Machine learning-based diagnosis in laser resonance frequency analysis for implant stability of orthopedic pedicle screws

Katsuhiro Mikami, Mitsutaka Nemoto, Takeo Nagura, Masaya Nakamura, Morio Matsumoto, Daisuke Nakashima

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号7553
ジャーナルSensors
21
22
DOI
出版ステータスPublished - 2021 11月 1
外部発表はい

ASJC Scopus subject areas

  • 分析化学
  • 情報システム
  • 原子分子物理学および光学
  • 生化学
  • 器械工学
  • 電子工学および電気工学

フィンガープリント

「Machine learning-based diagnosis in laser resonance frequency analysis for implant stability of orthopedic pedicle screws」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル