Sequential surgical signatures in micro-suturing task

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.