CNRS, Laboratoire Modélisation et Simulation Multi-Echelle, UMR CNRS 8208, 61 Avenue du Général de Gaulle, Créteil 94010, France.
Université Paris-Est, Laboratoire Modélisation et Simulation Multi Echelle, MSME UMR 8208 CNRS, 61 av du Général de Gaulle, Créteil 94010, France.
J Biomech Eng. 2020 Jul 1;142(7). doi: 10.1115/1.4046200.
Performing an osteotomy with a surgical mallet and an osteotome is a delicate intervention mostly based on the surgeon proprioception. It remains difficult to assess the properties of bone tissue being osteotomized. Mispositioning of the osteotome or too strong impacts may lead to bone fractures which may have dramatic consequences. The objective of this study is to determine whether an instrumented hammer may be used to retrieve information on the material properties around the osteotome tip. A hammer equipped with a piezo-electric force sensor was used to impact 100 samples of different composite materials and thicknesses. A model-based inversion technique was developed based on the analysis of two indicators derived from the analysis of the variation of the force as a function of time in order to (i) classify the samples depending on their material types, (ii) determine the materials stiffness, and (iii) estimate the samples thicknesses. The model resulting from the classification using support vector machines (SVM) learning techniques can efficiently predict the material of a new sample, with an estimated 89% prediction performance. A good agreement between the forward analytical model and the experimental data was obtained, leading to an average error lower than 10% in the samples thickness estimation. Based on these results, navigation and decision-support tools could be developed and allows surgeons to adapt their surgical strategy in a patient-specific manner.
使用手术锤和骨刀进行截骨术是一种精细的干预措施，主要基于外科医生的本体感觉。评估正在截骨的骨组织的特性仍然很困难。骨刀的位置不当或冲击力过强可能导致骨折，这可能会产生严重后果。本研究的目的是确定仪器化的锤子是否可用于获取骨刀尖端周围材料特性的信息。一个配备压电测力传感器的锤子用于冲击 100 个不同复合材料和厚度的样本。基于对作为时间函数的力变化的分析，开发了一种基于模型的反演技术，以便（i）根据材料类型对样本进行分类，（ii）确定材料的刚度，以及（iii）估计样本的厚度。使用支持向量机 (SVM) 学习技术进行分类的模型可以有效地预测新材料样本的材料，估计的预测性能为 89%。在样本厚度估计方面，获得了正向分析模型与实验数据之间的良好一致性，平均误差低于 10%。基于这些结果，可以开发导航和决策支持工具，并允许外科医生以患者特异性的方式调整他们的手术策略。
