RESUMO
Introduction: Automated skills assessment can provide surgical trainees with objective, personalized feedback during training. Here, we measure the efficacy of artificial intelligence (AI)-based feedback on a robotic suturing task. Materials and Methods: Forty-two participants with no robotic surgical experience were randomized to a control or feedback group and video-recorded while completing two rounds (R1 and R2) of suturing tasks on a da Vinci surgical robot. Participants were assessed on needle handling and needle driving, and feedback was provided via a visual interface after R1. For feedback group, participants were informed of their AI-based skill assessment and presented with specific video clips from R1. For control group, participants were presented with randomly selected video clips from R1 as a placebo. Participants from each group were further labeled as underperformers or innate-performers based on a median split of their technical skill scores from R1. Results: Demographic features were similar between the control (n = 20) and feedback group (n = 22) (p > 0.05). Observing the improvement from R1 to R2, the feedback group had a significantly larger improvement in needle handling score (0.30 vs -0.02, p = 0.018) when compared with the control group, although the improvement of needle driving score was not significant when compared with the control group (0.17 vs -0.40, p = 0.074). All innate-performers exhibited similar improvements across rounds, regardless of feedback (p > 0.05). In contrast, underperformers in the feedback group improved more than the control group in needle handling (p = 0.02). Conclusion: AI-based feedback facilitates surgical trainees' acquisition of robotic technical skills, especially underperformers. Future research will extend AI-based feedback to additional suturing skills, surgical tasks, and experience groups.
RESUMO
Background: There is no standard for the feedback that an attending surgeon provides to a training surgeon, which may lead to variable outcomes in teaching cases. Objective: To create and administer standardized feedback to medical students in an attempt to improve performance and learning. Design setting and participants: A cohort of 45 medical students was recruited from a single medical school. Participants were randomly assigned to two groups. Both completed two rounds of a robotic surgical dissection task on a da Vinci Xi surgical system. The first round was the baseline assessment. In the second round, one group received feedback and the other served as the control (no feedback). Outcome measurements and statistical analysis: Video from each round was retrospectively reviewed by four blinded raters and given a total error tally (primary outcome) and a technical skills score (Global Evaluative Assessment of Robotic Surgery [GEARS]). Generalized linear models were used for statistical modeling. According to their initial performance, each participant was categorized as either an innate performer or an underperformer, depending on whether their error tally was above or below the median. Results and limitations: In round 2, the intervention group had a larger decrease in error rate than the control group, with a risk ratio (RR) of 1.51 (95% confidence interval [CI] 1.07-2.14; p = 0.02). The intervention group also had a greater increase in GEARS score in comparison to the control group, with a mean group difference of 2.15 (95% CI 0.81-3.49; p < 0.01). The interaction effect between innate performers versus underperformers and the intervention was statistically significant for the error rates, at F(1,38) = 5.16 (p = 0.03). Specifically, the intervention had a statistically significant effect on the error rate for underperformers (RR 2.23, 95% CI 1.37-3.62; p < 0.01) but not for innate performers (RR 1.03, 95% CI 0.63-1.68; p = 0.91). Conclusions: Real-time feedback improved performance globally compared to the control. The benefit of real-time feedback was stronger for underperformers than for trainees with innate skill. Patient summary: We found that real-time feedback during a training task using a surgical robot improved the performance of trainees when the task was repeated. This feedback approach could help in training doctors in robotic surgery.