Cargando…

Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model

SIGNIFICANCE: Orthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical s...

Descripción completa

Detalles Bibliográficos
Autores principales: Kosik, Ivan, Dallaire, Frédérick, Pires, Layla, Tran, Trang, Leblond, Frédéric, Wilson, Brian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231831/
https://www.ncbi.nlm.nih.gov/pubmed/37265877
http://dx.doi.org/10.1117/1.JBO.28.5.057003
_version_ 1785051823796649984
author Kosik, Ivan
Dallaire, Frédérick
Pires, Layla
Tran, Trang
Leblond, Frédéric
Wilson, Brian
author_facet Kosik, Ivan
Dallaire, Frédérick
Pires, Layla
Tran, Trang
Leblond, Frédéric
Wilson, Brian
author_sort Kosik, Ivan
collection PubMed
description SIGNIFICANCE: Orthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages. AIM: The purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine. APPROACH: A portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train—and test on independent hold-out data subsets—a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model. RESULTS: The five-class model achieved [Formula: see text] accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches. CONCLUSIONS: We provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes.
format Online
Article
Text
id pubmed-10231831
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Society of Photo-Optical Instrumentation Engineers
record_format MEDLINE/PubMed
spelling pubmed-102318312023-06-01 Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model Kosik, Ivan Dallaire, Frédérick Pires, Layla Tran, Trang Leblond, Frédéric Wilson, Brian J Biomed Opt Sensing SIGNIFICANCE: Orthopedic surgery is frequently performed but currently lacks consensus and availability of ideal guidance methods, resulting in high variability of outcomes. Misdirected insertion of surgical instruments can lead to weak anchorage and unreliable fixation along with risk to critical structures including the spinal cord. Current methods for surgical guidance using conventional medical imaging are indirect and time-consuming with unclear advantages. AIM: The purpose of this study was to investigate the potential of intraoperative in situ near-infrared Raman spectroscopy (RS) combined with machine learning in guiding pedicular screw insertion in the spine. APPROACH: A portable system equipped with a hand-held RS probe was used to make fingerprint measurements on freshly excised porcine vertebrae, identifying six tissue types: bone, spinal cord, fat, cartilage, ligament, and muscle. Supervised machine learning techniques were used to train—and test on independent hold-out data subsets—a six-class model as well as two-class models engineered to distinguish bone from soft tissue. The two-class models were further tested using in vivo spectral fingerprint measurements made during intra-pedicular drilling in a porcine spine model. RESULTS: The five-class model achieved [Formula: see text] accuracy in distinguish all six tissue classes when applied onto a hold-out testing data subset. The binary classifier detecting bone versus soft tissue (all soft tissue or spinal cord only) yielded 100% accuracy. When applied onto in vivo measurements performed during interpedicular drilling, the soft tissue detection models correctly detected all spinal canal breaches. CONCLUSIONS: We provide a foundation for RS in the orthopedic surgical guidance field. It shows that RS combined with machine learning is a rapid and accurate modality capable of discriminating tissues that are typically encountered in orthopedic procedures, including pedicle screw placement. Future development of integrated RS probes and surgical instruments promises better guidance options for the orthopedic surgeon and better patient outcomes. Society of Photo-Optical Instrumentation Engineers 2023-05-31 2023-05 /pmc/articles/PMC10231831/ /pubmed/37265877 http://dx.doi.org/10.1117/1.JBO.28.5.057003 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Sensing
Kosik, Ivan
Dallaire, Frédérick
Pires, Layla
Tran, Trang
Leblond, Frédéric
Wilson, Brian
Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
title Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
title_full Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
title_fullStr Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
title_full_unstemmed Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
title_short Preclinical evaluation of Raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
title_sort preclinical evaluation of raman spectroscopy for pedicular screw insertion surgical guidance in a porcine spine model
topic Sensing
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231831/
https://www.ncbi.nlm.nih.gov/pubmed/37265877
http://dx.doi.org/10.1117/1.JBO.28.5.057003
work_keys_str_mv AT kosikivan preclinicalevaluationoframanspectroscopyforpedicularscrewinsertionsurgicalguidanceinaporcinespinemodel
AT dallairefrederick preclinicalevaluationoframanspectroscopyforpedicularscrewinsertionsurgicalguidanceinaporcinespinemodel
AT pireslayla preclinicalevaluationoframanspectroscopyforpedicularscrewinsertionsurgicalguidanceinaporcinespinemodel
AT trantrang preclinicalevaluationoframanspectroscopyforpedicularscrewinsertionsurgicalguidanceinaporcinespinemodel
AT leblondfrederic preclinicalevaluationoframanspectroscopyforpedicularscrewinsertionsurgicalguidanceinaporcinespinemodel
AT wilsonbrian preclinicalevaluationoframanspectroscopyforpedicularscrewinsertionsurgicalguidanceinaporcinespinemodel