SOM and LVQ Classification of Endovascular Surgeons Using Motion-Based Metrics

  • Benjamin D. Kramer
  • Dylan P. Losey
  • Marcia K. O’Malley
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 428)

Abstract

An increase in the prevalence of endovascular surgery requires a growing number of proficient surgeons. Current endovascular surgeon evaluation techniques are subjective and time-consuming; as a result, there is a demand for an objective and automated evaluation procedure. Leveraging reliable movement metrics and tool-tip data acquisition, we here use neural network techniques such as LVQs and SOMs to identify the mapping between surgeons’ motion data and imposed rating scales. Using LVQs, only 50 % testing accuracy was achieved. SOM visualization of this inadequate generalization, however, highlights limitations of the present rating scale and sheds light upon the differences between traditional skill groupings and neural network clusters. In particular, our SOM clustering both exhibits more truthful segmentation and demonstrates which metrics are most indicative of surgeon ability, providing an outline for more rigorous evaluation strategies.

Keywords

SOM LVQ Skill assessment Surgical training 

References

  1. 1.
    Schanzer, A., Steppacher, R., Eslami, M., Arous, E., Messina, L., Belkin, M.: Vascular surgery training trends from 2001–2007: a substantial increase in total procedure volume is driven by escalating endovascular procedure volume and stable open procedure. J. Vasc. Surg. 49(5), 1344–1399 (2009)CrossRefGoogle Scholar
  2. 2.
    Cox, M., Irby, D.M., Reznick, R.K., MacRae, H.: Teaching surgical skills–changes in the wind. N. Engl. J. Med. 355(25), 2664–2669 (2006)CrossRefGoogle Scholar
  3. 3.
    Reiley, C.E., Lin, H.C., Yuh, D.D., Hager, G.D.: Review of methods for objective surgical skill evaluation. Surg. Endosc. 25(2), 356–366 (2011)CrossRefGoogle Scholar
  4. 4.
    Darzi, A., Mackay, S.: Assessment of surgical competence. Qual. Health Care 10(suppl 2), ii64–ii69 (2001)Google Scholar
  5. 5.
    Cotin, S., Stylopoulos, N., Ottensmeyer, M., Neumann, P., Rattner, D., Dawson, S.: Metrics for laparoscopic skills trainers: the weakest link! In: Medical Image Computing and Computer-Assisted Intervention, pp. 35–43. Springer, Berlin (2002)Google Scholar
  6. 6.
    van Hove, P.D., Tuijthof, G.J.M., Verdaasdonk, E.G.G., Stassen, L.P.S., Dankelman, J.: Objective assessment of technical surgical skills. Br. J. Surg. 97(7), 972–987 (2010)CrossRefGoogle Scholar
  7. 7.
    Kuipers, J.: Object tracking and determining orientation of object using coordinate transformation means, system and process (1975)Google Scholar
  8. 8.
    Hogan, N., Sternad, D.: Sensitivity of smoothness measures to movement duration, amplitude, and arrests. J. Mot. Behav. 41(6), 529–534 (2009)CrossRefGoogle Scholar
  9. 9.
    Balasubramanian, S., Melendez-Calderon, A., Burdet, E.: A robust and sensitive metric for quantifying movement smoothness. IEEE Trans. Biomed. Eng. 59(8), 2126–2136 (2012)CrossRefGoogle Scholar
  10. 10.
    Rohrer, B., Hogan, N.: Avoiding spurious submovement decompositions: a scattershot algorithm. Biol. Cybern. 94(5), 409–414 (2006)MATHCrossRefGoogle Scholar
  11. 11.
    Rafii-Tari, H., Payne, C.J., Liu, J., Riga, C., Bicknell, C., Yang, G.Z.: Towards automated surgical skill evaluation of endovascular catheterization tasks based on force and motion signatures. In: Proceeding of IEEE International Conference on Robotics and Automation, pp. 1789–1794 (2015)Google Scholar
  12. 12.
    Lin, H.C., Shafran, I., Yuh, D., Hager, G.D.: Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput. Aided Surg. 11(5), 220–230 (2006)CrossRefGoogle Scholar
  13. 13.
    Estrada, S., O’Malley, M.K., Duran, C., Schulz, D., Bismuth, J.: On the development of objective metrics for surgical skills evaluation based on tool motion. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3144–3149. IEEE (2014)Google Scholar
  14. 14.
    Kohonen, T.: Learning vector quantization. Self-Organizing Maps. Springer Series in Information Sciences, vol. 30, pp. 175–189. Springer, Berlin (1995)Google Scholar
  15. 15.
    Bismuth, J., Donovan, M.A., O’Malley, M.K., El Sayed, H.F., Naoum, J.J., Peden, E.K., Davies, M.G., Lumsden, A.B.: Incorporating simulation in vascular surgery education. J. Vasc. Surg. 52(4), 1072–1080 (2010)Google Scholar
  16. 16.
    Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)MATHCrossRefGoogle Scholar
  17. 17.
    Merényi, E., Tasdemir, K., Zhang, L.: Learning highly structured manifolds: harnessing the power of SOMs. In: Similarity-based clustering. Lecture Notes in Computer Science, pp. 138–168. Springer, Berlin (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Benjamin D. Kramer
    • 1
  • Dylan P. Losey
    • 1
  • Marcia K. O’Malley
    • 1
  1. 1.Rice UniversityHoustonUSA

Personalised recommendations