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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Darzi, A., Mackay, S.: Assessment of surgical competence. Qual. Health Care 10(suppl 2), ii64–ii69 (2001)
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)
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)
Kuipers, J.: Object tracking and determining orientation of object using coordinate transformation means, system and process (1975)
Hogan, N., Sternad, D.: Sensitivity of smoothness measures to movement duration, amplitude, and arrests. J. Mot. Behav. 41(6), 529–534 (2009)
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)
Rohrer, B., Hogan, N.: Avoiding spurious submovement decompositions: a scattershot algorithm. Biol. Cybern. 94(5), 409–414 (2006)
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)
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)
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)
Kohonen, T.: Learning vector quantization. Self-Organizing Maps. Springer Series in Information Sciences, vol. 30, pp. 175–189. Springer, Berlin (1995)
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)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kramer, B.D., Losey, D.P., O’Malley, M.K. (2016). SOM and LVQ Classification of Endovascular Surgeons Using Motion-Based Metrics. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-28518-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28517-7
Online ISBN: 978-3-319-28518-4
eBook Packages: EngineeringEngineering (R0)