Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws

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...

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Autores principales:	Katsuhiro Mikami, Mitsutaka Nemoto, Takeo Nagura, Masaya Nakamura, Morio Matsumoto, Daisuke Nakashima
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	orthopedics pedicle screw stability diagnosis laser resonance frequency analysis Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/c547ebd30d0945cbb52d537e39a26d3e
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spelling	oai:doaj.org-article:c547ebd30d0945cbb52d537e39a26d3e2021-11-25T18:57:24ZMachine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws10.3390/s212275531424-8220https://doaj.org/article/c547ebd30d0945cbb52d537e39a26d3e2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7553https://doaj.org/toc/1424-8220Evaluation 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.Katsuhiro MikamiMitsutaka NemotoTakeo NaguraMasaya NakamuraMorio MatsumotoDaisuke NakashimaMDPI AGarticleorthopedicspedicle screwstability diagnosislaser resonance frequency analysisChemical technologyTP1-1185ENSensors, Vol 21, Iss 7553, p 7553 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	orthopedics pedicle screw stability diagnosis laser resonance frequency analysis Chemical technology TP1-1185
spellingShingle	orthopedics pedicle screw stability diagnosis laser resonance frequency analysis Chemical technology TP1-1185 Katsuhiro Mikami Mitsutaka Nemoto Takeo Nagura Masaya Nakamura Morio Matsumoto Daisuke Nakashima Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
description	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.
format	article
author	Katsuhiro Mikami Mitsutaka Nemoto Takeo Nagura Masaya Nakamura Morio Matsumoto Daisuke Nakashima
author_facet	Katsuhiro Mikami Mitsutaka Nemoto Takeo Nagura Masaya Nakamura Morio Matsumoto Daisuke Nakashima
author_sort	Katsuhiro Mikami
title	Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
title_short	Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
title_full	Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
title_fullStr	Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
title_full_unstemmed	Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws
title_sort	machine learning-based diagnosis in laser resonance frequency analysis for implant stability of orthopedic pedicle screws
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c547ebd30d0945cbb52d537e39a26d3e
work_keys_str_mv	AT katsuhiromikami machinelearningbaseddiagnosisinlaserresonancefrequencyanalysisforimplantstabilityoforthopedicpediclescrews AT mitsutakanemoto machinelearningbaseddiagnosisinlaserresonancefrequencyanalysisforimplantstabilityoforthopedicpediclescrews AT takeonagura machinelearningbaseddiagnosisinlaserresonancefrequencyanalysisforimplantstabilityoforthopedicpediclescrews AT masayanakamura machinelearningbaseddiagnosisinlaserresonancefrequencyanalysisforimplantstabilityoforthopedicpediclescrews AT moriomatsumoto machinelearningbaseddiagnosisinlaserresonancefrequencyanalysisforimplantstabilityoforthopedicpediclescrews AT daisukenakashima machinelearningbaseddiagnosisinlaserresonancefrequencyanalysisforimplantstabilityoforthopedicpediclescrews
_version_	1718410488451170304

Machine Learning-Based Diagnosis in Laser Resonance Frequency Analysis for Implant Stability of Orthopedic Pedicle Screws

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