Discovering discriminative and interpretable patterns for surgical motion analysis

Germain Forestier, François Petitjean, Pavel Senin, Fabien Despinoy, Pierre Jannin

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    9 Citations (Scopus)

    Abstract

    The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems make an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care. In this paper, we present a novel approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the discretization of the continuous kinematic data into strings which are then processed to form bags of words. This step allows us to apply discriminative pattern mining technique based on the word occurrence frequency. We show that the patterns identified by the proposed technique can be used to accurately classify individual gestures and skill levels. We also present how the patterns provide a detailed feedback on the trainee skill assessment. Experimental evaluation performed on the publicly available JIGSAWS dataset shows that the proposed approach successfully classifies gestures and skill levels.

    Original languageEnglish
    Title of host publicationArtificial Intelligence in Medicine
    Subtitle of host publication16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21–24, 2017, Proceedings
    EditorsAnnette ten Teije, John H. Holmes, Christian Popow, Lucia Sacchi
    Place of PublicationCham, Switzerland
    PublisherSpringer
    Pages136-145
    Number of pages10
    ISBN (Electronic)9783319597584
    ISBN (Print)9783319597577
    DOIs
    Publication statusPublished - 2017
    EventArtificial Intelligence in Medicine in Europe 2017 - Vienna, Austria
    Duration: 21 Jun 201724 Jun 2017
    Conference number: 16th
    http://aime17.aimedicine.info/home.html
    https://link.springer.com/book/10.1007%2F978-3-319-59758-4 (Proceedings)

    Publication series

    NameLecture Notes in Artificial Intelligence
    PublisherSpringer
    Volume10259
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceArtificial Intelligence in Medicine in Europe 2017
    Abbreviated titleAIME 2017
    CountryAustria
    CityVienna
    Period21/06/1724/06/17
    Internet address

    Keywords

    • Pattern mining
    • Skill assessment
    • Surgical motion analysis

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