| المؤلفون: |
Ma R; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Kiyasseh D; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA., Laca JA; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Kocielnik R; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA., Wong EY; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Chu TN; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Cen S; Radiology Department, University of Southern California, Los Angeles, California, USA., Yang CH; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Dalieh IS; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Haque TF; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Goldenberg MG; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA., Huang X; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA.; Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA., Anandkumar A; Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA., Hung AJ; Catherine & Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA. |
| مستخلص: |
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. |
