Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy.
Int J Comput Assist Radiol Surg. 2022 Dec;17(12):2315-2323. doi: 10.1007/s11548-022-02712-1. Epub 2022 Jul 8.
Advanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm. This paper aims at evaluating surgeon performance automatically with novel evaluation metrics based on different surgical data.
Urologists ([Formula: see text]) from a hospital were requested to perform a simplified neobladder reconstruction on an ex vivo setup twice with different camera modalities ([Formula: see text]) randomly. They were divided into novices and experts ([Formula: see text], respectively) according to their experience in robot-assisted surgeries. Different performance metrics ([Formula: see text]) are proposed to achieve the surgical skill evaluation, considering both instruments and endoscope. Also, nonparametric tests are adopted to check if there are significant differences when evaluating surgeons performance.
When grouping according to four stages of neobladder reconstruction, statistically significant differences can be appreciated in phase 1 ([Formula: see text]) and phase 2 ([Formula: see text]) with normalized time-related metrics and camera movement-related metrics, respectively. On the other hand, considering experience grouping shows that both metrics are able to highlight statistically significant differences between novice and expert performances in the control protocol. It also shows that the camera-related performance of experts is significantly different ([Formula: see text]) when handling the endoscope manually and when it is automatic.
Surgical skill evaluation, using the approach in this paper, can effectively measure surgical procedures of surgeons with different experience. Preliminary results demonstrate that different surgical data can be fully utilized to improve the reliability of surgical evaluation. It also demonstrates its versatility and potential in the quantitative assessment of various surgical operations.
医学领域的先进发展逐渐增加了公众对手术技能评估的需求。然而,这种评估总是依赖于经验丰富的外科医生的直接观察,既费时又多变。机器人辅助手术的引入为这种评估模式提供了新的可能性。本文旨在基于不同的手术数据,利用新的评估指标自动评估外科医生的表现。
要求来自一家医院的泌尿科医生([公式:见文本])在离体设置上两次使用不同的摄像模式([公式:见文本])进行简化的新膀胱重建。他们根据机器人辅助手术的经验分为新手和专家([公式:见文本])。提出了不同的性能指标([公式:见文本])来实现手术技能评估,同时考虑了器械和内窥镜。此外,还采用非参数检验来检查评估外科医生表现时是否存在显著差异。
当根据新膀胱重建的四个阶段进行分组时,可以在第 1 阶段([公式:见文本])和第 2 阶段([公式:见文本])中看到与时间相关的归一化指标和与摄像机运动相关的指标的统计显著差异。另一方面,考虑经验分组可以看出,在对照方案中,两种指标都能够突出新手和专家表现之间的统计学显著差异。它还表明,当手动操作内窥镜和自动操作时,专家的摄像机相关表现有显著差异([公式:见文本])。
使用本文中的方法进行手术技能评估,可以有效地衡量具有不同经验的外科医生的手术程序。初步结果表明,可以充分利用不同的手术数据来提高手术评估的可靠性。它还展示了其在各种手术操作的定量评估中的通用性和潜力。