University of Oulu

M. Prakash, A. Joukainen, J. Torniainen, M.K.M. Honkanen, L. Rieppo, I.O. Afara, H. Kröger, J. Töyräs, J.K. Sarin, Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy, Osteoarthritis and Cartilage, Volume 27, Issue 8, 2019, Pages 1235-1243, ISSN 1063-4584, https://doi.org/10.1016/j.joca.2019.04.008

Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy

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Author: Prakash, M.1,2; Joukainen, A.3; Torniainen, J.1,2;
Organizations: 1Department of Applied Physics, University of Eastern Finland, Finland
2Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
3Department of Orthopedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland
4Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
5School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2019091828597
Language: English
Published: Elsevier, 2019
Publish Date: 2020-04-23
Description:

Abstract

Objective: To investigate the feasibility of near-infrared (NIR) spectroscopy (NIRS) for evaluation of human articular cartilage biomechanical properties during arthroscopy.

Design: A novel arthroscopic NIRS probe designed in our research group was utilized by an experienced orthopedic surgeon to measure NIR spectra from articular cartilage of human cadaver knee joints (ex vivo, n = 18) at several measurement locations during an arthroscopic surgery. Osteochondral samples (n = 265) were extracted from the measurement sites for reference analysis. NIR spectra were remeasured in a controlled laboratory environment (in vitro), after which the corresponding cartilage thickness and biomechanical properties were determined. Hybrid multivariate regression models based on principal component analysis and linear mixed effects modeling (PCA-LME) were utilized to relate cartilage in vitro spectra and biomechanical properties, as well as to account for the spatial dependency. Additionally, a k-nearest neighbors (kNN) classifier was employed to reject outlying ex vivo NIR spectra resulting from a non-optimal probe-cartilage contact. Model performance was evaluated for both in vitro and ex vivo NIR spectra via Spearman’s rank correlation (ρ) and the ratio of performance to interquartile range (RPIQ).

Results: Regression models accurately predicted cartilage thickness and biomechanical properties from in vitro NIR spectra (Model: 0.77 ≤ ρ ≤ 0.87, 2.03 ≤ RPIQ ≤ 3.0; Validation: 0.74 ≤ ρ ≤ 0.84, 1.87 ≤ RPIQ ≤ 2.90). When predicting cartilage properties from ex vivo NIR spectra (0.33 ≤ ρ ≤ 0.57 and 1.02 ≤ RPIQ ≤ 2.14), a kNN classifier enhanced the accuracy of predictions (0.52 ≤ ρ ≤ 0.87 and 1.06 ≤ RPIQ ≤ 1.88).

Conclusion: Arthroscopic NIRS could substantially enhance identification of damaged cartilage by enabling quantitative evaluation of cartilage biomechanical properties. The results demonstrate the capacity of NIRS in clinical applications.

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Series: Osteoarthritis and cartilage
ISSN: 1063-4584
ISSN-E: 1522-9653
ISSN-L: 1063-4584
Volume: 27
Issue: 8
Pages: 1235 - 1243
DOI: 10.1016/j.joca.2019.04.008
OADOI: https://oadoi.org/10.1016/j.joca.2019.04.008
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Subjects:
Funding: This study was funded by the Academy of Finland (projects 267551, 2315820 and 269315, University of Eastern Finland), Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (project PY210, 5041750, 5041744, 5041772, 5041746, 5041757, 5654149 and 5041778), Kuopio, Finland), Instrumentarium Science Foundation (170033), and The Finnish Foundation for Technology Promotion (8193, 6227).
Copyright information: © 2019 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/