Computational efficient method for assessing the influence of surgical variability on primary stability of a contemporary femoral stem in a cohort of subjects

Abstract

Finite element (FE) modelling can provide detailed information on implant stability; however, its computational cost limits the possibility of completing large numerical analyses into the effect of surgical variability in a cohort of patients. The aim of this study was to develop an efficient surrogate model for a cohort of patients implanted using a common cementless hip stem. FE models of implanted femora were generated from computed tomography images for 20 femora (11 males, 9 females; 50–80 years; 52–116 kg). An automated pipeline generated FE models for 61 different unique scenarios that span the femur-specific range of implant positions. Peak hip contact and muscle forces for stair climbing were scaled to the donors’ body weight and applied to the models. A cohort-specific surrogate for implant micromotion was constructed from Gaussian process models trained using data from FE simulations representing the median and extreme implant positions for each femur. A convergence study confirmed suitability of the sampling method for cohorts with 10+ femora. The final model was trained using data from the 20 femora. Results showed very good agreement between the FE and the surrogate predictions for a total of 1036 alignment scenarios [root mean squared error (RMSE) < 20 µm; \(R_{\text{validation}}^{2}\) = 0.81]. The total time required for the surrogate model to predict the micromotion range associated with surgical variability was approximately one-eighth of the corresponding full FE analysis. This confirms that the developed model is an accurate yet computationally cheaper alternative to full FE analysis when studying the implant robustness in a cohort of 10+ femora.

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Acknowledgements

The authors are grateful to the staff of the Mortuary and the Donor Tissue Bank at the Victorian Institute of Forensic Medicine Australia for their assistance in collecting the material upon which this study is based. The authors are also grateful to the families of the donors who gave permission or the collection of the material expressly for research. The Australian Research Council (DP180103146, FT180100338) is also gratefully acknowledged.

Funding

This study was part of a project funded by the Australian Research Council (DP180103146, FT180100338), with partial funding from DePuy Synthes.

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Correspondence to Rami M. A. Al-Dirini.

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Prof. Taylor and Dr. Martelli are chief investigators named on the ARC Grants. Dr. Al-Dirini was employed in these projects.

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Appendices

Appendix 1

See Table 2.

Table 2 Summary of patient demographics and characteristics for the study cohort

Appendix 2

Assessment of interpolation quality in preserving the original FE micromotion distribution

See Fig. 7.

Fig. 7
figure7

A representative example showing the close match between the micromotion distribution originally predicted by the FE models and the interpolated micromotion distributions used to train the surrogate models in this study. This is shown by the normalised frequency histograms in a and the cumulative distribution frequency plots in b of this figure

Appendix 3

Surrogate models’ ability in predicting unseen femora

See Fig. 8.

Fig. 8
figure8

Surrogate models were trained using data from a different number of randomly selected femora. The models were used to predict the micromotion range corresponding to the range of implant positions for femora not initially included in the training set. Results in a and b of this figure show that the models were not able to produce accurate predictions, with the R2 < 0.6 for all percentiles

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Al-Dirini, R.M.A., Martelli, S. & Taylor, M. Computational efficient method for assessing the influence of surgical variability on primary stability of a contemporary femoral stem in a cohort of subjects. Biomech Model Mechanobiol 19, 1283–1295 (2020). https://doi.org/10.1007/s10237-019-01235-0

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Keywords

  • Primary stability
  • Micromotion
  • Efficient models
  • Machine learning