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

Abstract

The current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing and enhancing microsurgical anastomosis skills among surgical trainees. We hypothesize that this framework can differentiate the proficiency levels of microsurgeons, aligning with subjective assessments based on the ALI score. We select relevant performance metrics from the literature on laparoscopic skill assessment and human motor control studies, focusing on time, instrument kinematics, and tactile information. This information is measured and estimated by a set of sensors, including cameras, a motion capture system, and tactile sensors. The recorded data is analyzed offline using our proposed evaluation framework. Our study involves 12 participants of different ages ([Formula: see text] years) and genders (nine males and three females), including six novice and six intermediate subjects, who perform surgical anastomosis procedures on a chicken leg model. We show that the proposed set of objective and quantitative metrics to assess skill proficiency aligns with subjective evaluations, particularly the ALI score method, and can effectively differentiate novices from more proficient microsurgeons. Furthermore, we find statistically significant disparities, where microsurgeons with intermediate level of skill proficiency surpassed novices in both task speed, reduced idle time, and smoother, briefer hand displacements. The framework enables accurate skill assessment and provides objective feedback for improving microsurgical anastomosis skills among surgical trainees. By overcoming the subjectivity and limitations of current assessment methods, our approach contributes to the advancement of surgical education and the development of aspiring microsurgeons. Furthermore, our framework emerges to precisely distinguish and classify proficiency levels (novice and intermediate) exhibited by microsurgeons.