Learning curve of robotic assisted microsurgery in surgeons with different skill levels: a prospective preclinical study

Abstract

Achieving precision in microsurgery requires skill, adequate instruments and magnification, as well as extensive training. Dedicated surgical robotic systems have enhanced and expanded the application of (super-)microsurgical techniques by introducing motion scaling and providing improved surgeon ergonomics. In this prospective preclinical trial, we analyzed the learning curve in robotic assisted microsurgery in 13 participants including medical students, residents, and attending physicians. Data on demographics as well as prior experience in surgery, microsurgery, and gaming were collected. In three study sessions, the participants performed nine microsurgical anastomoses each on 2 mm vessel models using the Symani® Surgical System in combination the VITOM 3D exoscope. A senior expert microsurgeon reviewed the de-identified and blinded videos and scored all anastomoses using a modified “Structured Assessment of Microsurgical Skills” (SARMS) score. All participants significantly reduced their time needed per anastomosis and their overall SARMS score, as well as individual scores for motion and speed throughout the trial. We saw a significant correlation of prior years of practice in surgery with the overall mean time and mean SARMS score. In a separate analysis of the three sessions, this influence could no longer be seen in the last session. Furthermore, we found no significant effect of gender, age, hand dominance, or gaming experience on speed and quality of the anastomoses. In this study of 117 robotic assisted anastomoses, a rapid improvement of performance of all participants with different surgical skills levels could be shown, serving as encouraging evidence for further research in the implementation of microsurgical robotic systems.