Ahmidi Narges, Hager Gregory D, Ishii Lisa, Fichtinger Gabor, Gallia Gary L, Ishii Masaru
Queen's University, Kingston, ON K7L3N6, Canada.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):295-302. doi: 10.1007/978-3-642-15711-0_37.
In the context of minimally invasive surgery, clinical risks are highly associated with surgeons' skill in manipulating surgical tools and their knowledge of the closed anatomy. A quantitative surgical skill assessment can reduce faulty procedures and prevent some surgical risks. In this paper focusing on sinus surgery, we present two methods to identify both skill level and task type by recording motion data of surgical tools as well as the surgeon's eye gaze location on the screen. We generate a total of 14 discrete Hidden Markov Models for seven surgical tasks at both expert and novice levels using a repeated k-fold evaluation method. The dataset contains 95 expert and 139 novice trials of surgery over a cadaver. The results reveal two insights: eye-gaze data contains skill related structures; and adding this info to the surgical tool motion data improves skill assessment by 13.2% and 5.3% for expert and novice levels, respectively. The proposed system quantifies surgeon's skill level with an accuracy of 82.5% and surgical task type of 77.8%.
在微创手术的背景下，临床风险与外科医生操作手术工具的技能及其对闭合解剖结构的了解高度相关。定量手术技能评估可以减少错误操作并预防一些手术风险。在本文聚焦鼻窦手术的研究中，我们提出了两种方法，通过记录手术工具的运动数据以及外科医生在屏幕上的眼睛注视位置来识别技能水平和任务类型。我们使用重复k折评估方法，针对专家级和新手级别的七个手术任务总共生成了14个离散隐马尔可夫模型。该数据集包含95次专家级和139次新手级在尸体上的手术试验。结果揭示了两点：眼睛注视数据包含与技能相关的结构；将此信息添加到手术工具运动数据中，分别使专家级和新手级的技能评估提高了13.2%和5.3%。所提出的系统量化外科医生技能水平的准确率为82.5%，量化手术任务类型的准确率为77.8%。
