###### Abstract
The natural vibration frequency obtained using L-RFA indicates implant stability determined by the interaction between the implant surface and the bone texture on which it is placed, similar to conventional pullout force and insertion torque tests. Therefore, the implant stability estimated in the L-RFA is predicted from the correlation expressed in terms of the natural vibration frequency and mechanical strength to be evaluated in the L-RFA [9, 10, 11]. However, the correlation between the natural vibration frequencies measured by the L-RFA and the insertion torque experiences reduced prediction accuracy, with a coefficient of determination of \(R^{2}\) = 0.867 for artificial bone using a logarithmic approximation, and \(R^{2}\) = 0.513 for cadaveric bone [10]. In addition to the difference between artificial and cadaveric bones, another issue with L-RFA is that the natural vibration frequency varies depending on the mechanical properties and shape of the evaluation sample, which can easily lead to errors. In recent years, highly accurate L-RFA has been performed using machine learning-based analyses that utilize multiple explanatory variables instead of one index of natural vibration frequency. It was reported that for a polyaxial screw with a movable neck, whose natural frequency is unstable owing to changes in the sample shape, machine learning-based analysis provides the same accuracy for implant stability as that of a monoaxial screw with a fixed shape [20]. Machine learning-based analysis techniques require large amounts of high-quality teaching data. Because L-RFA is based on the evaluation principle of measuring the natural vibration of orthopedic implants, teaching data must be prepared for each implant. However, accumulating teaching data in the clinical field is difficult because of their mechanical strength, which can be obtained through destructive testing. In addition, it is challenging to conduct non-clinical tests using cadaveric bone because the variation in donors is biased and a limited number of tests can be conducted. Therefore, it is desirable to utilize data obtained from artificial bones as teaching data, similar to previous studies [9, 10, 11]. Although differences in the structure and composition of artificial and cadaveric bones are expected to affect the accuracy of implant stability estimation, a combined analysis of these types of bone data has not been considered in previous studies [10]. In addition, although the L-RFAin previous studies was determined via the relative relationship between the implant and the bone; it did not utilize bone information, and only the natural vibration frequency of the implant was examined. Therefore, by introducing density, which provides information on the foundation, higher accuracy in estimating implant stability is anticipated. In this study, a machine learning analysis method combining L-RFA data of artificial bone, as well as the bone-density data of cadaveric bone obtained using micro-computed tomography, was used to verify the prediction accuracy of stability for a pedicle screw implant installed in cadaveric bone using L-RFA. Computed tomography (CT) is a widely used imaging modality in the field of medicine, providing detailed cross-sectional images of the human body. Since its introduction in the 1970s, CT has revolutionized diagnostic medicine by offering a non-invasive and rapid imaging technique that allows for the detection, characterization, and monitoring of various diseases and conditions. Micro-CT is a specialized imaging technique designed for the high-resolution imaging of small specimens or samples. It employs a micro-focus X-ray source and detector to generate detailed three-dimensional images of structures with sub-millimeter or even sub-micrometer spatial resolutions. Micro-CT is particularly useful in research and preclinical studies as it provides valuable insights into the microscopic structures of tissues, bones, biomaterials, and small animal models.