Cargando…

Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties

OBJECTIVE: To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN: Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared...

Descripción completa

Detalles Bibliográficos
Autores principales: Shehata, Eslam, Nippolainen, Ervin, Shaikh, Rubina, Ronkainen, Ari-Petteri, Töyräs, Juha, Sarin, Jaakko K., Afara, Isaac O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518284/
https://www.ncbi.nlm.nih.gov/pubmed/37328704
http://dx.doi.org/10.1007/s10439-023-03271-5
_version_ 1785109478553681920
author Shehata, Eslam
Nippolainen, Ervin
Shaikh, Rubina
Ronkainen, Ari-Petteri
Töyräs, Juha
Sarin, Jaakko K.
Afara, Isaac O.
author_facet Shehata, Eslam
Nippolainen, Ervin
Shaikh, Rubina
Ronkainen, Ari-Petteri
Töyräs, Juha
Sarin, Jaakko K.
Afara, Isaac O.
author_sort Shehata, Eslam
collection PubMed
description OBJECTIVE: To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN: Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. RESULTS: The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). CONCLUSION: RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10439-023-03271-5.
format Online
Article
Text
id pubmed-10518284
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-105182842023-09-26 Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties Shehata, Eslam Nippolainen, Ervin Shaikh, Rubina Ronkainen, Ari-Petteri Töyräs, Juha Sarin, Jaakko K. Afara, Isaac O. Ann Biomed Eng Original Article OBJECTIVE: To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN: Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. RESULTS: The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). CONCLUSION: RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10439-023-03271-5. Springer International Publishing 2023-06-16 2023 /pmc/articles/PMC10518284/ /pubmed/37328704 http://dx.doi.org/10.1007/s10439-023-03271-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Shehata, Eslam
Nippolainen, Ervin
Shaikh, Rubina
Ronkainen, Ari-Petteri
Töyräs, Juha
Sarin, Jaakko K.
Afara, Isaac O.
Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
title Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
title_full Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
title_fullStr Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
title_full_unstemmed Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
title_short Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties
title_sort raman spectroscopy and machine learning enables estimation of articular cartilage structural, compositional, and functional properties
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518284/
https://www.ncbi.nlm.nih.gov/pubmed/37328704
http://dx.doi.org/10.1007/s10439-023-03271-5
work_keys_str_mv AT shehataeslam ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties
AT nippolainenervin ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties
AT shaikhrubina ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties
AT ronkainenaripetteri ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties
AT toyrasjuha ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties
AT sarinjaakkok ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties
AT afaraisaaco ramanspectroscopyandmachinelearningenablesestimationofarticularcartilagestructuralcompositionalandfunctionalproperties