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01.06.2015 | Original Article | Ausgabe 6/2015

International Journal of Computer Assisted Radiology and Surgery 6/2015

Automatic phase prediction from low-level surgical activities

Zeitschrift:
International Journal of Computer Assisted Radiology and Surgery > Ausgabe 6/2015
Autoren:
Germain Forestier, Laurent Riffaud, Pierre Jannin
Wichtige Hinweise

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

Informed consent was obtained from all individual participants included in the study.

Abstract

Purpose

Analyzing surgical activities has received a growing interest in recent years. Several methods have been proposed to identify surgical activities and surgical phases from data acquired in operating rooms. These context-aware systems have multiple applications including: supporting the surgical team during the intervention, improving the automatic monitoring, designing new teaching paradigms.

Methods

In this paper, we use low-level recordings of the activities that are performed by a surgeon to automatically predict the current (high-level) phase of the surgery. We augment a decision tree algorithm with the ability to consider the local context of the surgical activities and a hierarchical clustering algorithm.

Results

Experiments were performed on 22 surgeries of lumbar disk herniation. We obtained an overall precision of 0.843 in detecting phases of 51,489 single activities. We also assess the robustness of the method with regard to noise.

Conclusion

We show that using the local context allows us to improve the results compared with methods only considering single activity. Experiments show that the use of the local context makes our method very robust to noise and that clustering the input data first improves the predictions.

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