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Surgical task analysis of simulated laparoscopic cholecystectomy with a navigation system

  • T. Sugino
  • H. Kawahira
  • R. NakamuraEmail author
Original Article

Abstract

Purpose

   Advanced surgical procedures, which have become complex and difficult, increase the burden of surgeons. Quantitative analysis of surgical procedures can improve training, reduce variability, and enable optimization of surgical procedures. To this end, a surgical task analysis system was developed that uses only surgical navigation information.

Methods

   Division of the surgical procedure, task progress analysis, and task efficiency analysis were done. First, the procedure was divided into five stages. Second, the operating time and progress rate were recorded to document task progress during specific stages, including the dissecting task. Third, the speed of the surgical instrument motion (mean velocity and acceleration), as well as the size and overlap ratio of the approximate ellipse of the location log data distribution, was computed to estimate the task efficiency during each stage. These analysis methods were evaluated based on experimental validation with two groups of surgeons, i.e., skilled and “other” surgeons. The performance metrics and analytical parameters included incidents during the operation, the surgical environment, and the surgeon’s skills or habits.

Results

   Comparison of groups revealed that skilled surgeons tended to perform the procedure in less time and involved smaller regions; they also manipulated the surgical instruments more gently.

Conclusion

   Surgical task analysis developed for quantitative assessment of surgical procedures and surgical performance may provide practical methods and metrics for objective evaluation of surgical expertise.

Keywords

Surgical task analysis Surgical navigation system  Laparoscopic cholecystectomy Surgery evaluation 

Notes

Acknowledgments

This research was partly supported by the Fund for the Improvement of Research Environment for Young Researchers and Grants-in-Aid (KAKENHI) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT; Nos. 23680056, 22650115, and 24103704).

Conflict of interest

Takaaki Sugino, Hiroshi Kawahira, and Ryoichi Nakamura declare that they have no conflicts of interest.

References

  1. 1.
    MacKenzie CL, Ibbotson AJ, Cao CGL, Lomax A (2001) Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment. Minim Invasive Ther Allied Technol 10(3):121–128CrossRefGoogle Scholar
  2. 2.
    Fischer M, Strauss G, Burgert O, Dietz A, Trantakis C, Meixensberger J, Lemke HU (2005) ENT-surgical workflow as an instrument to assess the efficiency of technological developments in medicine. Comput Assist Radiol Surg 1281:851–855Google Scholar
  3. 3.
    Neumuth T, Durstewitz N, Fischer M, Strauss G, Dietz A, Meixensberger J, Jannin P, Cleary K, Lemke HU, Burgert O (2006) Structured recording of intraoperative surgical workflows. SPIE Med Imaging PACS Surg 6145:61450AGoogle Scholar
  4. 4.
    Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O (2009) Validation of knowledge acquisition for surgical process models. J Am Med Inform Assoc 16(1):72–82PubMedCrossRefPubMedCentralGoogle Scholar
  5. 5.
    Neumuth T, Kaschek B, Neumuth D, Ceschia M, Meixensberger J, Strauss G, Burgert O (2010) An observation support system with an adaptive ontology-driven user interface for the modeling of complex behaviors during surgical interventions. Behav Res Methods 42(4):1049–1058PubMedCrossRefGoogle Scholar
  6. 6.
    Neumuth T, Loebe F, Jannin P (2012) Similarity metrics for surgical process models. Artif Intell Med 54(1):15–27PubMedCrossRefGoogle Scholar
  7. 7.
    Forestier G, Lalys F, Riffaud L, Trelhu B, Jannin P (2012) Classification of surgical processes using dynamic time warping. J Biomed Inform 45(2):255–264PubMedCrossRefGoogle Scholar
  8. 8.
    Bouarfa L, Akman O, Schneider A, Jonker PP, Dankelman J (2012) In-vivo real-time tracking of surgical instruments in endoscopic video. Minim Invasive Ther Allied Technol 21(3):129–134PubMedCrossRefGoogle Scholar
  9. 9.
    Bouarfa L, Dankelman J (2012) Workflow mining and outlier detection from clinical activity logs. J Biomed Inform 45(6):1185–1190PubMedCrossRefGoogle Scholar
  10. 10.
    Speidel S, Sudra G, Senemaud J, Drentschew M, Müller-Stich BP, Gutt C, Dillmann R (2008) Recognition of risk situations based on endoscopic instrument tracking and knowledge based situation modeling. In: Proceedings of SPIE 6918. Medical imaging 2008: visualization, image-guided procedures, and modeling, 69180XGoogle Scholar
  11. 11.
    Lalys F, Bouget D, Riffaud L, Jannin P (2013) Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures. Int J Comput Assist Radiol Surg 8(1):39–49PubMedCrossRefGoogle Scholar
  12. 12.
    Nara A, Izumi K, Iseki H, Suzuki T, Nambu K, Sakurai Y (2011) Surgical workflow monitoring based on trajectory data mining. New Frontiers Artif Intell 6797:283–291Google Scholar
  13. 13.
    Thiemjarus S, James A, Yang GZ (2012) An eye-hand data fusion framework for pervasive sensing of surgical activities. Pattern Recognit 45(8):2855–2867CrossRefGoogle Scholar
  14. 14.
    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 Robotics Mechatron 24(5):791–801Google Scholar
  15. 15.
    Blum T, Padoy N, Feusner H, Navab N (2008) Workflow mining for visualization and analysis of surgeries. Int J Comput Assist Radiol Surg 3(5):379–386CrossRefGoogle Scholar
  16. 16.
    Padoy N, Blum T, Ahmadi SA, Feussner H (2012) Statistical modeling and recognition of surgical workflow. Med Image Anal 16(3):632–641PubMedCrossRefGoogle Scholar
  17. 17.
    Datta V, Mackay S, Mandalia M, Darzi A (2001) The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model. J Am Coll Surg 193(5):479–485PubMedCrossRefGoogle Scholar
  18. 18.
    Cao CG, MacKenzie RD, Payandeh S (1996) Task and motion analyses in endoscopic surgery. In: ASME IMECE, vol 58. pp 583–590Google Scholar
  19. 19.
    Flury B (1997) A first course in multivariate statistics. Springer, BerlinCrossRefGoogle Scholar
  20. 20.
    Maurer CR Jr, Fitzpatrick JM, Wang MY, Galloway RL Jr, Maciunas RJ, Allen GS (1997) Registration of head volume images using implantable fiducial markers. IEEE Trans Med Imaging 16(4):447–462PubMedCrossRefGoogle Scholar
  21. 21.
    Wu YV, Linehan DC (2010) Bile duct injuries in the era of laparoscopic cholecystectomies. Surg Clin North Am 90(4):787–802PubMedCrossRefGoogle Scholar
  22. 22.
    Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244CrossRefGoogle Scholar
  23. 23.
    Vassiliou MC, Feldman LS, Andrew CG, Bergman S, Leffondré K, Stanbridge D, Fried GM (2005) A global assessment tool for evaluation of intraoperative laparoscopic skills. Am J Surg 190(1):107–113PubMedCrossRefGoogle Scholar
  24. 24.
    Doyle JD, Webber EM, Sidhu RS (2007) A universal global rating scale for the evaluation of technical skills in the operating room. Am J Surg 193(5):551–555PubMedCrossRefGoogle Scholar
  25. 25.
    Tanigawa N, Lee SW, Kimura T, Mori T, Uyama I, Nomura E, Okuda J, Konishi F (2011) The Endoscopic Surgical Skill Qualification System for gastric surgery in Japan. Asian J Endosc Surg 4(3):112–115PubMedCrossRefGoogle Scholar
  26. 26.
    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(3):854–863PubMedCrossRefGoogle Scholar
  27. 27.
    Lin HC, Shafran I, Yuh D, Hager GD (2006) Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput Aided Surg 11(5):220–230PubMedCrossRefGoogle Scholar
  28. 28.
    Reiley CE, Hager GD (2009) Task versus subtask surgical skill evaluation of robotic minimally invasive surgery. Med Image Comput Comput Assist Interv Lect Notes Comput Sci 5761:435–442Google Scholar
  29. 29.
    Sato I, Nakamura R (2013) Positioning error evaluation of GPU-based 3D ultrasound surgical navigation system for moving targets by using optical tracking system. Int J Comput Assist Radiol Surg 8(3):379–393PubMedCrossRefGoogle Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  1. 1.Department of Medical System Engineering, Graduate School of EngineeringChiba UniversityChibaJapan
  2. 2.Center for Frontier Medical EngineeringChiba UniversityChibaJapan

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