Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos
Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos
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DOI：
10.1007/s11548-019-02072-3
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发表时间：
2020-01-01
影响因子：
3
通讯作者：
Voros, Sandrine
中科院分区：
文献类型：
--
作者：
Derathe, Arthur;Reche, Fabian;Voros, Sandrine
目的：评估外科手术的质量是微创手术中的一个主要问题。我们提出了一种自下而上的方法，基于对袖状胃切除手术的研究，我们分析了我们认为是外科专业知识的一个重要指标：手术场景的暴露。我们首先利用从腹腔镜视频中提取的特征来预测这一指标，然后分析所提取的描述手术实践的特征对这一指标的影响。方法：在一项单中心研究中，29名患者接受了由两名确认的外科医生进行的袖状胃切除术。从视频的空间和程序注释中提取特征，并由外科专家评估特定时刻手术暴露的质量。这些特征被用作分类器(线性判别分析和支持向量机)的输入，以预测专门知识指标。结果：优化后的算法以空间特征作为输入(Acc=0.68，Sn=0.72，Sp=0.7\Documentclass[12pt]=0.68，mathm{Sn}=0.72，mathm{Sp}=0.7$\end{Document})。它还同样预测了这两类指标，尽管它们之间存在很强的不平衡。通过分析算法中输入特征的选择，可以比较算法的不同配置，并显示手术暴露与外科医生实践之间的联系。结论：这一初步研究验证了根据空间特征预测手术暴露的可能性。对算法选择的特征簇的分析也显示了令人鼓舞的结果和潜在的临床解释。
Purpose: Evaluating the quality of surgical procedures is a major concern in minimally invasive surgeries. We propose a bottom-up approach based on the study of Sleeve Gastrectomy procedures, for which we analyze what we assume to be an important indicator of the surgical expertise: the exposure of the surgical scene. We first aim at predicting this indicator with features extracted from the laparoscopic video feed, and second to analyze how the extracted features describing the surgical practice influence this indicator.Method: Twenty-nine patients underwent Sleeve Gastrectomy performed by two confirmed surgeons in a monocentric study. Features were extracted from spatial and procedural annotations of the videos, and an expert surgeon evaluated the quality of the surgical exposure at specific instants. The features were used as input of a classifier (linear discriminant analysis followed by a support vector machine) to predict the expertise indicator. Features selected in different configurations of the algorithm were compared to understand their relationships with the surgical exposure and the surgeon's practice.Results : The optimized algorithm giving the best performance used spatial features as input (Acc=0.68,Sn=0.72,Sp=0.7\documentclass[12pt]=0.68, mathrm{Sn}=0.72, \mathrm{Sp}=0.7$$\end{document}). It also predicted equally the two classes of the indicator, despite their strong imbalance. Analyzing the selection of input features in the algorithm allowed a comparison of different configurations of the algorithm and showed a link between the surgical exposure and the surgeon's practice.Conclusion: This preliminary study validates that a prediction of the surgical exposure from spatial features is possible. The analysis of the clusters of feature selected by the algorithm also shows encouraging results and potential clinical interpretations.