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Sequential surgical signatures in micro-suturing task

  • Arnaud Huaulmé
  • Kanako Harada
  • Germain Forestier
  • Mamoru Mitsuishi
  • Pierre Jannin
Original Article

Abstract

Purpose

Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects.

Methods

In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures.

Validation

We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise.

Results

We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases.

Conclusion

We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems.

Keywords

Pattern mining Surgical training Micro-surgery Suturing 

Notes

Acknowledgements

This work was funded by ImPACT Program of Council for Science, Technology and Innovation, Cabinet Office, Government of Japan. Authors thanks the IRT b<>com for the provision of the software “Surgery Workflow Toolbox [annotated],” used for this work.

Compliance with ethical standards

Conflict of interest

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.

Informed consent

This articles does not contain patient data.

Supplementary material

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Supplementary material 1 (avi 974510 KB)
11548_2018_1775_MOESM2_ESM.avi (514.8 mb)
Supplementary material 2 (avi 527130 KB)

References

  1. 1.
    Jannin P, Raimbault M, Morandi X, Gibaud B (2001) Modeling surgical procedures for multimodal image-guided neurosurgery. In: Niessen Wiro J, Viergever Max A (eds) Medical image computing and computer-assisted intervention MICCAI 2001. Number 2208 in lecture notes in computer science. Springer, Berlin, pp 565–572CrossRefGoogle Scholar
  2. 2.
    Lalys F, Jannin P (2013) Surgical process modelling: a review. Int J Comput Assist Radiol Surg 9(3):495–511CrossRefPubMedGoogle Scholar
  3. 3.
    Reiley CE, Hager GD (2009) Task versus subtask surgical skill evaluation of robotic minimally invasive surgery. In: Medical image computing and computer-assisted intervention MICCAI 2009. Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 435–442Google Scholar
  4. 4.
    Despinoy F, Bouget D, Forestier G, Penet C, Zemiti N, Poignet P, Jannin P (2016) Unsupervised trajectory segmentation for surgical gesture recognition in robotic training. IEEE Trans Biomed Eng 63(6):1280–1291CrossRefPubMedGoogle Scholar
  5. 5.
    Neumuth T, Strau G, Meixensberger J, Lemke HU, Burgert O (2006) Acquisition of process descriptions from surgical interventions. In: Bressan S, Kng J, Wagner R (eds) Database and expert systems applications. Number 4080 in lecture notes in computer science. Springer, Berlin, pp 602–611CrossRefGoogle Scholar
  6. 6.
    Padoy N, Blum T, Feussner H, Berger MO, Navab N (2008) On-line recognition of surgical activity for monitoring in the operating room. In: IAAI, pp 1718–1724Google Scholar
  7. 7.
    Padoy N, Blum T, Ahmadi S-A, Feussner H, Berger M-O, Navab N (2012) Statistical modeling and recognition of surgical workflow. Med Image Anal 16(3):632–641CrossRefPubMedGoogle Scholar
  8. 8.
    Bouarfa L, Jonker PP, Dankelman J (2011) Discovery of high-level tasks in the operating room. J Biomed Inform 44(3):455–462CrossRefPubMedGoogle Scholar
  9. 9.
    James A, Vieira D, Lo B, Darzi A, Yang G-Z (2007) Eye-gaze driven surgical workflow segmentation. Med Image Comput Comput Assist Interv MICCAI 2007:110–117Google Scholar
  10. 10.
    Ko S-Y, Kim J, Lee W-J, Kwon D-S (2007) Surgery task model for intelligent interaction between surgeon and laparoscopic assistant robot. Int J Assit Robot Mech 8(1):38–46Google Scholar
  11. 11.
    Lalys F, Bouget D, Riffaud L, Jannin P (2012) Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures. Int J Comput Assist Radiol Surg 8(1):39–49CrossRefPubMedGoogle Scholar
  12. 12.
    Forestier G, Lalys F, Riffaud L, Collins DL, Meixensberger J, Wassef SN, Neumuth T, Goulet B, Jannin P (2013) Multi-site study of surgical practice in neurosurgery based on surgical process models. J Biomed Inform 46(5):822–829CrossRefPubMedGoogle Scholar
  13. 13.
    Huaulmé A, Voros S, Riffaud L, Forestier G, Moreau-Gaudry A, Jannin P (2017) Distinguishing surgical behavior by sequential pattern discovery. J Biomed Inform 67:34–41CrossRefPubMedGoogle Scholar
  14. 14.
    Riffaud L, Neumuth T, Morandi X, Trantakis C, Meixensberger J, Burgert O, Trelhu B, Jannin P (2010) Recording of surgical processes: a study comparing senior and junior neurosurgeons during lumbar disc herniation surgery. Oper Neurosurg 67:ons325–ons332CrossRefGoogle Scholar
  15. 15.
    Neumuth T, Wiedemann R, Foja C, Meier P, Schlomberg J, Neumuth D, Wiedemann P (2010) Identification of surgeon individual treatment profiles to support the provision of an optimum treatment service for cataract patients. J Ocul Biol Dis Inf 3(2):73–83CrossRefGoogle Scholar
  16. 16.
    Cao C, MacKenzie CL, Payandeh S (1996) Task and motion analyses in endoscopic surgery. In: Proceedings ASME dynamic systems and control division. Citeseer, pp 583–590Google Scholar
  17. 17.
    Forestier G, Petitjean F, Senin P, Despinoy F, Jannin P (2017) Discovering discriminative and interpretable patterns for surgical motion analysis. In: Conference on artificial intelligence in medicine in Europe. Springer, Berlin, pp 136–145Google Scholar
  18. 18.
    Mitsuishi M, Morita A, Sugita N, Sora S, Mochizuki R, Tanimoto K, Baek YM, Takahashi H, Harada K (2013) Master-slave robotic platform and its feasibility study for micro-neurosurgery: master-slave robotic platform for microneurosurgery. Int J Med Robot Comput Assist Surg 9(2):180–189CrossRefGoogle Scholar
  19. 19.
    Garraud C, Gibaud B, Penet C, Gazuguel G, Dardenne G, Jannin P (2014) An ontology-based software suite for the analysis of surgical process model. In: Proceedings of Surgetica’2014. Chambery, France, pp 243–245Google Scholar
  20. 20.
    Sokal RR, Michener CD (1958) A statistical method for evaluating systematic relationships. Univ Kans Sci Bull 28:1409–1438Google Scholar
  21. 21.
    Process mining and automated process discovery software for professionals—fluxicon disco. URL https://fluxicon.com/disco/. Accessed 18 Dec 2017

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Univ Rennes, Inserm, LTSI - UMR_S 1099RennesFrance
  2. 2.Department of Mechanical EngineeringThe University of TokyoBunkyo-kuJapan
  3. 3.MIPSUniversity of Haute-AlsaceMulhouseFrance

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