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Osteoarthritis Cartilage. 2019 Apr 23. pii: S1063-4584(19)30931-8. doi: 10.1016/j.joca.2019.04.008. [Epub ahead of print]

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

Author information

1
Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. Electronic address: mithilesh.prakash@uef.fi.
2
Department of Orthopedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland. Electronic address: antti.joukainen@kuh.fi.
3
Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. Electronic address: jari.torniainen@uef.fi.
4
Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. Electronic address: miitu.honkanen@uef.fi.
5
Department of Applied Physics, University of Eastern Finland, Finland; Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland. Electronic address: lassi.rieppo@oulu.fi.
6
Department of Applied Physics, University of Eastern Finland, Finland. Electronic address: isaac.afara@uef.fi.
7
Department of Orthopedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland. Electronic address: heikki.kroger@kuh.fi.
8
Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia. Electronic address: juha.toyras@uef.fi.
9
Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. Electronic address: jaakko.sarin@uef.fi.

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.

KEYWORDS:

Arthroscopy; Articular cartilage; Human knee joint; Machine learning; Near-infrared (NIR) spectroscopy; Principal components; Statistical decision making

PMID:
31026649
DOI:
10.1016/j.joca.2019.04.008

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