Using an impact hammer to perform biomechanical measurements during osteotomies: Study of an animal model

Characterization of the concentration of agar-based soft tissue mimicking phantoms by impact analysis

In various medical fields, a change of soft tissue stiffness is associated with its physio-pathological evolution. While elastography is extensively employed to assess soft tissue stiffness in vivo, its application requires a complex and expensive technology. The aim of this study is to determine whether an easy-to-use method based on impact analysis can be employed to determine the concentration of agar-based soft tissue mimicking phantoms. Impact analysis was performed on soft tissue mimicking phantoms made of agar gel with a mass concentration ranging from 1% to 5%. An indicator Δt is derived from the temporal variation of the impact force signal between the hammer and a small beam in contact with the sample. The results show a non-linear decrease of Δt as a function of the agar concentration (and thus of the sample stiffness). The value of Δt provides an estimation of the agar concentration with an error of 0.11%. This sensitivity of the impact analysis based method to the agar concentration is of the same order of magnitude than results obtained with elastography techniques. This study opens new paths towards the development of impact analysis for a fast, easy and relatively inexpensive clinical evaluation of soft tissue elastic properties.

Detection of periprosthetic fractures around the femoral stem by resonance frequency analysis: An in vitro study

Periprosthetic femoral bone fractures are frequent complications of Total Hip Arthroplasty (THA) and may occur during the insertion of uncemented Femoral Stems (FS), due to the nature of the press-fit fixation. Such fracture may lead to the surgical failure of the THA and require a revision surgery, which may have dramatic consequences. Therefore, an early detection of intra-operative fractures is important to avoid worsening the fracture and/or to enable a peroperative treatment. The aim of this in vitro study is to determine the sensitivity of a method based on resonance frequency analysis of the bone-stem-ancillary system for periprosthetic fractures detection. A periprosthetic fracture was artificially created close to the lesser-trochanter of 10 femoral bone mimicking phantoms. The bone-stem-ancillary resonance frequencies in the range (2-12) kHz were measured on an ancillary instrumented with piezoelectric sensors, which was fixed to the femoral stem. The measurements were repeated for different fracture lengths from 4 to 55 mm. The results show a decrease of the resonance frequencies due to the fracture occurrence and propagation. The frequency shift reached up to 170 Hz. The minimum fracture length that can be detected varies from 3.1±1.7 mm to 5.9±1.9 mm according to the mode and to the specimen. A significantly higher sensitivity (p = 0.011) was obtained for a resonance frequency around 10.6 kHz, corresponding to a mode vibrating in a plane perpendicular to the fracture. This study opens new paths toward the development of non-invasive vibration-based methods for intra-operative periprosthetic fractures detection.

Using an Instrumented Hammer to Predict the Rupture of Bone Samples Subject to an Osteotomy

Osteotomies are common procedures in maxillofacial and orthopedic surgery. The surgeons still rely on their proprioception to control the progression of the osteotome. Our group has developed an instrumented hammer that was shown to provide information on the biomechanical properties of the tissue located around the osteotome tip. The objective of this study is to determine if this approach may be used to predict the rupture of a bone sample thanks to an instrumented hammer equipped with a force sensor. For each impact, an indicator τ is extracted from the signal corresponding to the variation of the force as a function of time. A linear by part regression analysis is applied to the curve corresponding to the variation of τ as a function of the distance d between the tip of the osteotome and the end of the sample. The experiments were conducted with plywood and bovine trabecular bone samples. The results show that τ starts increasing when the value of d is lower than 2.6 mm on average, which therefore corresponds to a typical threshold detection distance between the osteotome tip and the sample end. These findings open new paths for the development of this instrumented surgical hammer.

Anatomical subject validation of an instrumented hammer using machine learning for the classification of osteotomy fracture in rhinoplasty

Osteotomies during rhinoplasty are usually based on the surgeon's proprioception to determine the number and the strength of the impacts. The aim of this study is to determine whether a hammer instrumented with a force sensor can be used to classify fractures and to determine the location of the osteotome tip. Two lateral osteotomies were realized in nine anatomical subjects using an instrumented hammer recording the evolution of the impact force. Two indicators τ and λ were derived from the signal, and video analysis was used to determine whether the osteotome tip was located in nasal or frontal bone as well as the condition of the bone tissue around the osteotome tip. A machine-learning algorithm was used to predict the condition of bone tissue after each impact. The algorithm was able to predict the condition of the bone after the impacts with an accuracy of 83%, 91%, and 93% when considering a tolerance of 0, 1, and 2 impacts, respectively. Moreover, in nasal bone, the values of τ and λ were significantly lower (p < 10^-10) and higher (p < 10^-4) than in frontal bone, respectively. This study paves the way for the development of the instrumented hammer as a decision support system.