In cementless total hip arthroplasty (THA), the initial stability is obtained by press-fitting the implant in the bone to allow osseointegration for a long term secondary stability. However, finding the insertion endpoint that corresponds to a proper initial stability is currently based on the tactile and auditory experiences of the orthopedic surgeon, which can be challenging. This study presents a novel real-time method based on acoustic signals to monitor the acetabular implant fixation in cementless total hip arthroplasty. Twelve acoustic in vitro experiments were performed on three types of bone models; a simple bone block model, an artificial pelvic model and a cadaveric model. A custom made beam was screwed onto the implant which functioned as a sound enhancer and insertor. At each insertion step an acoustic measurement was performed. A significant acoustic resonance frequency shift was observed during the insertion process for the different bone models; 250 Hz (35%, second bending mode) to 180 Hz (13%, fourth bending mode) for the artificial bone block models and 120 Hz (11%, eighth bending mode) for the artificial pelvis model. No significant frequency shift was observed during the cadaveric experiment due to a lack of implant fixation in this model. This novel diagnostic method shows the potential of using acoustic signals to monitor the implant seating during insertion.

Background: Joint acoustic emissions from knees have been evaluated as a convenient, non-invasive digital biomarker of inflammatory knee involvement in a small cohort of children with Juvenile Idiopathic Arthritis (JIA). The objective of the present study was to validate this in a larger cohort. Findings: A total of 116 subjects (86 JIA and 30 healthy controls) participated in this study. Of the 86 subjects with JIA, 43 subjects had active knee involvement at the time of study. Joint acoustic emissions were bilaterally recorded, and corresponding signal features were used to train a machine learning algorithm (XGBoost) to classify JIA and healthy knees. All active JIA knees and 80% of the controls were used as training data set, while the remaining knees were used as testing data set. Leave-one-leg-out cross-validation was used for validation on the training data set. Validation on the training and testing set of the classifier resulted in an accuracy of 81.1% and 87.7% respectively. Sensitivity / specificity for the training and testing validation was 88.6% / 72.3% and 88.1% / 83.3%, respectively. The area under the curve of the receiver operating characteristic curve was 0.81 for the developed classifier. The distributions of the joint scores of the active and inactive knees were significantly different. Conclusion: Joint acoustic emissions can serve as an inexpensive and easy-to-use digital biomarker to distinguish JIA from healthy controls. Utilizing serial joint acoustic emission recordings can potentially help monitor disease activity in JIA affected joints to enable timely changes in therapy.

In the research field of robotic spine surgery, there is a big upcoming momentum for surgeon-like autonomous behaviour and surgical accuracy in robotics which goes beyond the standard engineering notions such as geometric precision. The objective of this review is to present an overview of the state of the art in non-visual, non-radiative spine sensing for the enhancement of surgical techniques in robotic automation. It provides a vantage point that facilitates experimentation and guides new research projects to what has not been investigated or integrated in surgical robotics. Studies were identified, selected and processed according to the PRISMA guidelines. Relevant study characteristics that were searched for include the sensor type and measured feature, the surgical action, the tested sample, the method for data analysis and the system’s accuracy of state identification. The 6DOF f/t sensor, the microphone and the electromyography probe were the most commonly used sensors in each category, respectively. The performance of the electromyography probe is unsatisfactory in terms of preventing nerve damage as it can only signal after the nerve is disturbed. Feature thresholding and artificial neural networks were the most common decision algorithms for state identification. The fusion of different sensor data in the decision algorithm improved the accuracy of state identification.

Modal parameters are often investigated in order to assess the initial fixation of an implant. Most of studies are focused on the natural frequencies and frequency response function. Usually the femoral stem is tested although the acetabular cup fixation is important as well. The results of implant stability assessment are inconsistent and seem to suggest that frequency as a stability indicator is not sufficiently sensitive. In this study the sensitivity of the modal properties to changes in the bone-implant interface was investigated with the help of the finite element method (FEM). A novel fixation index based on modal shape curvature was investigated as a potential measure of the implant fixation. Modal frequencies are sensitive to interface changes in some manner, but suffer from insensitivity to local changes at bone-implant interface. The sensitivity up to 44% of natural frequencies to stiffness change due insertion steps was observed. The tested damage indicators are able to detect localized small changes in peripheral stiffness (5% stiffness reduction) with 95% confidence under the noise up to 1%. The modal shapes and their curvatures have a great potential to be a robust fixation indicator.