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Probabilistic risk assessment and service life performance management of load bearing biomedical implants

Author

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  • Stewart, Mark G.
  • O’Connor, Alan

Abstract

It is important to consider the performance of load bearing biomedical implants as a stochastic problem. This provides scope to optimise their whole life performance in terms of design and lifetime performance management measures with the aim of minimisation of the need for replacement, or the number of replacements, during the expected life of the patient. An important parallel is developed with the field of structural reliability analysis (i.e., probabilistic assessment) which has developed in recent years with great success in optimisation of whole life performance of load bearing infrastructure systems. This paper demonstrates how this same methodology can be employed in the field of biomedical engineering to optimise the design and whole life performance of implants considering factors such as (i) deterioration with age, and (ii) stochastic variation in load. The paper also demonstrates the importance of Bayesian updating and correlation modelling in considering the design and whole life performance optimisation of biomedical implants.

Suggested Citation

  • Stewart, Mark G. & O’Connor, Alan, 2012. "Probabilistic risk assessment and service life performance management of load bearing biomedical implants," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 49-55.
    • Handle: RePEc:eee:reensy:v:108:y:2012:i:c:p:49-55
    DOI: 10.1016/j.ress.2012.06.012
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    Cited by:

    1. Haddad, Tarek & Himes, Adam & Campbell, Michael, 2014. "Fracture prediction of cardiac lead medical devices using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 145-157.
    2. Marhavilas, P.K. & Koulouriotis, D.E. & Spartalis, S.H., 2013. "Harmonic analysis of occupational-accident time-series as a part of the quantified risk evaluation in worksites: Application on electric power industry and construction sector," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 8-25.
    3. Panagiotis K. Marhavilas & Michael G. Tegas & Georgios K. Koulinas & Dimitrios E. Koulouriotis, 2020. "A Joint Stochastic/Deterministic Process with Multi-Objective Decision Making Risk-Assessment Framework for Sustainable Constructions Engineering Projects—A Case Study," Sustainability, MDPI, vol. 12(10), pages 1-21, May.
    4. Leiva, Víctor & Ruggeri, Fabrizio & Saulo, Helton & Vivanco, Juan F., 2017. "A methodology based on the Birnbaum–Saunders distribution for reliability analysis applied to nano-materials," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 192-201.

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