# Preoperative Personalization of Hallux Valgus Biomechanics: Integrating Clinical Data into Finite Element Modelling

Léonore Bordere, Wei Wei, Michel Behr

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Léonore Bordere, Wei Wei, Michel Behr. Preoperative Personalization of Hallux Valgus Biomechanics: Integrating Clinical Data into Finite Element Modelling. Multidisciplinary Biomechanics Journal, 2025, 50th congress of the.., 10.46298/mbj.16213 . hal-05191766v2

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# Preoperative Personalization of Hallux Valgus Biomechanics: Integrating Clinical Data into Finite Element Modelling

Léonore Bordere<sup>a, b*</sup>, Wei Wei<sup>a</sup>, Michel Behr<sup>a</sup>

<sup>a</sup> Laboratoire de Biomécanique Appliquée Univ Eiffel, AMU, Marseille, France

<sup>b</sup> Xfeet-Orthotics, Montpellier, France

* Corresponding author: leonore.bordere@univ-eiffel.fr

Received date: 05/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** personalization, finite element model, validation, hallux valgus, foot

© 2025 The Authors
Licence CC-BY 4.0
Published by Société de Biomécanique

## 1. Introduction

Hallux valgus is a common foot deformity often requiring surgery, however recurrence rates remain high (up to 64% at 5 years) (Thomas & Barrington, 2003; Netto, 2023). Finite element (FE) modeling offers potential for improving surgical planning by understanding the biomechanical impact of surgery. Yet, current models are usually limited to geometric personalization and lack sufficient validation. Most rely on complex, non-clinical data and remain static (Shih, 2023; Xie, 2023), questioning their predictive reliability. This study proposes a clinically accessible workflow for preoperative FE modeling, combining geometric and biomechanical personalization. These inputs are integrated into a dynamic simulation and validated against experimental data. This approach aims to enable the access to reliable validated patient-specific model using data readily available in routine clinical practice, suitable for surgical planning.

## 2. Methods

### 2.1 Patient and Clinical Data Acquisition

A 71-year-old female patient with severe Hallux Valgus (hallux valgus angle: 39°, intermetatarsal angle: 19°) was included prior to corrective surgery. Preoperative CT and weight-bearing radiographs were acquired. Clinical motion analysis was performed using three synchronized cameras, twelve anatomical markers, and a pressure plate (Win-pod, Medicapteurs). The patient

completed three trials of unipedal loading and natural-speed walking. Marker tracking and post-processing enabled 3D motion reconstruction and plantar pressure extraction for model personalization and validation.

### 2.2 Finite element model

A previously detailed and validated 3D FE foot model (Tarrade, 2020) was used as a baseline. It includes 28 rigid bones, rigid tension-only elements for plantar fascia, skin and encapsulated soft tissues with respectively hyperplastic and elastic behaviours and a rigid plate modelling the ground. Joints are represented by 6-DOF nonlinear modular springs with literature-based translational and rotational stiffness curves, enabling realistic joint mobility and intrinsic muscle action without explicit ligaments. Static FE simulations reproduced unipedal stance with vertical bodyweight loading. Dynamic FE simulations used a time-dependant mobile-ground setup (Wong., 2018) with subject-specific experimental gait data (GRF, a,kle angle, walking speed). Triceps surae traction was applied as the main muscular force.

### 2.3 Static Personalization

The model was geometrically personalized (see Fig.1) using radial basis function morphing with automatic landmark selection from segmented CT scans.

Once the geometric personalization achieved, the biomechanical one was performed. The model was personalized under static loading through a 13-parameter
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Qualitative comparison between patient's foot acquisition (on top) and finite element geometrically personalized (bottom).</figcaption></figure>

Credit: Léonore Bordere.

DOE-based optimization. Parameters included plantar fascia stiffness, triceps surae traction, and torque/motion amplitudes of key parameters joints. Optimization was performed by minimizing objective function based on plantar pressures characteristics (surface, position of CoP, maximum pressure by regions), loaded bony structure (extracted from radiographs) and kinematic data from clinical marker position.

## 2.4 Dynamic Simulation and Validation

Once static agreement was achieved, the optimized parameters were implemented into a dynamic simulation of the personalized walking cycle. Validation was performed using plantar pressure distributions at key moment of the gait (heel strike, mid stance and push off) and 3D kinematic trajectories.

## 3. Results and discussion

Geometric morphing achieved high accuracy with a mean error of 0.6 mm, a Hausdorff distance of 4 mm and <1 % distorted elements (Jacobian, aspect ratio, element length).

The static parameter optimization led to two results. First, the best set of parameters showed very good agreement in static foot behaviour with errors below 6% across all criteria (key parameters in Table 1). When transferred to the dynamic model, these parameters yielded satisfactory results in both plantar pressure and kinematic outputs.

Besides, sensitivity analysis was performed on the 13 input parameters of the DOE to assess their influence on key output responses (as the medial longitudinal arch angle, navicular drop or maximum pressure). Pearson correlation coefficients revealed five significant

parameters (p < 0.05): triceps surae traction, subtalar joint position and stiffness, joint translation stiffness, and MTP joint stiffness. These results support future model simplification by enabling reduced-parameter personalization without compromising predictive performance.

<table><caption>**Table 1.** Static Validation – Percent Errors on Selected Criteria for 100% BW.</caption><thead><tr><td>Criterion (100% BW)</td><td>Target data</td><td>Error (%)</td></tr></thead><tbody><tr><td>Medial Longitudinal Arche Angle (°)</td><td>156.3</td><td>0.5</td></tr><tr><td>Navicular Drop (mm)</td><td>13</td><td>0.9</td></tr><tr><td>Length M1M5 (mm)</td><td>106.2</td><td>-0.7</td></tr><tr><td>1st Intermetatarsal angle (°)</td><td>15.8</td><td>3.9</td></tr><tr><td>Rearfoot deviation (°)</td><td>169</td><td>-0.6</td></tr><tr><td>Hallux Valgus Angle (°)</td><td>41.9</td><td>-1.9</td></tr><tr><td>Rearfoot Pmax (MPa)</td><td>0.19</td><td>5.9</td></tr><tr><td>Forefoot Pmax (MPa)</td><td>0.09</td><td>4.2</td></tr><tr><td>Contact surface (cm²)</td><td>109</td><td>2.7</td></tr></tbody></table>

Credit: Léonore Bordere.

## 4. Conclusions

The level of validation achieved in both static and dynamic loading conditions demonstrates the feasibility of building a reliable preoperative finite element model of the Hallux Valgus foot using a streamlined personalization process. Furthermore, by identifying a small set of biomechanically influential parameters, the workflow enables a reduction in model complexity while improving clinical applicability. This approach enhances the reliability of patient-specific simulations and sets the stage for postoperative model adaptation and the evaluation of predictive accuracy. Overall, it represents a key step toward the development of clinically usable predictive tools to support the planning and optimization of Hallux Valgus correction strategies.

## Acknowledgements

The authors would like to thank the Clinique du Pied (Nice, France) and the doctor Martin SCHRAMM for the clinical partnership.

## Conflict of Interest Statement

None.
## Contributor Roles

LB: Conceptualization, Methodology, writing original draft; WW: Methodology, Supervision, Writing-review & editing; MB: Methodology, Supervision, Writing-review & editing.

## Funding

The authors would like to thank Xfeet Orthotics (Montpellier, France) for providing financial support to this project.

## References

* Thomas, S., & Barrington, R. (2003). Hallux valgus. *Current Orthopaedics*, 17(4), 299–307. https://doi.org/10.1016/S0268-0890(02)00184-6

* Netto, C. C. (2023). From 2D to 3D: Understanding hallux valgus deformity. *Foot & Ankle International*, 44(8), 788–789. https://doi.org/10.1177/10711007231180573

* Tarrade, T., Dakhil, N., Behr, M., Salin, D., & Llari, M. (2020). Real-time analysis of the dynamic foot function: A machine learning and finite element approach. *Journal of Biomechanical Engineering*, 143(4), 041005. https://doi.org/10.1115/1.4049024

* Shih, K.-S., Hsu, C.-C., & Huang, G.-T. (2023). Biomechanical investigation of hallux valgus deformity treated with different osteotomy methods and Kirschner wire fixation strategies using the finite element method. *Bioengineering*, 10. https://doi.org/10.3390/bioengineering10040499

* Xie, Q., Li, X., & Wang, P. (2023). Three-dimensional finite element analysis of biomechanics of osteotomy ends with three different fixation methods after hallux valgus minimally invasive osteotomy. *Journal of Orthopaedic Surgery*, 31, 10225536231175235. https://doi.org/10.1177/10225536231175235

* Wong, D. W.-C., Wang, Y., Leung, A. K.-L., Yang, M., & Zhang, M. (2018). Finite element simulation on posterior tibial tendinopathy: Load transfer alteration and implications to the onset of pes planus. *Clinical Biomechanics*, 51, 10–16. https://doi.org/10.1016/j.clinbiomech.2017.11.001