Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
J Endourol. 2022 Feb;36(2):273-278. doi: 10.1089/end.2021.0417.
Robotic surgical performance, in particular suturing, has been associated with postoperative clinical outcomes. Suturing can be deconstructed into components (, , , and ) allowing for the provision of more specific feedback while teaching suturing and more precision when evaluating suturing technical skill and prediction of clinical outcomes. This study evaluates if the technical skill required for particular substeps of the suturing process is associated with the execution of subsequent substeps in terms of technical skill, accuracy, and efficiency. Training and expert surgeons completed standardized sutures on the Mimic™ Flex virtual reality robotic simulator. Video recordings were deidentified, time annotated, and provided technical skill scores for each of the four suturing substeps. Hierarchical Poisson regression with generalized estimating equation was used to examine the association of technical skill rating categories between substeps. Twenty-two surgeons completed 428 suturing attempts with 1669 individual technical skill assessments made. Technical skill scores between substeps of the suturing process were found to be significantly associated. When was ideal, was associated with a significantly greater chance of being ideal risk ratio [RR] = 1.12, = 0.05). In addition, ideal and technical skill scores were each significantly associated with ideal technical skill scores (RR = 1.27, = 0.03; RR = 1.3, = 0.03, respectively). Our study determined that ideal technical skill was associated with increased accuracy and efficiency of select substeps. Our study found significant associations in the technical skill required for completing substeps of suturing, demonstrating inter-relationships within the suturing process. Together with the known association between technical skill and clinical outcomes, training surgeons should focus on mastering not just the overall suturing process, but also each substep involved. Future machine learning efforts can better evaluate suturing, knowing that these inter-relationships exist.
机器人手术性能，特别是缝合，与术后临床结果相关。缝合可以分解为多个组件（进针、出针、结扎和剪线），这使得在教授缝合技术时可以提供更具体的反馈，并且在评估缝合技术技能和预测临床结果时更加精确。本研究评估了缝合过程中特定子步骤所需的技术技能是否与后续子步骤的执行相关，包括技术技能、准确性和效率。培训和专家外科医生在 Mimic™ Flex 虚拟现实机器人模拟器上完成了标准化缝合。对视频记录进行去识别、时间标注，并为每个缝合子步骤提供技术技能评分。使用分层泊松回归和广义估计方程来检查子步骤之间技术技能评分类别的关联。22 名外科医生完成了 428 次缝合尝试，共进行了 1669 次单独的技术技能评估。缝合过程中的子步骤之间的技术技能评分发现存在显著关联。当 处于理想状态时， 与理想状态的可能性显著增加（风险比 [RR] = 1.12， = 0.05）。此外，理想的 和 技术技能评分与理想的 技术技能评分显著相关（RR = 1.27， = 0.03；RR = 1.3， = 0.03，分别）。我们的研究确定，理想的技术技能与选择子步骤的准确性和效率提高相关。我们的研究发现，缝合子步骤完成所需的技术技能之间存在显著关联，表明缝合过程中存在相互关系。结合技术技能与临床结果之间的已知关联，培训外科医生应该不仅关注整体缝合过程，还要关注涉及的每个子步骤。未来的机器学习工作可以更好地评估缝合，因为知道这些相互关系的存在。
