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Int J Comput Assist Radiol Surg. 2020 Jan;15(1):59-67. doi: 10.1007/s11548-019-02072-3. Epub 2019 Oct 31.

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

Author information

1
Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, 38000, Grenoble, France.
2
Department of Digestive Surgery, CHU de Grenoble, 38000, Grenoble, France.
3
Clinical Investigation Centre - Innovative Technology, INSERM & CHUGA & UGA, 38000, Grenoble, France.
4
LTSI - UMR_S 1099, Université de Rennes, 35000, Rennes, France.
5
INSERM, 35000, Rennes, France.
6
Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, 38000, Grenoble, France. sandrine.voros@univ-grenoble-alpes.fr.

Abstract

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 ([Formula: see text]). 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.

KEYWORDS:

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

PMID:
31673963
DOI:
10.1007/s11548-019-02072-3

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