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Updated: Jan 21, 2026
Pupillary Response as Assessment of Effective Seizure Induction by Electroconvulsive Therapy
Published on: April 11, 2019
Using objective robotic automated performance metrics and task-evoked pupillary response to distinguish surgeon
Jessica H Nguyen1, Jian Chen1, Sandra P Marshall2
1Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, University of Southern California Institute of Urology, 1441 Eastlake Avenue, Suite 7416, Los Angeles, CA, 90033, USA.
Purpose:
In this study, we investigate the ability of automated performance metrics (APMs) and task-evoked pupillary response (TEPR), as objective measures of surgeon performance, to distinguish varying levels of surgeon expertise during generic robotic surgical tasks. Additionally, we evaluate the association between APMs and TEPR.
Methods:
Participants completed ten tasks on a da Vinci Xi Surgical System (Intuitive Surgical, Inc.), each representing a surgical skill type: EndoWrist® manipulation, needle targeting, suturing/knot tying, and excision/dissection. Automated performance metrics (instrument motion tracking, EndoWrist® articulation, and system events data) and TEPR were recorded by a systems data recorder (Intuitive Surgical, Inc.) and Tobii Pro Glasses 2 (Tobii Technologies, Inc.), respectively. The Kruskal-Wallis test determined significant differences between groups of varying expertise. Spearman's rank correlation coefficient measured associations between APMs and TEPR.
Results:
Twenty-six participants were stratified by robotic surgical experience: novice (no prior experience; n = 9), intermediate (< 100 cases; n = 9), and experts (≥ 100 cases; n = 8). Several APMs differentiated surgeon experience including task duration (p < 0.01), time active of instruments (p < 0.03), linear velocity of instruments (p < 0.04), and angular velocity of dominant instrument (p < 0.04). Task-evoked pupillary response distinguished surgeon expertise for three out of four task types (p < 0.04). Correlation trends between APMs and TEPR revealed that expert surgeons move more slowly with high cognitive workload (ρ < - 0.60, p < 0.05), while novices move faster under the same cognitive experiences (ρ > 0.66, p < 0.05).
Conclusions:
Automated performance metrics and TEPR can distinguish surgeon expertise levels during robotic surgical tasks. Furthermore, under high cognitive workload, there can be a divergence in robotic movement profiles between expertise levels.
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