Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama 649-6493, Japan.
Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo 160-8582, Japan.
Sensors (Basel). 2021 Nov 13;21(22):7553. doi: 10.3390/s21227553.
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.
评估植入物的初始稳定性对于减少骨科手术后椎弓根螺钉的植入物失败数量至关重要。激光共振频率分析（L-RFA）最近已被提议作为一种可行的诊断方案。在之前的一项研究中，L-RFA 用于证明具有固定头的单轴螺钉的植入物稳定性的诊断。然而，在实践中也经常使用带有可移动头的多轴螺钉。在本文中，我们阐明了激光诱导的多轴螺钉振动光谱的特性，这些特性是进行 L-RFA 诊断植入物稳定性所必需的。此外，还展示并提出了一种基于机器学习的振动光谱分析新方案。所提出的基于机器学习的诊断方法对多轴椎弓根螺钉的植入物稳定性（峰值扭矩）具有高度准确的预测。这一成果将为使用具有运动部件和各种临床情况下使用的形状的植入物的 L-RFA 进行植入物稳定性诊断提供一种重要的分析方法。
