Introduction: Surgical skill evaluation while performing minimally invasive surgeries is a highly complex task. It is important to objectively assess an individual's technical skills throughout surgical training to monitor progress and to intervene when skills are not commensurate with the year of training. The miniaturization of wireless wearable platforms integrated with sensor technology has made it possible to noninvasively assess muscle activations and movement variability during performance of minimally invasive surgical tasks. Our objective was to use electromyography (EMG) to deconstruct the motions of a surgeon during robotic suturing (RS) and distinguish quantifiable movements that characterize the skill of an experienced expert urologic surgeon from trainees.
Methods: Three skill groups of participants, novice (n = 11), intermediate (n = 12), and expert (n = 3), were enrolled in the study. A total of 12 wireless wearable sensors consisting of surface EMGs and accelerometers were placed along upper extremity muscles to assess muscle activations and movement variability, respectively. Participants then performed a RS task.
Results: EMG-based parameters, total time, dominant frequency, and cumulative muscular workload, were significantly different across the three skill groups. We also found nonlinear movement variability parameters such as correlation dimension, Lyapunov exponent trended differently across the three skill groups.
Conclusions: These findings suggest that economy of motion variables and nonlinear movement variabilities are affected by surgical experience level. Wearable sensor signal analysis could make it possible to objectively evaluate surgical skill level periodically throughout the residency training experience.
Rahul Soangra, Pengbo Jiang, Daniel Haik, Perry Xu, Andrew Brevik, Akhil Peta, Shlomi Tapiero, Jaime Landman, Emmanuel John, and Ralph V. Clayman. Beyond Efficiency: Surface Electromyography Enables Further Insights into the Surgical Movements of Urologists. Journal of Endourology. http://doi.org/10.1089/end.2022.0120
Mary Ann Liebert
Available for download on Tuesday, June 20, 2023