Objective assessment of robot-assisted suturing using a weighted performance score and a virtual simulation platform

Robot-assisted suturing requires precise technical skills and objective assessment methods to support training. This study aimed to define an objective Performance Score for simulated robot-assisted suturing and to examine practice-related changes within each cohort. Six suturing scenarios ordered according to experts' perceived difficulty were implemented in a simulated training environment. Ten expert surgeons (out of 11 enrolled) completed the six scenarios once using the da Vinci Research Kit simulator; afterwards, they completed 20 repetitions of Scenario 4. Twenty-two residents (out of 25 enrolled) performed a training task in which they repeated the same scenario until they achieved a Performance Score >75% three consecutive times, up to a maximum of 10 repetitions. The Performance Score, which was based on expert-derived weights, prioritized accuracy and errors over time. On average, experts exceeded the 75% proficiency threshold, whereas residents initially performed below it but improved with practice (p < 0.001). Experts also showed time differences between scenarios (p = 0.044), and time reduction with practice (p < 0.001), while accuracy and errors remained stable. Residents improved the Performance Score from 74 ± 7% to 80 ± 3%, driven by faster execution (p < 0.001) and greater accuracy (p < 0.001). The proposed Performance Score and suturing simulations are objective tools for training and assessing robot-assisted suturing skills. The findings provide evidence for composite performance indicators that capture multiple dimensions of technical performance and therefore can support personalized and adaptive surgical training programs.