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Comparative analysis of surgical processes for image-guided endoscopic sinus surgery

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

This study proposes a method to analyze surgical performance by modeling, aligning, and comparing surgical processes. This method is intended to serve as a means to support the enhancement of surgical skills for endoscopic sinus surgeries (ESSs). We focus on surgical navigation systems used in image-guided ESSs and aim to construct a comparative analysis method for surgical processes based on the information about the surgical instruments motion obtained from the navigation system.

Methods

The proposed method consists of the following three parts: quantification of surgical features, modeling of surgical processes, and alignment and comparison of surgical process models (SPMs). First, we defined time-series parameters using the navigation-based surgical data. Second, we created SPMs by applying the defined parameters and the relative positional information of the instruments to the patient’s anatomy. Third, we constructed a method to align and compare SPMs based on dynamic time warping with barycenter averaging.

Results

The proposed method was validated on a dataset containing surgical data obtained by an optical tracking system from 14 clinical ESS cases. We evaluated the validity of the comparative analysis by aligning and comparing SPMs between experts and residents. The validation results suggested that the proposed method could achieve proper alignment of the SPMs and clarify the differences in surgical processes between experts and residents.

Conclusion

We developed a method to enable a time-series comparative analysis of surgical processes based on the surgical data from the navigation system. This method can allow surgeons to identify differences between their procedures and reference procedures such as experts’ procedures.

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References

  1. Eliashar R, Sichel JY, Gross M, Hocwald E, Dano I, Biron A, Ben-Yaacov A, Goldfarb A, Elidan J (2003) Image guided navigation system-a new technology for complex endoscopic endonasal surgery. Postgrad Med J 79:686–690

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Stankiewicz JA, Lal D, Connor M, Welch K (2011) Complications in endoscopic sinus surgery for chronic rhinosinusitis: a 25-year experience. Laryngoscope 121:2684–2701

    Article  PubMed  Google Scholar 

  3. Oropesa I, Sánchez-González P, Chmarra MK, Lamata P, Fernández A, Sánchez-Margallo JA, Jansen FW, Dankelman J, Sánchez-Margallo FM, Gómez EJ (2013) EVA: laparoscopic instrument tracking based on Endoscopic Video Analysis for psychomotor skills assessment. Surg Endosc 27:1029–1039

    Article  PubMed  Google Scholar 

  4. Hofstad EF, Våpenstad C, Chmarra MK, Langø T, Kuhry E, Mårvik R (2013) A study of psychomotor skills in minimally invasive surgery: what differentiates expert and nonexpert performance. Surg Endosc 27:854–863

    Article  PubMed  Google Scholar 

  5. Lalys F, Jannin P (2014) Surgical process modelling: a review. Int J Comput Assist Radiol Surg 9:495–511

    Article  PubMed  Google Scholar 

  6. Franke S, Meixensberger J, Neumuth T (2015) Multi-perspective workflow modeling for online surgical situation models. J Biomed Inform 54:158–166

    Article  PubMed  Google Scholar 

  7. Morineau T, Riffaud L, Morandi X, Villain J, Jannin P (2015) Work domain constraints for modelling surgical performance. Int J Comput Assist Radiol Surg 10:1589–1597

    Article  PubMed  Google Scholar 

  8. Holden MS, Ungi T, Sargent D, McGraw RC, Chen EC, Ganapathy S, Peters TM, Fichtinger G (2014) Feasibility of real-time workflow segmentation for tracked needle interventions. IEEE Trans Biomed Eng 61:1720–1728

    Article  PubMed  Google Scholar 

  9. Ahmidi N, Poddar P, Jones JD, Vedula SS, Ishii L, Hager GD, Ishii M (2015) Automated objective surgical skill assessment in the operating room from unstructured tool motion in septoplasty. Int J Comput Assist Radiol Surg 10:981–991

    Article  PubMed  Google Scholar 

  10. Nakamura R, Aizawa T, Muragaki Y, Maruyama T, Iseki H (2012) Automatic surgical workflow estimation method for brain tumor resection using surgical navigation information. J Robot Mechatron 24:791–801

    Article  Google Scholar 

  11. Sugino T, Kawahira H, Nakamura R (2017) Comprehensive surgical task analysis on image-guided surgery. J Med Imaging Health Inform 7:780–787

    Article  Google Scholar 

  12. Forestier G, Petitjean F, Riffaud L, Jannin P (2014) Non-linear temporal scaling of surgical processes. Artif Intell Med 62:143–152

    Article  PubMed  Google Scholar 

  13. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26:43–49

    Article  Google Scholar 

  14. Petitjean F, Ketterlin A, Gançarski P (2011) A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognit 44:678–693

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Grants-in-Aid (KAKENHI) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT; Nos. 24103704 and 15H03029), JST PRESTO (JPMJPR16D9), and the Research Grant (C) from the Tateishi Science and Technology Foundation.

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Authors and Affiliations

  1. Graduate School of Engineering, Chiba University, Chiba, Japan

    Takaaki Sugino, Ryoichi Nakamura & Masashi Yamamoto

  2. Center for Frontier Medical Engineering, Chiba University, Chiba, Japan

    Ryoichi Nakamura

  3. Faculty of Engineering, Chiba University, Chiba, Japan

    Ryoichi Nakamura & Osamu Honda

  4. PRESTO, Japan Science and Technology Agency, Saitama, Japan

    Ryoichi Nakamura

  5. Department of Otorhinolaryngology, The Jikei University School of Medicine, Tokyo, Japan

    Akihito Kuboki & Nobuyoshi Ohtori

Authors
  1. Takaaki Sugino
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  2. Ryoichi Nakamura
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  3. Akihito Kuboki
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  4. Osamu Honda
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  5. Masashi Yamamoto
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  6. Nobuyoshi Ohtori
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Corresponding author

Correspondence to Ryoichi Nakamura.

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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.

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Sugino, T., Nakamura, R., Kuboki, A. et al. Comparative analysis of surgical processes for image-guided endoscopic sinus surgery. Int J CARS 14, 93–104 (2019). https://doi.org/10.1007/s11548-018-1855-y

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  • DOI: https://doi.org/10.1007/s11548-018-1855-y

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