Resonance frequency analysis (RFA) is valuable for assessing implant status. In a previous investigation, acetabular cup fixation was assessed using laser RFA and the pull-down force was predicted in an in vitro setting. While the pull-down force alone is sufficient for initial fixation evaluation, it is desirable to evaluate the bone strength of the foundation for subsequent fixation. Diminished bone quality causes micromotion, migration, and protracted osseointegration, consequently elevating susceptibility to periprosthetic fractures and failure of ingrained trabecular bone. Limited research exists on the evaluation of bone mineral density (BMD) around the cup using RFA. For in vivo application of laser RFA, we implemented the sweep pulse excitation method and engineered an innovative laser RFA device having low laser energy and small dimensions. We focused on a specific frequency range (2500-4500 Hz), where the peak frequency was presumed to be influenced by foundational density. Quantitative computed tomography with a phantom was employed to assess periprosthetic BMD. Correlation between the resonance frequency within the designated range and the density around the cup was evaluated both in the laboratory and in vivo using the novel laser RFA device. The Kruskal-Wallis test showed robust correlations in both experiments (laboratory study: R = 0.728, p < 0.001; in vivo study: R = 0.619, p < 0.001). Our laser RFA system can assess the quality of bone surrounding the cup. Laser RFA holds promise in predicting the risk of loosening and might aid in the decision-making process for additional fixation through screw insertion.

Evaluation of the initial stability of implants is essential to reduce the number of implant failures of pedicle screws after orthopedic surgeries. Laser resonance frequency analysis (L-RFA) has been recently proposed as a viable diagnostic scheme in this regard. In a previous study, L-RFA was used to demonstrate the diagnosis of implant stability of monoaxial screws with a fixed head. However, polyaxial screws with movable heads are also frequently used in practice. In this paper, we clarify the characteristics of the laser-induced vibrational spectra of polyaxial screws which are required for making L-RFA diagnoses of implant stability. In addition, a novel analysis scheme of a vibrational spectrum using L-RFA based on machine learning is demonstrated and proposed. The proposed machine learning-based diagnosis method demonstrates a highly accurate prediction of implant stability (peak torque) for polyaxial pedicle screws. This achievement will contribute an important analytical method for implant stability diagnosis using L-RFA for implants with moving parts and shapes used in various clinical situations.