An objective skill assessment framework for microsurgical anastomosis based on ALI scores

Robot-Assisted Microsurgery Has a Steeper Learning Curve in Microsurgical Novices

INTRODUCTION

Mastering microsurgery requires advanced fine motor skills, hand-eye coordination, and precision, making it challenging for novices. Robot-assisted microsurgery offers benefits, such as eliminating physiological tremors and enhancing precision through motion scaling, which may potentially make learning microsurgical skills easier.

MATERIALS AND METHODS

Sixteen medical students without prior microsurgical experience performed 160 anastomoses in a synthetic model. The students were randomly assigned into two cohorts, one starting with the conventional technique (HR group) and one with robotic assistance (RH group) using the Symani surgical system.

RESULTS

Both cohorts showed a reduction in procedural time and improvement in SAMS scores over successive attempts, with robotic anastomoses demonstrating a 48.2% decrease in time and a 54.6% increase in SAMS scores. The decreases were significantly larger than the RH group (p < 0.05). The quality of the final anastomoses was comparable in both groups (p > 0.05).

DISCUSSION

This study demonstrated a steep preclinical learning curve for robot-assisted microsurgery (RAMS) among novices in a synthetic, preclinical model. No significant differences in SAMS scores between robotic and manual techniques after ten anastomoses. Robot-assisted microsurgery required more time per anastomosis, but the results suggest that experience with RAMS may aid in manual skill acquisition. The study indicates that further exploration into the sequencing of robotic and manual training could be valuable, especially in designing structured microsurgical curricula.