Sequential surgical signatures in micro-suturing task
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.
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