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01.06.2015 | Original Article | Ausgabe 6/2015

International Journal of Computer Assisted Radiology and Surgery 6/2015

Automated objective surgical skill assessment in the operating room from unstructured tool motion in septoplasty

Zeitschrift:
International Journal of Computer Assisted Radiology and Surgery > Ausgabe 6/2015
Autoren:
Narges Ahmidi, Piyush Poddar, Jonathan D. Jones, S. Swaroop Vedula, Lisa Ishii, Gregory D. Hager, Masaru Ishii
Wichtige Hinweise

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

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 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

For this study (approved by Institutional Review Board at Johns Hopkins), no written consent was necessary.

Abstract

Purpose

Previous work on surgical skill assessment using intraoperative tool motion has focused on highly structured surgical tasks such as cholecystectomy and used generic motion metrics such as time and number of movements. Other statistical methods such as hidden Markov models (HMM) and descriptive curve coding (DCC) have been successfully used to assess skill in structured activities on bench-top tasks. Methods to assess skill and provide effective feedback to trainees for unstructured surgical tasks in the operating room, such as tissue dissection in septoplasty, have yet to be developed.

Methods

We proposed a method that provides a descriptive structure for septoplasty by automatically segmenting it into higher-level meaningful activities called strokes. These activities characterize the surgeon’s tool motion pattern. We constructed a spatial graph from the sequence of strokes in each procedure and used its properties to train a classifier to distinguish between expert and novice surgeons. We compared the results from our method with those from HMM, DCC, and generic metric-based approaches.

Results

We showed that our method—with an average accuracy of 91 %—performs better or equal than these state-of-the-art methods, while simultaneously providing surgeons with an intuitive understanding of the procedure.

Conclusions

In this study, we developed and evaluated an automated approach to objectively assess surgical skill during unstructured task of tissue dissection in nasal septoplasty.

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