and motion economy outperformed those for hand coordination and tissue handling.