Bickford Michael, Alruwaili Fayez, Ragab Sara, Rothenberg Hanna, Abedin-Nasab Mohammad
Rowan-Virtua School of Osteopathic Medicine, Rowan University, Stratford, NJ, 08084, USA.
Department of Biomedical Engineering, Rowan University, Glassboro, NJ, 08028, USA.
J Robot Surg. 2025 Jul 22;19(1):412. doi: 10.1007/s11701-025-02559-z.
Robot-assisted surgeries (RAS) have an extremely steep learning curve. Because of this, surgeons have created many methods to practice RAS outside the operating room. These training models usually include animal or plastic models; however, extended reality simulators have recently been introduced into surgical training programs. This systematic review and meta-analysis was conducted to determine if extended reality simulators can improve the performance of robotic novices and how their performance compares to the conventional training of surgeons on surgical robots. Using the PRISMA 2020 guidelines, a systematic review was performed searching PubMed, Embase, Web of Science, and Cochrane library for studies that compared the performance of robotic novices that received no additional training, trained with extended reality, or trained with inanimate physical simulators (conventional additional training). Articles that gauged performance using GEARS or time to complete measurements were included, while articles that did not make this comparison were excluded. A meta-analysis was performed on the 15 studies found using SPSS to compare the performance outcomes of the novices after training. Robotic novices trained with extended reality simulators showed a statistically significant improvement in time to complete (Cohen's d = -0.95, p = 0.02) compared to those with no additional training. Extended reality training also showed no statistically significant difference in performance in time to complete (Cohen's d = 0.65, p = 0.14) or GEARS scores (Cohen's d = -0.093, p = 0.34) compared to robotic novices trained with conventional models. This meta-analysis seeks to determine if extended reality simulators translate complex skills to surgeons in a low-cost and low-risk environment.
机器人辅助手术（RAS）的学习曲线极为陡峭。正因如此，外科医生创造了许多在手术室之外练习RAS的方法。这些训练模型通常包括动物模型或塑料模型；然而，扩展现实模拟器最近已被引入外科培训项目。进行这项系统评价和荟萃分析是为了确定扩展现实模拟器是否能提高机器人新手的表现，以及他们的表现与外科医生在手术机器人上的传统培训相比如何。使用PRISMA 2020指南，进行了一项系统评价，在PubMed、Embase、科学网和考科蓝图书馆中搜索比较未接受额外培训、接受扩展现实培训或接受无生命物理模拟器培训（传统额外培训）的机器人新手表现的研究。纳入了使用GEARS或完成测量时间来衡量表现的文章，而未进行此比较的文章则被排除。对使用SPSS找到的15项研究进行荟萃分析，以比较新手培训后的表现结果。与未接受额外培训的新手相比，接受扩展现实模拟器培训的机器人新手在完成时间上有统计学显著改善（科恩d值=-0.95，p=0.02）。与接受传统模型培训的机器人新手相比，扩展现实培训在完成时间的表现（科恩d值=0.65，p=0.14）或GEARS分数（科恩d值=-0.093，p=0.34）上也没有统计学显著差异。这项荟萃分析旨在确定扩展现实模拟器是否能在低成本和低风险环境中将复杂技能传授给外科医生。
