# Detecting the rupture of the pterygoid plate using an instrumented hammer: an animal study

Manon Bas Dit Nugues, Giuseppe Rosi, Charles Henri Flouzat-Lachaniette, Roman Hossein Khonsari, Guillaume Haiat

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Manon Bas Dit Nugues, Giuseppe Rosi, Charles Henri Flouzat-Lachaniette, Roman Hossein Khonsari, Guillaume Haiat. Detecting the rupture of the pterygoid plate using an instrumented hammer: an animal study. Multidisciplinary Biomechanics Journal, 2025, 49th congress of the Société de Biomécanique, 10.46298/mbj.14536. hal-04738023v2

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# Detecting the rupture of the pterygoid plate using an instrumented hammer: an animal study

Manon Bas dit Nugues<sup>a</sup>, Giuseppe Rosi<sup>b</sup>,
Charles Henri Flouzat-Lachaniette<sup>c</sup>,
Roman Hossein Khonsari<sup>d</sup>, Guillaume Haiat<sup>a*</sup>

a MSME CNRS UMR 8208 CNRS, Créteil, France

b MSME Université Paris-Est Créteil CNRS, Université Gustave Eiffel, Créteil, France

c INSERM U955 IMRB Université Paris-Est/APHP Hôpital Henri-Mondor Service de Chirurgie Orthopédique et Traumatologique, Créteil, France

d APHP Hôpital Necker-Enfants Malades Service de Chirurgie maxillo-faciale et chirurgie plastique/Laboratoire Forme et Croissance du Crâne, Paris, France

* Corresponding author: haiat@u-pec.fr

Received date: 06/04/2024

Accepted date: 28/06/2024

Publication date: 31/01/2025

**Keywords:** osteotomy, impact hammer, bone biomechanics, biomechanical testing

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Osteotomies are surgical procedures during which the surgeon uses an osteotome to cut bone tissue. Despite their routine clinical use, they are often performed without any visual control. For example, in many maxillofacial surgeries, the surgeon must detach the pterygoid plates from the palatal bones using an osteotome, with significant risks described in detail in (Kang, Lin, & Marentette, 2009). Although X-ray-based surgical navigation systems have been proposed (Nijmeh, Goodger, Hawkes, Edwards, & McGurk, 2005), they are not commonly used because they are radiating and they increase the operating time. Surgeons still rely on their proprioception to determine when to stop impacting the osteotome, which is important to avoid complications such as dental damage and bleeding.

Our group has developed a technique consisting in using an instrumented hammer that can provide information on the mechanical properties of the tissue located around the osteotome tip (Hubert, Rosi, Bosc, & Haiat, 2020). The aim of the present study is to determine whether a hammer instrumented with a force sensor can be used to detect the crossing of the osteotome through the pterygoid plate.

## 2. Methods

### 2.1 Experiments

31 osteotomies were performed on 16 lamb skulls with a hammer (32-6906-26, Zepf, Tuttlingen, Germany)

instrumented with a force sensor (208C04, PCB Piezotronics, Depew, NY, USA). The hammer and the osteotome were held manually. The force signal $s(t)$ obtained for each impact was analyzed using a dedicated signal processing technique. This signal, shown in Figure 1, is composed of several peaks. The first peak corresponds to the impact of the hammer on the osteotome, the following peaks correspond to the rebound of the osteotome between the hammer and the material on which the osteotome lies (Hubert et al., 2020). The indicator $\tau$ was then defined as the difference between the times of the second and first peaks of $s(t)$. The impulse ratio $\lambda$ corresponds to the ratio between the first two peak integrals.

### 2.2 Data processing

Each impact was then assigned to a class corresponding to the material surrounding the tip of the osteotome. After each impact, an operator checks visually whether the osteotome has crossed the plate or not. The canonical variable $c_1$ and $c_2$ were determined following (Krzanowski, 1998) as linear combinations of the indicator $\tau$, $\lambda$, $\Delta\tau$ and $\Delta\lambda$ given by the difference of $\tau$ and $\lambda$ between an impact and the previous one, respectively. An SVM-based algorithm for classification was then developed to automatically classify impact according to $c_1$ and $c_2$.

Since the aim of our device is to detect the impact corresponding to the osteotome crossing the bone plate, we
had to minimize the impact classified as an impact in the bone while the osteotome has already crossed the bone plate. To do so, we chose to apply a cost matrix as defined in (Liu *et al.*, 2018), which considers different weight to the misclassification. To adjust the weight apply to the misclassification, we performed an optimization procedure based on the negative likelihood ratio (Yang, Parikh, Stavros, Otto, & Maislin, 2018).

# 3. Results and discussion

## 3.1 Experimental results

The indicator $\tau$ increases when the osteotome crosses the pterygoid plates and it is negatively correlated with the plate thickness. These results are consistent with those presented in (Hubert *et al.*, 2020).

## 3.2 Classification result

The performance of the prediction algorithm before and after the use of the cost matrix is summarized in Table 1. Although the accuracy of the algorithm decreases slightly when the cost matrix is applied (from 0.958 to 0.947), the cost matrix allows to reclassify 20 “Crossing” classifies as “Bone” which is the most critical case in the clinic.

<table><caption>**Table 1.** Performance of the prediction algorithm before and after the use of the cost matrix</caption><thead><tr><th>Parameters</th><th>Without cost matrix</th><th>With cost matrix</th></tr></thead><tbody><tr><td>Total Impact Number</td><td>1591</td><td>1591</td></tr><tr><td>Correct Prediction</td><td>1527</td><td>1507</td></tr><tr><td>“Crossing” classified as “Bone”</td><td>49</td><td>29</td></tr><tr><td>“Bone” classified as “Crossing”</td><td>15</td><td>55</td></tr></tbody></table>

Credit: Manon Bas dit Nugues

# 4. Conclusions

This study shows that the hammer presented herein was able to detect the crossing of the osteotome and that it is also sensitive to the thickness of the sample being osteotomized. It could therefore provide information to the

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Example of a part of two signals $s(t)$, variation of the force as a function of time, for an impact in ‘Bone’ and an impact during a ‘Crossing’</figcaption></figure>

surgeon during the operation, thereby avoiding unnecessary impacts leading to complications. These results pave the way for the development of an intra-operative decision support system in maxillofacial surgery.

# Funding

This project has received funding from the project OrthAncil (ANR-21-CE19-0035-03) and from the project OrthoMat (ANR-21-CE17-0004). This project has received funding from the European Research Council (ERC) under Horizon 2020 (grant agreement #101062467, project ERC Proof of Concept Impactor).

# Conflict of Interest Statement

The authors declare no conflict of interest.

# References

Hubert, A., Rosi, G., Bosc, R., & Haiat, G. (2020). Using an Impact Hammer to Estimate Elastic Modulus and Thickness of a Sample During an Osteotomy. *Journal of Biomechanical Engineering*, 142(7). https://doi.org/10.1115/1.4046200

Kang, S. Y., Lin, E. M., & Marentette, L. J. (2009). Importance of complete pterygomaxillary separation in the le fort I osteotomy: An anatomic report. *Skull Base: Official Journal of North American Skull Base Society* ... [*et Al.*], 19(4), 273-277. https://doi.org/10.1055/s-0029-1220198
Krzanowski, W.J. (1998). *Principles of multivariate analysis :*
*A user's perspective* (Reprinted). Oxford: Clarendon
Press.

Liu, N., Shen, J., Xu, M., Gan, D., Qi, E.-S., & Gao, B. (2018). Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis. *Mathematical Problems in Engineering, 2018*, 1-13.
https://doi.org/10.1155/2018/3875082

Nijmeh, A. D., Goodger, N. M., Hawkes, D., Edwards, P. J., & McGurk, M. (2005). Image-guided navigation in oral and maxillofacial surgery. *British Journal of Oral and Maxillofacial Surgery, 43*(4), 294-302. https://doi.org/10.1016/j.bjoms.2004.11.018

Yang, W. T., Parikh, J. R., Stavros, A. T., Otto, P., & Maislin, G. (2018). Exploring the Negative Likelihood Ratio and How It Can Be Used to Minimize False-Positives in Breast Imaging. *American Journal of Roentgenology, 210*(2), 301-306. https://doi.org/10.2214/AJR.17.18774