查看文章

Materials and methods

42 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 daVinci 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 under-performers or innate-performers based on a median split of their technical skill scores from R1.

Results

Demographic features were similar between control (n= 20) and feedback group (n= 22)(p> 0.05). Observing the improvement from R1 to R2, feedback group had a significantly larger improvement in needle handling score (0.30 vs-0.02, p= 0.018) when compared to control group, though the improvement of needle driving score was not significant when compared to 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, under-performers in feedback group improved more than 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.