Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California.
J Urol. 2021 Jan;205(1):271-275. doi: 10.1097/JU.0000000000001328. Epub 2020 Aug 18.
Deconstruction of robotic surgical gestures into semantic vocabulary yields an effective tool for surgical education. In this study we disassembled tissue dissection into basic gestures, created a classification system, and showed its ability to distinguish between experts and novices.
Videos of renal hilum preparation during robotic assisted partial nephrectomies were manually reviewed to identify all discrete surgical movements. Identified dissection movements were classified into distinct gestures based on the consensus of 6 expert surgeons. This classification system was then employed to compare expert and novice dissection patterns during the renal hilum preparation.
A total of 40 robotic renal hilum preparation videos were reviewed, representing 16 from 6 expert surgeons (100 or more robotic cases) and 24 from 13 novice surgeons (fewer than 100 robotic cases). Overall 9,819 surgical movements were identified, including 5,667 dissection movements and 4,152 supporting movements. Nine distinct dissection gestures were identified and classified into the 3 categories of single blunt dissection (spread, peel/push, hook), single sharp dissection (cold cut, hot cut and burn dissect) and combination gestures (pedicalize, 2-hand spread, and coagulate then cut). Experts completed 5 of 9 dissection gestures more efficiently than novices (p ≤0.033). In consideration of specific anatomical locations, experts used more peel/push and less hot cut while dissecting the renal vein (p <0.001), and used more pedicalize while dissecting the renal artery (p <0.001).
Using this novel dissection gesture classification system, key differences in dissection patterns can be found between experts/novices. This comprehensive classification of dissection gestures may be broadly applied to streamline surgical education.
将机器人手术动作分解为语义词汇，为手术教学提供了一种有效的工具。本研究将组织解剖分解为基本动作，创建了一个分类系统，并展示了其区分专家和新手的能力。
对机器人辅助部分肾切除术的肾门准备过程中的视频进行手动审查，以识别所有离散的手术动作。根据 6 名专家外科医生的共识，将识别出的解剖动作分类为不同的动作。然后，该分类系统被用于比较肾门准备期间专家和新手的解剖模式。
共回顾了 40 个机器人肾门准备视频，包括 6 名专家外科医生（100 例以上机器人手术）的 16 个视频和 13 名新手外科医生（100 例以下机器人手术）的 24 个视频。共识别出 9819 个手术动作，包括 5667 个解剖动作和 4152 个辅助动作。确定了 9 种不同的解剖动作，并将其分为 3 类：单钝性解剖（展开、剥离/推、钩）、单锐性解剖（冷切、热切和烧伤解剖）和组合动作（pedicalize、2 手展开、凝结后切割）。专家完成 9 个解剖动作中的 5 个比新手更有效（p≤0.033）。考虑到特定的解剖位置，专家在解剖肾静脉时使用更多的剥离/推，而较少使用热切（p<0.001），在解剖肾动脉时使用更多的 pedicalize（p<0.001）。
使用这种新的解剖动作分类系统，可以发现专家/新手之间在解剖模式上的关键差异。这种解剖动作的综合分类可能广泛应用于简化手术教学。
