Surgical skill level assessment using automatic feature extraction methods
Surgical skill level assessment using automatic feature extraction methods
复制标题
使用自动特征提取方法评估手术技能水平
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Majewicz
中科院分区：
文献类型：
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作者：
Marzieh Ershad;R. Rege;A. Majewicz
手术技能的客观和自动评估对于手术机器人训练中使用的手术模拟器的设计非常重要。已经进行了广泛的研究来识别和评估各种评估指标（例如路径长度、完成时间）；然而，这些指标仅在任务完成后提供给用户，并且可能无法充分利用运动数据中的底层信息。本研究提出了一种在执行任务期间在短时间内自动客观评估外科专业水平的方法。我们首先比较三种不同的自动特征提取方法，包括：（1）主成分分析（PCA）、（2）独立成分分析（ICA）和（3）低级位置数据的线性判别分析（LDA），以区分不同专业水平的能力。然后，我们研究最佳特征提取方法在不同时间间隔内的性能，以找到准确预测用户技能水平的最小时间范围。招募了 14 名不同专业水平的受试者在达芬奇训练模拟器上执行两项模拟任务。记录受试者惯用手的手臂关节（肩、肘和腕）的位置以及双手的位置。使用四种分类器（朴素贝叶斯、支持向量机、最近邻和决策树）来确定最佳特征提取方法。结果表明，PCA 与支持向量机相结合，可以在 0.25 秒的时间范围内对专业知识水平进行分类，准确率达到 98%。
Objective and automatic evaluation of surgical skill is important for the design of surgical simulators used in surgical robotics training. Extensive research has been done to identify and evaluate a variety of evaluation metrics (e.g., path length, completion time); however, these metrics are only provided to the user after completion of the task, and may not fully use the underlying information in the movement data. This study proposes a method for automatic and objective evaluation of surgical expertise levels, in short time intervals, during task performance. We first compare three different automatic feature extraction methods including: (1) principle component analysis (PCA), (2) independent component analysis (ICA), and (3) linear discriminant analysis (LDA) on low-level position data, in their ability to distinguish among different expertise levels. We then study the performance of the best feature extraction method in different time intervals, for the purpose of finding the minimal time frame that accurately predicts user skill level. 14 subjects of different expertise levels were recruited to perform two simulated tasks on the da Vinci training simulator. The position of the subjects’ arm joints (shoulder, elbow and wrist) in the dominant hand, as well as the position of both hands, were recorded. Four classifiers (Naive Bayes, support vector machine, nearest neighbor, and Decision Tree) were used to identify the best feature extraction method. The results indicate that PCA in combination with support vector machine can classify expertise levels with an accuracy of 98% in time frames of 0.25 seconds.
影响因子：
4.3
作者：
Bhattacharya, Sourav;Nurmi, Petteri;Poeltz, Thomas
通讯作者：
Poeltz, Thomas