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Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos

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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.


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.


The optimized algorithm giving the best performance used spatial features as input (\(\mathrm{Acc}=0.68, \mathrm{Sn}=0.72, \mathrm{Sp}=0.7\)). 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.


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.

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This work was supported by French state funds managed by the ANR within the Investissements d’Avenir Programme (Labex CAMI) under reference ANR-11-LABX-0004. Authors thank the IRT b\(<>\)com for the provision of the software ‘Surgery Workflow Toolbox [annotate]’ used in this study.

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Correspondence to Sandrine Voros.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Study ethics approval was obtained on May 24, 2018 (CECIC Rhône-Alpes-Auvergne, Clermont-Ferrand, IRB 5891)

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Derathé, A., Reche, F., Moreau-Gaudry, A. et al. Predicting the quality of surgical exposure using spatial and procedural features from laparoscopic videos. Int J CARS 15, 59–67 (2020).

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  • Video-based analysis
  • Surgical exposure
  • Surgical expertise
  • Laparoscopic surgery