Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
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- Katsuhiro Mikami
- Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama 649-6493, Japan
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- Mitsutaka Nemoto
- Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama 649-6493, Japan
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- Takeo Nagura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan
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- Masaya Nakamura
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan
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- Morio Matsumoto
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan
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- Daisuke Nakashima
- Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan
説明
<jats:p>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.</jats:p>
収録刊行物
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- Sensors
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Sensors 21 (22), 7553-, 2021-11-13
MDPI AG