ABSTRACT
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.
