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Assessment of surgical skills using Surgical Processes and Dynamic Time Warping

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Abstract

Toward the creation of new computer-assisted intervention systems, Surgical Process Models (SPMs) is an emerging concept used for analyzing and assessing surgical interventions. SPMs represent Surgical Process (SPs) which are formalized as symbolic structured descriptions of surgical interventions, using a pre-defined level of granularity and a dedicated terminology. In this context, an important challenge is the creation of new metrics for the comparison and the evaluation of SPs. Thus, correlations between these metrics and pre-operative data allow to classify surgeries and highlight specific information on the surgery itself and on the surgeon, such as his/her level of expertise. In this paper, we explore the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm. DTW allows to compute a distance between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences. Indeed, it turns out to be a complementary approach to classical methods focusing on the time and the number of activities differences only. Experiments were carried out on 24 lumbar disc herniation surgeries to discriminate the level of expertise of surgeons according to prior classification of SPs. Unsupervised classification experiments have shown that this approach was able to automatically identify groups of surgeons according to their level of expertise (senior and junior), and opens many perspectives for the creation of new metrics for surgeries comparison and evaluation.
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Dates and versions

inserm-00617017 , version 1 (25-08-2011)

Identifiers

  • HAL Id : inserm-00617017 , version 1

Cite

Germain Forestier, Florent Lalys, Laurent Riffaud, Brivael Trelhu, Pierre Jannin. Assessment of surgical skills using Surgical Processes and Dynamic Time Warping. M2CAI 2011 (MICCAI workshop) - Modelling and Monitoring of Computer-Assisted Intervention, Sep 2011, Toronto, Canada. ⟨inserm-00617017⟩
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