London Kent Surrey Sussex Deanery, London, UK.
Division of Surgery, Department of Surgery and Cancer, Imperial College London, London, UK.
Eye (Lond). 2014 Jan;28(1):78-84. doi: 10.1038/eye.2013.211. Epub 2013 Sep 27.
Training within a proficiency-based virtual reality (VR) curriculum may reduce errors during real surgical procedures. This study used a scientific methodology to develop a VR training curriculum for phacoemulsification surgery (PS).
Ten novice-(n) (performed <10 cataract operations), 10 intermediate-(i) (50-200), and 10 experienced-(e) (>500) surgeons were recruited. Construct validity was defined as the ability to differentiate between the three levels of experience, based on the simulator-derived metrics for two abstract modules (four tasks) and three procedural modules (five tasks) on a high-fidelity VR simulator. Proficiency measures were based on the performance of experienced surgeons.
Abstract modules demonstrated a 'ceiling effect' with construct validity established between groups (n) and (i) but not between groups (i) and (e)-Forceps 1 (46, 87, and 95; P<0.001). Increasing difficulty of task showed significantly reduced performance in (n) but minimal difference for (i) and (e)-Anti-tremor 4 (0, 51, and 59; P<0.001), Forceps 4 (11, 73, and 94; P<0.001). Procedural modules were found to be construct valid between groups (n) and (i) and between groups (i) and (e)-Lens-cracking (0, 22, and 51; P<0.05) and Phaco-quadrants (16, 53, and 87; P<0.05). This was also the case with Capsulorhexis (0, 19, and 63; P<0.05) with the performance decreasing in the (n) and (i) group but improving in the (e) group (0, 55, and 73; P<0.05) and (0, 48, and 76; P<0.05) as task difficulty increased.
Experienced/intermediate benchmark skill levels are defined allowing the development of a proficiency-based VR training curriculum for PS for novices using a structured scientific methodology.
在基于熟练度的虚拟现实 (VR) 课程中进行培训可能会减少实际手术过程中的错误。本研究采用科学方法为白内障超声乳化手术 (PS) 开发了 VR 培训课程。
招募了 10 名新手-(n)（进行了 <10 例白内障手术）、10 名中级-(i)（50-200 例）和 10 名经验丰富-(e)（>500 例）外科医生。构念效度定义为基于高保真 VR 模拟器上的两个抽象模块（四个任务）和三个程序模块（五个任务）的模拟器衍生指标，区分三个经验水平的能力。熟练程度衡量标准基于经验丰富的外科医生的表现。
抽象模块在组 (n) 和 (i) 之间显示出“上限效应”，但在组 (i) 和 (e) 之间没有显示出构念效度-镊子 1（46、87 和 95；P<0.001）。随着任务难度的增加，(n) 的表现明显下降，但 (i) 和 (e) 的差异极小-抗颤 4（0、51 和 59；P<0.001），镊子 4（11、73 和 94；P<0.001）。程序模块在组 (n) 和 (i) 之间以及组 (i) 和 (e) 之间被发现具有构念效度-晶状体破裂（0、22 和 51；P<0.05）和超声乳化象限（16、53 和 87；P<0.05）。Capsulorhexis 也是如此（0、19 和 63；P<0.05），(n) 和 (i) 组的表现下降，但 (e) 组的表现提高（0、55 和 73；P<0.05）和（0、48 和 76；P<0.05）随着任务难度的增加。
使用结构化科学方法，为新手定义了经验丰富/中级的基准技能水平，为 PS 开发了基于熟练度的 VR 培训课程。
