BEYOND 'HARD BONE': QUANTITATIVE METHODOLOGIES FOR OBJECTIVE BONE MODEL SELECTION IN ORTHOPEDIC DEVICE VERIFICATION TESTING
DOI:
https://doi.org/10.5281/zenodo.20960966Keywords:
Impact Microindentation, Bone Surrogate Materials, Orthopedic Device Testing, Bone Material Strength Index, Verification and Validation, Sclerotic Bone, Robotic Spine SurgeryAbstract
Orthopedic device verification testing assumes that the mechanical properties of the tissues used in bench testing are representative of the in vivo mechanical loading the device will encounter during use. Descriptors of bone quality (soft, normal, hard) to be used in testing have been employed for more than 30 years without a standardized quantitative description and with a wide variation between laboratories and manufacturers. This article presents the scientific basis for this gap and a quantitative protocol for choosing an appropriate bone model according to IMI. A Medtronic/Active Life Scientific and University of Washington research collaboration develops a cross-model hardness profile referenced to clinically established sclerotic bone using the OsteoProbe device that measures resistance to penetration quantified as the Bone Material Strength index (BMSi) using a cross-model matrix of synthetic polyurethane foams, animal bone and fresh cadaveric human tissue. Results showed that commercially relevant surrogates labeled 'hard' span a large, inconsistent region of BMSi space and that worst-case model selection was quantitative, not based on inference from surrogate labels. Given the context of regulatory verification and validation used to characterize safety-critical orthopedic devices, this approach could be adopted as an industry standard. Adoption of this approach would materially improve the scientific rigor and clinical relevance of bench test evidence submitted in support of device market authorizations?
References
Manuela Schoeb et al., "Added value of impact microindentation in the evaluation of bone fragility: a systematic review of the literature," Frontiers in Endocrinology, vol. 11, pp. 1–15, 2020. [Online]. Available: https://doi.org/10.3389/fendo.2020.00015
Lucas R. Budd et al., "Objective selection of bone mimetic materials using impact microindentation," PubMed Central, 2024. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12720120/
E. Dall Ara et al., "A nonlinear QCT-based finite element model validation study for the human femur tested in two configurations in vitro," Bone, vol. 52, no. 1, pp. 27–38, 2013. [Online]. Available: https://doi.org/10. 1016/j.bone.2012.09.006
S. Shim et al., "Compact bone surgery robot with a high-resolution and high-rigidity remote center of motion mechanism," IEEE Transactions on Biomedical Engineering, vol. 67, no. 9, pp. 2497–2506, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/8951212
Manuela Schoeb et al., "Safety outcomes of impact microindentation: a prospective observational study in the Netherlands," Journal of Bone and Mineral Research Plus, vol. 7, no. 10, 2023. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/37808395/
Pamela Rufus-Membere et al., "Reference intervals for bone impact microindentation in healthy adults: a multi-centre international study," Journal of Bone and Mineral Research, vol. 38, no. 3, pp. 365–375, 2023. [Online]. Available: https://link.springer.com/article/10.1007/s00223-022-01047-y
Egon Perilli et al., "Failure strength of human vertebrae: prediction using bone mineral density measured by DXA and bone volume by micro-CT," Bone, vol. 51, no. 6, pp. 1170–1176, 2012. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S875632821200720X
https://pmc.ncbi.nlm.nih.gov/articles/PMC5152622/p et al., "Assessing underlying bone quality in spine surgery patients: a narrative review of dual-energy X-ray absorptiometry (DXA) and alternatives," The Spine Journal, vol. 20, no. 12, pp. 1927–1936, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/abs/ pii/S1529943020310780
A Diez-Perez et al., "Technical note: recommendations for a standard procedure to assess cortical bone at the tissue level in vivo using impact microindentation," Bone Reports, vol. 5, pp. 287–290, 2016. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC5152622/
Krzysztof Zerdzicki et al., "Compressive and tensile properties of polyurethane foam mimicking trabecular tissue in artificial femoral head bones," Frontiers in Bioengineering and Biotechnology, vol. 13, 2025. [Online]. Available: https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/ fbioe.2024.1482165/full?utm_source=facebook&utm_medium=cpc&utm_campaign=imp_jnlprom_04-26_fbioe_en_nat_fos-inst__reg13&utm_content=empty&utm_id=120243624876350493_v2_s06_
e7606&utm_term=120243624876350493&fbclid=IwY2xjawRfoKlleHRuA2FlbQEw
AGFkaWQBqzD4LlFD3WJyaWQRMWlSSG5qMDlKbmYzTGxFaXRzcnRjBmFwcF9p
ZBAyMjIwMzkxNzg4MjAwODkyAAEeIUNJrVXXXkRdGzBHQqxUxHuEZxS_cTxwp8
tCG9c8uqreuWHde4OzLpcRubY_aem_STspA3BpZ6cSZoPsQaXQrQ
James Fletcher et al., "Juvenile bovine bone is an appropriate surrogate for normal and reduced density human bone in biomechanical testing: a validation study," Scientific Reports, vol. 8, no. 1, p. 10181, 2018. [Online]. Available: https://www.researchgate.net/publication/326200599_Juvenile_bovine_bone_
is_an_appropriate_surrogate_for_normal_and_reduced_density_human_bone
_in_biomechanical_testing_A_validation_study
ASTM International, "F1839-08: Standard specification for rigid polyurethane foam for use as a standard material for testing orthopaedic devices and instruments," ASTM International, West Conshohocken, PA, 2021. [Online]. Available: https://store.astm.org/f1839-08r21.html
Guangming Xia et al., "Pedicle drilling force control of a robotic surgical system via spine-soft tissue coupling model and parameters optimization," Computers in Biology and Medicine, vol. 167, p. 107641, 2023. [Online]. Available: https://doi.org/10.1016/j.compbiomed.2023.107641
Y. Suthari et al., “Cost optimization techniques for efficient resource allocation in cloud computing environments,” in Proceedings of the 2025 3rd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), pp. 793–797, 2025. [Online]. Available: https://doi.org/10.1109/IcoICI6521 7.2025.11253514.
N. Nellutla et al., “Cloud-aware ML engineering: Enabling real-time decision systems via federated DataOps,” in Proceedings of the 2025 5th Asian Conference on Innovation in Technology (ASIANCON), pp. 1–9, Aug. 2025. [Online]. Available: https://doi.org/10.1109/ASIANCON66527.2025.11281066.
R. Gollapudi, “Operational drift and risk-bounded decision-making in production database systems,” Journal of International Crisis and Risk Communication Research, pp. 132–147, 2023. [Online]. Available: https://doi.org/10.63278/jicrcr.vi.3762.
M. K. Babu and Y. Suthari, “Secure and intelligent PLC systems: Integrating artificial intelligent for enhanced industrial control and data privacy,” Computer Fraud & Security, Special Issue, 2024. [Online]. Available: https://doi.org/10.52710/cfs.627.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties and that the Article has not been published elsewhere. Author(s) agree to the terms that the IPHO Journal will have the full right to remove the published article on any misconduct found in the published article.
