关节镜肩关节手术中专家/新手感知的研究

Myat Su Yin, P. Haddawy, Benedikt W. Hosp, P. Sa-ngasoongsong, Thanwarat Tanprathumwong, Madereen Sayo, Supawit Yangyuenpradorn, A. Supratak
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引用次数: 4

摘要

关节镜肩部手术是一种先进的骨科手术,由于肩部复杂的解剖结构和狭窄的导航空间,这也限制了关节镜的视野,因此尤其具有挑战性。在进行关节镜检查时,快速有效地通过关节到达所需位置的能力是必不可少的。在定位和导航器械时,新手常常会在试图将关节镜输出的信息与解剖学背景知识进行三角测量时感到困惑。在这篇论文中,我们报告了第一个关节镜手术的尸体眼动追踪研究的结果,我们调查了专家和新手之间的感知差异。在整个过程中,特别是在观察到受试者感到困惑的过程中,用认知负荷分析来分析新手的感知。在调查这些部分时,凝视数据分析是由来自眼动仪的陀螺仪和加速度传感器的头部旋转和加速度信息补充的。我们还使用收集到的眼动追踪指标来构建一个模型,将受试者分为专家/新手。我们发现头部运动和瞳孔直径与困惑期之间有统计学意义的关系。我们确定了一个指标的子集,我们使用它来构建一个简单的分类器,能够区分新手和专家,准确率为84%。
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A Study of Expert/Novice Perception in Arthroscopic Shoulder Surgery
Arthroscopic shoulder surgery is an advanced orthopedic surgical procedure, which is particularly challenging due to the complex anatomy of the shoulder, and tight spaces for navigation, which also limits the view from the arthroscope. In carrying out arthroscopy, the ability to quickly and effectively navigate through the joint to reach a desired location is essential. Novices often experience confusion in trying to triangulate the information from arthroscopy output with the background knowledge of anatomy while orienting and navigating the instruments. In this paper, we report on the results of the first cadaveric eye-tracking study of arthroscopic surgery in which we investigate differences in perception between experts and novices. Novices' perception is analyzed with cognitive load analysis throughout the procedure and specifically, during the portions of the procedure in which subjects are observed to be confused. In investigating such portions, the gaze data analysis is supplemented with head rotations and acceleration information from gyroscope and accelerometer sensors from the eye tracker. We also use the gathered eye tracking metrics to construct a model to classify subjects into expert/novice. We find statistically significant relations between head movement as well as pupil diameter and periods of confusion. We identify a subset of the metrics that we use to build a simple classifier that is able to distinguish between novices and experts with accuracy of 84%.
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