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Toward a standard ontology of surgical process models

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative—OntoSPM Collaborative Action—with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative.

Methods

A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM.

Results

The workshop led to the OntoSPM Collaborative Action—launched in mid-2016—with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use.

Conclusion

The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts’ suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.

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Notes

  1. Web Ontology Language: https://www.w3.org/TR/owl2-overview/.

  2. Ontofox tool: http://ontofox.hegroup.org.

  3. OntoSPM wiki: https://ontospm.univ-rennes1.fr/doku.php.

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Acknowledgements

This work was initiated in the context of the S3PM project which received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the ”Investing for the Future” program under reference ANR-10-LABX-07-01. The work on ontological modeling at the DKFZ is supported by the Federal Ministry of Economics and Energy (BMWi) and the German Aerospace Center (DLR). The work on ontological modeling at Politecnico (Milano) has received funding from the European Unions Horizon 2020 research and innovation program under grant agreement No. H2020-ICT-2016-732515. It was also partly supported by Instituto Politecnico de Castelo Branco and by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013. T. Haidegger is supported through the New National Excellence Program of the Ministry of Human Capacities, his research was partially supported by the Hungarian OTKA PD 116121 grant. This work has been partially supported by ACMIT (Austrian Center for Medical Innovation and Technology), which is funded within the scope of the COMET (Competence Centers for Excellent Technologies) program of the Austrian Government. We acknowledge the financial support of this work by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project. The work at ICCAS was funded by the German Ministry of Education and Research (BMBF).

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Author notes

  1. Chantal Julliard

    Present address: Stryker GmbH, Freiburg, Germany

  2. Darko Katić

    Present address: ArtiMinds Robotics GmbH, Karlsruhe, Germany

Authors and Affiliations

  1. Inserm, LTSI – UMR_S 1099, Univ Rennes, Rennes, France

    Bernard Gibaud, Chantal Julliard, Javier Rojas Balderrama & Pierre Jannin

  2. Division of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Carolin Feldmann, Lena Maier-Hein & Keno März

  3. NEARLAB, Politecnico di Milano, Milan, Italy

    Giancarlo Ferrigno, Elena de Momi & Hirenkumar Nakawala

  4. MIPS Laboratory, University of Haute-Alsace, Mulhouse, France

    Germain Forestier

  5. Instituto Politécnico de Castelo Branco, Castelo Branco, Portugal

    Paulo Gonçalves

  6. IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Paulo Gonçalves

  7. Antal Bejczy Center for Intelligent Robotics, Óbuda University, Budapest, Hungary

    Tamás Haidegger & Dénes Ákos Nagy

  8. Austrian Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria

    Tamás Haidegger & Dénes Ákos Nagy

  9. LIRMM, Université de Montpellier, Montpellier, France

    Chantal Julliard

  10. Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany

    Darko Katić

  11. National Center for Tumor Diseases (NCT), Dresden, Germany

    Stefanie Speidel

  12. Department of General, Abdominal and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany

    Hannes Kenngott & Martin Wagner

  13. Innovation Center Computer Assisted Surgery, Leipzig University, Leipzig, Germany

    Juliane Neumann & Thomas Neumuth

  14. INRIA, Rennes, France

    Javier Rojas Balderrama

Authors
  1. Bernard Gibaud
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  2. Germain Forestier
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  3. Carolin Feldmann
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  4. Giancarlo Ferrigno
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  5. Paulo Gonçalves
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  6. Tamás Haidegger
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  7. Chantal Julliard
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  8. Darko Katić
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  9. Hannes Kenngott
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  10. Lena Maier-Hein
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  11. Keno März
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  12. Elena de Momi
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  13. Dénes Ákos Nagy
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  14. Hirenkumar Nakawala
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  15. Juliane Neumann
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  16. Thomas Neumuth
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  17. Javier Rojas Balderrama
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  18. Stefanie Speidel
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  19. Martin Wagner
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  20. Pierre Jannin
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Corresponding author

Correspondence to Bernard Gibaud.

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This article does not contain any studies with human participants or animal performed by any of the authors.

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Statement of informed consent was not applicable since the manuscript does not contain any patient data.

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Gibaud, B., Forestier, G., Feldmann, C. et al. Toward a standard ontology of surgical process models. Int J CARS 13, 1397–1408 (2018). https://doi.org/10.1007/s11548-018-1824-5

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  • DOI: https://doi.org/10.1007/s11548-018-1824-5

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