Advertisement

A Time Saver: Optimization Approach for the Fully Automatic 3D Planning of Forearm Osteotomies

  • Fabio CarrilloEmail author
  • Lazaros Vlachopoulos
  • Andreas Schweizer
  • Ladislav Nagy
  • Jess Snedeker
  • Philipp Fürnstahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Three-dimensional (3D) computer-assisted preoperative planning has become the state-of-the-art for surgical treatment of complex forearm bone malunions. Despite benefits of these approaches, surgeon time and effort to generate a 3D-preoperative planning remains too high, and limits their clinical application. This motivates the development of computer algorithms able to expedite the process. We propose a staged multi-objective optimization method based on a genetic algorithm with tailored fitness functions, capable to generate a 3D-preoperative plan in a fully automatic fashion. A clinical validation was performed upon 14 cases of distal radius osteotomy. Solutions generated by our algorithm (OA) were compared to those created by surgeons using dedicated planning software (Gold Standard; GS), demonstrating that in 53% of the tested cases, OA solutions were better than or equal to GS solutions, successfully reducing surgeon’s interaction time. Additionally, a quantitative evaluation based on 4 different error measurement confirmed the validity of our method.

Keywords

3D surgical planning Computer-assisted Radius Osteotomy 

Notes

Acknowledgments

This work has been funded through a Promedica foundation grant N° GHDE KQX7-DZZ.

References

  1. 1.
    Nagy, L., Jankauskas, L., Dumont, C.E.: Correction of forearm malunion guided by the preoperative complaint. Clin. Orthop. Relat. Res. 466(6), 1419–1428 (2008)CrossRefGoogle Scholar
  2. 2.
    Schweizer, A., et al.: Complex radius shaft malunion: osteotomy with computer-assisted planning. Hand 5(2), 171–178 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Vlachopoulos, L., et al.: Three-dimensional postoperative accuracy of extra-articular forearm osteotomies using CT-scan based patient-specific surgical guides. BMC Musculoskelet. Disord. 16(1), 1 (2015)CrossRefGoogle Scholar
  4. 4.
    Schweizer, A., Fürnstahl, P., Nagy, L.: Three-dimensional correction of distal radius intra-articular malunions using patient-specific drill guides. J. Hand Surg. 38(12), 2339–2347 (2013)CrossRefGoogle Scholar
  5. 5.
    Miyake, J., et al.: Three-dimensional corrective osteotomy for malunited diaphyseal forearm fractures using custom-made surgical guides based on computer simulation. JBJS Essent. Surg. Tech. 2(4), e24 (2012)CrossRefGoogle Scholar
  6. 6.
    Murase, T., et al.: Three-dimensional corrective osteotomy of malunited fractures of the upper extremity with use of a computer simulation system. J. Bone Joint Surg. 90(11), 2375–2389 (2008)CrossRefGoogle Scholar
  7. 7.
    Fürnstahl, P., et al.: Surgical treatment of long-bone deformities: 3D preoperative planning and patient-specific instrumentation. In: Zheng, G., Li, S. (eds.) Computational Radiology for Orthopaedic Interventions. LNCVB, vol. 23, pp. 123–149. Springer, Cham (2016). doi: 10.1007/978-3-319-23482-3_7CrossRefGoogle Scholar
  8. 8.
    Athwal, G.S., et al.: Computer-assisted distal radius osteotomy 1. J. Hand Surg. 28(6), 951–958 (2003)CrossRefGoogle Scholar
  9. 9.
    Schkommodau, E., et al.: Computer-assisted optimization of correction osteotomies on lower extremities. Comput. Aided Surg. 10(5–6), 345–350 (2005)CrossRefGoogle Scholar
  10. 10.
    Belei, P., et al.: Computer-assisted single-or double-cut oblique osteotomies for the correction of lower limb deformities. Proc. Inst. Mech. Eng. Part H: J. Eng. Med. 221(7), 787–800 (2007)CrossRefGoogle Scholar
  11. 11.
    Vatti, B.R.: A generic solution to polygon clipping. Commun. ACM 35(7), 56–63 (1992)CrossRefGoogle Scholar
  12. 12.
    Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  13. 13.
    Miettinen, K.: Nonlinear Multiobjective Optimization. Springer, New York (1999)zbMATHGoogle Scholar
  14. 14.
    Friedrich, T., Kroeger, T., Neumann, F.: Weighted preferences in evolutionary multi-objective optimization. In: Wang, D., Reynolds, M. (eds.) AI 2011. LNCS, vol. 7106, pp. 291–300. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25832-9_30CrossRefGoogle Scholar
  15. 15.
    Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space. Philos. Mag. Ser. 6 2(11), 559–572 (1901)CrossRefGoogle Scholar
  16. 16.
    Jones, M.W., Baerentzen, J.A., Sramek, M.: 3D distance fields: a survey of techniques and applications. IEEE Trans. Vis. Comput. Graph. 12(4), 581–599 (2006)CrossRefGoogle Scholar
  17. 17.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891–923 (1998). doi: 10.1145/293347.293348MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Fabio Carrillo
    • 1
    • 2
    Email author
  • Lazaros Vlachopoulos
    • 2
    • 3
  • Andreas Schweizer
    • 2
    • 3
  • Ladislav Nagy
    • 2
    • 3
  • Jess Snedeker
    • 1
  • Philipp Fürnstahl
    • 2
  1. 1.Laboratory for Orthopaedic BiomechanicsETH ZurichZurichSwitzerland
  2. 2.Computer Assisted Research and Development GroupZurichSwitzerland
  3. 3.Orthopedic Department, Balgrist University HospitalUniversity of ZurichZurichSwitzerland

Personalised recommendations