Genetic Algorithm to Inverse Least Squares Comparative Dual Optimization for Ceramic Hip Arthroplasty in Medical Physics

Authors

  • Francisco Casesnoves   PhD Engineering, MSc Physics, Physician. Independent Research Scientist. International Association of Advanced Materials, Sweden. Uniscience Global Scientific Member, Wyoming, USA. Harjumaa, Estonia.

DOI:

https://doi.org//10.32628/CSEIT2176101

Keywords:

Inverse Least Squares (ILS), Genetic Algorithms (GA), Tikhonov Regularization, (TR), Software Engineering Methods, Genetic Algorithm Nonlinear Optimization, Artificial Implants (AI), CAD (Computer Aided Design), CAM (Computer Aided Manufacturing), (Hip Implants, Total Hip Arthroplasty (THA), CoC (Ceramic on Ceramic implant), Objective Function (OF), Prosthesis Materials, Wear, Biomechanical Torques/Forces.

Abstract

Ceramic THA constitutes an important group among the most frequent used implants in Biomedical Engineering and Medical Devices research field. A genetic algorithms computational nonlinear optimization is presented with two commonly ceramic materials for Ceramic-on-Ceramic (CoC) THA. This optimization is compared to a previously published Inverse Least_Squares one. Selected materials are Alumina (Al3O2), and Zirconium (ZrO2). Principal result is the numerical validation-verification of the K adimensional-constant parameter of the model with both methods. Results from previous Least-Squares algorithm and Genetic Algorithms show be closely with identical magnitude order. Numerical figures for both dual optimizations give acceptable model-parameter values with low residuals. These findings are demonstrated with series of 2D and 3D Graphical/Interior Optimization graphics also. 4D Interior Optimization method constitutes also the computational innovation of this study. The Genetic Algorithms dual-optimized ceramic-model parameters are mathematically proven/verified. Mathematical consequences are obtained for model improvements and in vitro simulation methodology. These confirmed wear parameters for in vitro determinations and efficacious Genetic Algorithms approach constitute the article novelty of both optimization methods. Results for in vitro tribotesting wear predictions with these parameters for laboratory experimental show be useful/effective. Applications for clinical Medical Physics and Bioengineering improvements in material/ceramic-THA and CAM constitute practical consequences.

References

  1. Casesnoves, F. Mathematical Standard-Parameters Dual Optimization for Metal Hip Arthroplasty Wear Modelling with Medical Physics Applications. Standards 2021, 1, 53–66. https:// doi.org/10.3390/standards1010006.
  2. Casesnoves, F. Nonlinear Inverse Dual Optimization for Hip Arthroplasty Ceramic Materials. AJMS (Asian Journal of Mathematical Sciences). Jan-Mar-2021/Vol 5/Issue 1. Pp 53-61. ISSN 2581-3463. 2021.
  3. Casesnoves, F. Multiobjective Optimization for Ceramic Hip Arthroplasty with Medical Physics Applications. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2021, 7, 582–598, ISSN: 2456-3307, https://doi.org/10.32628/CSEIT21738.
  4. Casesnoves, F. Nonlinear comparative optimization for biomaterials wear in artificial implants technology. In Proceedings of the Applied Chemistry and Materials Science RTU2018 Conference Proceedings. Latvia. October 2018.
  5. Merola, M.; Affatato, S. Materials for Hip Prostheses: A Review of Wear and Loading Considerations. Materials 2019, 12, 495, doi:10.3390/ma12030495.
  6. Navarro, N. Biomaterials in orthopaedics. J. R. Soc. Interface 2008, 5, 1137–1158, DOI:10.1098/rsif.2008.0151.2008.
  7. Bono, V, and Colls. Revision Total Hip Arthroplasty. Springer.1999.
  8. Abdel, M, Della Valle, C . Complications after Primary Total Hip Arthroplasty. Springer. 2017.
  9. Learmonth, I . Interfaces in Total Hip Arthroplasty. Springer. 2000.
  10. Kurtz, S. Advances in Zirconia Toughened Alumina Biomaterials for Total Joint Replacement. J. Mech. Behav. Biomed. Mater. 2014, 31, 107–116, doi:10.1016/j.jmbbm. 2013.03.022. 2014.
  11. Sachin, G.; Mankar, A. Biomaterials in Hip Joint Replacement. Int. J. Mater. Sci. Eng. 2016, 4, pp. 113–125, doi:10.17706/ijmse.2016.4.2.113-125.
  12. Li, Y.; Yang, C.; Zhao, H.; Qu, S.; Li, X.; Li, Y. New Developments of Ti-Based Alloys for Biomedical Applications. Materials 2014, 7, 1709–1800, doi:10.3390/ma7031709.
  13. Kolli, R.; Devaraj, A. A Review of Metastable Beta Titanium Alloys. Metals 2018, 8, 506, DOI:10.3390/met8070506.
  14. Holzwarth, U.; Cotogno, G. Total Hip Arthroplasty. JRC Scientific and Policy Reports; European Commission: Brussels, ‎Belgium, 2012.
  15. Delimar, D. Femoral head wear and metallosis caused by damaged titanium porous coating after primary metal-on-polyethylene total hip arthroplasty: A case report. Croat. Med. J. 2018, 59, 253–257, DOI:10.3325/cmj.2018.59.253.
  16. Zhang, M.; Fan, Y. Computational Biomechanics of the Musculoskeletal System; CRC Press: Boca Raton, FL, USA, 2015.
  17. Dreinhöfer, K.; Dieppe, P.; Günther, K.; Puhl, W. Eurohip. Health Technology Assessment of Hip Arthroplasty in Europe; Springer: Berlin/Heidelberg, Germany, 2009.
  18. Casesnoves, F. 2D computational-numerical hardness comparison between Fe-based hardfaces with WC-Co reinforcements for Integral-Differential modelling. Trans. Tech. 2018, 762, 330–338, DOI:10.4028/www.scientific.net/KEM.762.330. ISSN: 1662-9795.
  19. Hutchings, I.; Shipway, P. Tribology Friction and Wear of Engineering Materials, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 2017.
  20. Shen, X.; Lei, C.; Li, R. Numerical Simulation of Sliding Wear Based on Archard Model. In Proceedings of the 2010 International Conference on Mechanic Automation and Control Engineering, Wuhan, China, 26–28 June 2010. DOI:10.1109/MACE.2010.5535855.
  21. Affatato, S.; Brando, D. Introduction to Wear Phenomena of Orthopaedic Implants; Woodhead Publishing: Sawston, UK, 2012.
  22. Matsoukas, G.; Kim, Y. Design Optimization of a Total Hip Prosthesis for Wear Reduction. J. Biomech. Eng. 2009, 131, 051003.
  23. Casesnoves, F.; Antonov, M.; Kulu, P. Mathematical models for erosion and corrosion in power plants. A review of applicable modelling optimization techniques. In Proceedings of RUTCON2016 Power Engineering Conference, Riga, Latvia, 13th October. 2016.
  24. Galante, J.; Rostoker, W. Wear in Total Hip Prostheses. Acta Orthop. Scand. 2014, 43, 1–46, DOI:10.3109/ort.1972.43.suppl-145.01.
  25. Mattei, L.; DiPuccio, F.; Piccigallo, B.; Ciulli, E. Lubrication and wear modelling of artificial hip joints: A review. Tribol. Int. 2011, 44, 532–549.
  26. Jennings, L. Enhancing the safety and reliability of joint replacement implants. Orthop. Trauma 2012, 26, 246–252.
  27. Casesnoves, F. Die Numerische Reuleaux-Methode Rechnerische und Dynamische Grundlagen mit Anwendungen (Erster Teil); Sciencia Scripts: 2019; ISBN-13: 978-620-0-89560-8, ISBN-10: 6200895600.
  28. Casesnoves, F. Mathematical Models and Optimization of Erosion and Corrosion. Ph.D. Thesis, Taltech University, Tallinn, Estonia. 14 December. 2018. ISSN 25856898.
  29. Saifuddin, A.; Blease, S.; Macsweeney, E. Axial loaded MRI of the lumbar spine. Clin. Radiol. 2003, 58, 661–671.
  30. Damm, P. Loading of Total Hip Joint Replacements. Ph.D. Thesis, Technischen Universität, Berlin, Germany, 2014.
  31. Casesnoves, F. The Numerical Reuleaux Method, a Computational and Dynamical Base with Applications. First Part; Lambert Academic Publishing: 2019; Republic of Moldava. ISBN-10 3659917478.
  32. Casesnoves, F. Large-Scale Matlab Optimization Toolbox (MOT) Computing Methods in Radiotherapy Inverse Treatment Planning. High Performance Computing Meeting; Nottingham University: Nottingham, UK, 2007.
  33. Casesnoves, F. A Monte-Carlo Optimization method for the movement analysis of pseudo-rigid bodies. In Proceedings of the 10th SIAM Conference in Geometric Design and Computing, San Antonio, TX, USA, 4–8 November 2007; Contributed Talk.
  34. Casesnoves, F. 1.-‘Theory and Primary Computational Simulations of the Numerical Reuleaux Method (NRM)’, Casesnoves, Francisco. Int. J. Math. Computation. 2011, 13, pp. 89-111. Available online: http://www.ceser.in/ceserp/index.php/ijmc/issue/view/119 (accessed on 28 June 2021).
  35. Casesnoves, F. Applied Inverse Methods for Optimal Geometrical-Mechanical Deformation of Lumbar artificial Disks/Implants with Numerical Reuleaux Method. 2D Comparative Simulations and Formulation. Comput. Sci. Appl. 2015, 2, 1–10. Available online: www.ethanpublishing.com (accessed on 28 June 2021).
  36. Casesnoves, F. Inverse methods and Integral-Differential model demonstration for optimal mechanical operation of power plants–numerical graphical optimization for second generation of tribology models. Electr. Control Commun. Eng. 2018, 14, 39–50, DOI:10.2478/ecce-2018-0005.
  37. Casesnoves, F.; Surzhenkov, A. Inverse methods for computational simulations and optimization of erosion models in power plants. In Proceedings of the IEEE Proceedings of RUTCON2017 Power Engineering Conference, Riga, Latvia, 5 December 2017. doi:10.1109/RTUCON.2017.8125630. Electronic ISBN:978-1-5386-3846-0. USB ISBN: 978-1-5386-3844-6. ISBN: 978-1-5386-3847-7.
  38. Abramobitz, S. Handbook of Mathematical Functions. Appl. Math. Ser. 55. 1972.
  39. Luenberger, G.D. Linear and Nonlinear Programming, 4th ed.; Springer: Berlin/Heidelberg, Germany, 2008.
  40. Casesnoves, F. Exact Integral Equation Determination with 3D Wedge Filter Convolution Factor Solution in Radiotherapy. Series of Computational-Programming 2D-3D Dosimetry Simulations. Int. J. Sci. Res. Sci. Eng. Technol, pp. 699-715. 2016, 2.
  41. Panjabi, M.; White, A. Clinical Biomechanics of the Spine. Lippincott 1980, 42, S3.
  42. Casesnoves, F. Software Programming with Lumbar Spine Cadaveric Specimens for Computational Biomedical Applications. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. 2021, 7, 7–13, ISSN: 2456-3307.
  43. Surzhenkov, A.; Viljus, M.; Simson, T.; Tarbe, R.; Saarna, M.; Casesnoves, F. Wear resistance and mechanisms of composite hardfacings at abrasive impact erosion wear. J. Phys. 2017, 843, 012060, doi:10.1088/1742-6596/843/1/012060.
  44. Casesnoves, F. Computational Simulations of Vertebral Body for Optimal Instrumentation Design. ASME J. Med. Devices 2012, 6, 021014, http://dx.doi.org/10.1115/1.4006670.
  45. Barker, P. The effect of applying tension to the lumbar fasciae on segmental flexion and extension. In Proceedings of 5th International Congress of Low Back and Pelvic Pain, Melbourne, Australia, 10–13 November 2014; pp. 50–52.
  46. Galme, S.; Barker, P.; Bhalerao, Y. Biomaterials in Hip Joint Replacement. Int. J. Mater. Sci. Eng. 2016, 4, 113–125.
  47. European Textbook on Ethics in Research. European Commission, Directorate-General for Research. Unit L3. Governance and Ethics. European Research Area. Science and Society. EUR 24452 EN. Available online: https://op.europa.eu/en/publication-detail/-/publication/12567a07-6beb-4998-95cd-8bca103fcf43. (accessed on 28 June 2021).
  48. ALLEA. The European Code of Conduct for Research Integrity, Revised ed.; ALLEA: Berlin Barndenburg Academy of Sciences. 2017.
  49. Haupt, R, Haupt, S. Practical Genetic Algorithms. Wiley. Second Edition.  2004.
  50. Kazufumi, I; Bangti, J. Inverse Problems, Tikhonov Theory and Algorithms. Series on Applied Mathematics Volume 22. World Scientific. 2015.
  51. Darwin, C. The origin of species. Barnes & Noble Classics. 2004.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-01-30

Issue

Volume 8, Issue 1, January-February-2022

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Research Articles

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How to Cite

[1]
Francisco Casesnoves , " Genetic Algorithm to Inverse Least Squares Comparative Dual Optimization for Ceramic Hip Arthroplasty in Medical Physics , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 1, pp.88-107, January-February-2022. Available at doi : https://doi.org/10.32628/CSEIT2176101