Research Paper:
Evaluation Using AI-Based Approach of Pedicle Screw Stability Using Laser Resonance Frequency Analysis Prior to Surgical Insertion
Katsuhiro Mikami*1,
, Shogo Hashimoto*2, Tetsuya Matsuyama*2, Mitsutaka Nemoto*1
, Takashi Nagaoka*1
, Yuichi Kimura*3
, Takuto Hatakeyama*4
, Takeo Nagura*4,*5
, and Daisuke Nakashima*4,*5

*1Faculty of Biology-Oriented Science and Technology, Kindai University
930 Nishi-mitani, Kinokawa, Wakayama 649-6493, Japan
Corresponding author
*2Graduate School of Biology-Oriented Science and Technology, Kindai University
930 Nishi-mitani, Kinokawa, Wakayama 649-6493, Japan
*3Faculty of Informatics, Cyber Informatics Research Institute, Kindai University
3-4-1 Kowakae, Higashiosaka, Osaka 577-8502, Japan
*4Department of Orthopedic Surgery, Keio University School of Medicine
35 Shinano-machi, Shinjuku-ku, Tokyo 160-8582, Japan
*5Department of Clinical Biomechanics, Keio University School of Medicine
35 Shinano-machi, Shinjuku-ku, Tokyo 160-8582, Japan
In orthopedics, assessing the initial stability of pedicle screws is crucial for preventing failure after implantation. Traditional methods for determining stability are applicable only to post-screw insertion, complicating recovery in cases of inadequate strength. This study aimed to predict implant stability at the tapping stage prior to screw placement. Artificial bones with five distinct densities were utilized to measure tapping and screw insertion torques, facilitating a statistical evaluation of bone density and placement strength. A laser resonance frequency analysis (L-RFA) was conducted to obtain the vibration frequency spectra of the tap. Furthermore, linear approximation and support vector regression (SVR) were employed, utilizing spectral data and a 95% confidence interval to predict bone density. Significant differences in tapping and screw insertion torques were observed relative to bone density (p<0.01). Prediction based on a wide frequency range utilizing SVR improved the mean squared error from 26.9 to 8.08 when compared to traditional regression prediction, resulting in a coefficient of determination R2 of 0.843. The acquisition of bone density data at the tapping stage enables the prediction of expected screw stability. Bone density prediction using L-RFA has the potential to provide stability indicators prior to pedicle screw insertion.
Prediction scheme for stability using L-RFA
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