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The minimally acceptable classification criterion for surgical skill: intent vectors and separability of raw motion data

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

Abstract

Purpose

Minimally invasive surgery requires objective methods for skill evaluation and training. This work presents the minimally acceptable classification (MAC) criterion for computational surgery: Given an obvious novice and an obvious expert, a surgical skill evaluation classifier must yield 100% accuracy. We propose that a rigorous motion analysis algorithm must meet this minimal benchmark in order to justify its cost and use.

Methods

We use this benchmark to investigate two concepts: First, how separable is raw, multidimensional dry laboratory laparoscopic motion data between obvious novices and obvious experts? We utilized information theoretic techniques to analytically address this. Second, we examined the use of intent vectors to classify surgical skill using three FLS tasks.

Results

We found that raw motion data alone are not sufficient to classify skill level; however, the intent vector approach is successful in classifying surgical skill level for certain tasks according to the MAC criterion. For a pattern cutting task, this approach yields 100% accuracy in leave-one-user-out cross-validation.

Conclusion

Compared to prior art, the intent vector approach provides a generalized method to assess laparoscopic surgical skill using basic motion segments and passes the MAC criterion for some but not all FLS tasks.

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References

  1. Ahmidi N, Gao Y, Béjar B, Vedula SS, Khudanpur S, Vidal R, Hager GD (2013) String motif-based description of tool motion for detecting skill and gestures in robotic surgery. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 26–33

  2. 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(6):981–991

    Article  PubMed  Google Scholar 

  3. Birkmeyer JD, Finks JF, O’Reilly A, Oerline M, Carlin AM, Nunn AR, Dimick J, Banerjee M, Birkmeyer NJ (2013) Surgical skill and complication rates after bariatric surgery. N Engl J Med 369(15):1434–1442

    Article  CAS  PubMed  Google Scholar 

  4. Chen C, White L, Kowalewski T, Aggarwal R, Lintott C, Comstock B, Kuksenok K, Aragon C, Holst D, Lendvay T (2014) Crowd sourced assessment of technical skills: a novel method to evaluate surgical performance. J Surg Res 187(1):65–71

    Article  PubMed  Google Scholar 

  5. Chmarra MK, Klein S, de Winter JC, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24(5):1031–1039

    Article  PubMed  Google Scholar 

  6. 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):479–485

    Article  CAS  PubMed  Google Scholar 

  7. Faulkner H, Regehr G, Martin J, Reznick R (1996) Validation of an objective structured assessment of technical skill for surgical residents. Acad Med 71(12):1363–1365

    Article  CAS  PubMed  Google Scholar 

  8. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188

    Article  Google Scholar 

  9. Gomez ED, Aggarwal R, McMahan W, Bark K, Kuchenbecker KJ (2016) Objective assessment of robotic surgical skill using instrument contact vibrations. Surg Endosc 30(4):1419–1431

    Article  PubMed  Google Scholar 

  10. Iba W, Langley P (1992) Induction of one-level decision trees. In: Proceedings of the ninth international conference on machine learning, pp 233–240

  11. Jog A, Itkowitz B, Liu M, DiMaio S, Hager G, Curet M, Kumar R (2011) Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation. In: 2011 IEEE international conference on robotics and automation (ICRA). IEEE, pp 5273–5278

  12. Kononenko I, Šimec E, Robnik-Šikonja M (1997) Overcoming the myopia of inductive learning algorithms with RELIEFF. Appl Intell 7(1):39–55

    Article  Google Scholar 

  13. Kowalewski TM, White LW, Lendvay TS, Jiang IS, Sweet R, Wright A, Hannaford B, Sinanan MN (2014) Beyond task time: automated measurement augments fundamentals of laparoscopic skills methodology. J Surg Res 192(2):329–338

    Article  PubMed  Google Scholar 

  14. Kowalewski TM, Sweet R, Lendvay TS, Menhadji A, Averch T, Box G, Brand T, Ferrandino M, Kaouk J, Knudsen B, Landman J, Leek B, Schwartz BF, McDougall E (2016) Validation of the AUA BLUS tasks. J Urol 195(4):998–1005

    Article  PubMed  Google Scholar 

  15. 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):220–230

    Article  PubMed  Google Scholar 

  16. Malpani A, Vedula SS, Chen CCG, Hager GD (2014) Pairwise comparison-based objective score for automated skill assessment of segments in a surgical task. In: International conference on information processing in computer-assisted interventions. Springer, pp 138–147

  17. Reiley CE, Hager GD (2009) Task versus subtask surgical skill evaluation of robotic minimally invasive surgery. In: Medical image computing and computer-assisted intervention—MICCAI 2009. Springer, pp 435–442

  18. Sroka G, Feldman LS, Vassiliou MC, Kaneva PA, Fayez R, Fried GM (2010) Fundamentals of laparoscopic surgery simulator training to proficiency improves laparoscopic performance in the operating room a randomized controlled trial. Am J Surg 199(1):115–120

    Article  PubMed  Google Scholar 

  19. Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (2012) Sparse hidden markov models for surgical gesture classification and skill evaluation. In: International conference on information processing in computer-assisted interventions. Springer, pp 167–177

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Acknowledgements

R. Dockter was supported by the University of Minnesota Interdisciplinary Doctoral and Informatics Institute (UMII) MnDRIVE fellowships.

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Correspondence to Rodney L. Dockter.

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The authors declare that they have no conflict of interest.

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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.

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

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Dockter, R.L., Lendvay, T.S., Sweet, R.M. et al. The minimally acceptable classification criterion for surgical skill: intent vectors and separability of raw motion data. Int J CARS 12, 1151–1159 (2017). https://doi.org/10.1007/s11548-017-1610-9

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  • DOI: https://doi.org/10.1007/s11548-017-1610-9

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