Validation of an Instrumented Hammer for Rhinoplasty Osteotomies: A Cadaveric Study.

Background: Osteotomies during rhinoplasty are usually based on surgeon's proprioception to determine the number, energy, and trajectory of impacts. Objective: The first objective was to detect the occurrence of fractures. The second objective was to determine when the thicker frontal bone was encountered by the osteotome. Materials and Methods: An instrumented hammer was used to measure the impact force during lateral osteotomies on nine human anatomic specimens. A prediction algorithm was developed using machine learning techniques, to detect the occurrence of fractures, and the proximity of the osteotome to the frontal bone. Results: The algorithm was able to predict the occurrence of fractures and the proximity to the frontal bone with a prediction rate of 83%, 91%, and 93% when allowing for an error of 0, 1, and 2 impacts, respectively. The location of the osteotome in the frontal bone was predicted with an error of 7.7%. Conclusion: An osteotomy hammer measuring the impact force when performing lateral osteotomies can predict the occurrence of fractures and the proximity to the frontal bone, providing the surgeon with instant feedback.

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.

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

Osteotomies are common surgical procedures used for instance in rhinoplasty and usually performed using an osteotome impacted by a mallet. Visual control being difficult, osteotomies are often based on the surgeon proprioception to determine the number and energy of each impact. The aim of this study is to determine whether a hammer instrumented with a piezoelectric force sensor can be used to (i) follow the displacement of the osteotome and (ii) determine when the tip of the osteotome arrives in frontal bone, which corresponds to the end of the osteotomy pathway. Seven New Zealand White rabbit heads were collected, and two osteotomies were performed on their left and right nasal bones using the instrumented hammer to record the variation of the force as a function of time during each impact. The second peak time τ was derived from each signal while the displacement of the osteotome tip D was determined using video motion tracking. The results showed a significant correlation between τ and D (ρ2 = 0.74), allowing to estimate the displacement of the osteotome through the measurement of τ. The values of τ measured in the frontal bone were significantly lower than in the nasal bone (p<10-10), which allows to determine the transition between the nasal and frontal bones when τ becomes lower than 0.78 its initial averaged value. Although results should be validated clinically, this technology could be used by surgeons in the future as a decision support system to help assessing the osteotome environment.