Sequential surgical signatures in micro-suturing task
- 73 Downloads
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.
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.
We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise.
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.
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.
KeywordsPattern mining Surgical training Micro-surgery Suturing
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.
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.
This articles does not contain patient data.
- 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
- 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
- 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.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
- 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.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
- 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.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.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.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.Sokal RR, Michener CD (1958) A statistical method for evaluating systematic relationships. Univ Kans Sci Bull 28:1409–1438Google Scholar
- 21.Process mining and automated process discovery software for professionals—fluxicon disco. URL https://fluxicon.com/disco/. Accessed 18 Dec 2017