Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy. Issue 8 (August 2019)

• Record Type: Journal Article
• Authors: Prakash, M.; Joukainen, A.; Torniainen, J.; Honkanen, M.K.M.; Rieppo, L.; Afara, I.O.; Kröger, H.; Töyräs, J.; Sarin, J.K.
• Is Part Of: Osteoarthritis and cartilage. Volume 27:Issue 8(2019)
• Page Start: 1235
• Page End: 1243
• Publication Date: 2019-08
• Subjects: Articular cartilage -- Near-infrared (NIR) spectroscopy -- Human knee joint -- Principal components -- Arthroscopy -- Statistical decision making -- Machine learning; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; Arthrose -- Périodiques; Articulations -- Maladies -- Périodiques; 616.7223005
• DOI: 10.1016/j.joca.2019.04.008
• Languages: English
• ISSNs: 1063-4584

Abstract

Summary: 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.