Arthroscopic Determination of Cartilage Proteoglycan Content and Collagen Network Structure with Near-Infrared Spectroscopy

Abstract

Conventional arthroscopic evaluation of articular cartilage is subjective and insufficient for assessing early compositional and structural changes during the progression of post-traumatic osteoarthritis. Therefore, in this study, arthroscopic near-infrared (NIR) spectroscopy is introduced, for the first time, for in vivo evaluation of articular cartilage thickness, proteoglycan (PG) content, and collagen orientation angle. NIR spectra were acquired in vivo and in vitro from equine cartilage adjacent to experimental cartilage repair sites. As reference, digital densitometry and polarized light microscopy were used to evaluate superficial and full-thickness PG content and collagen orientation angle. To relate NIR spectra and cartilage properties, ensemble neural networks, each with two different architectures, were trained and evaluated by using Spearman’s correlation analysis ( ρ ). The ensemble networks enabled accurate predictions for full-thickness reference properties (PG content: ρ in vitro, Val = 0.691, ρ in vivo = 0.676; collagen orientation angle: ρ in vitro, Val = 0.626, ρ in vivo = 0.574) from NIR spectral data. In addition, the networks enabled reliable prediction of PG content in superficial (25%) cartilage ( ρ in vitro, Val = 0.650, ρ in vivo = 0.613) and cartilage thickness ( ρ in vitro, Val = 0.797, ρ in vivo = 0.596). To conclude, NIR spectroscopy could enhance the detection of initial cartilage degeneration and thus enable demarcation of the boundary between healthy and compromised cartilage tissue during arthroscopic surgery.

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy

“…We quantified the potential of NIRS to distinguish between cartilage of control and experimental joints, simulating differences between healthy and degenerated cartilage. Although NIRS has been extensively applied for predicting cartilage biochemical 1,22,30 and structural 28 properties, which are the basis of its function, these properties would need to be measured for each location independently because of the site-specific variation in cartilage properties. Furthermore, the functional properties of articular cartilage, alone, are not reliable indicators of tissue integrity.…”

“…This is supported by the observation of Brown et al, 10 who showed significant overlap in the stiffness of normal and degenerated cartilage due to site-specific variations in articular cartilage structure, functional and material properties. More so, besides regression analysis for prediction of cartilage properties, 28,30 it is worth noting that no study has applied machine learning techniques for classification of cartilage integrity based on non-destructive spectroscopic methods like NIR spectroscopy, which has immense diagnostic potential.…”

“…As such, the main changes observed in samples with early matrix alteration are related to changes in PG content, which influences the water content and potentially the superficial collagen structure, all of which are detectable using NIRS. 1,2,22,30 This result suggests that cartilage degeneration, and thus changes in its spectral features, may not follow a linear trend, and therefore may benefit from a non-linear modelling approach such as neural networks, including shallow and deep networks.…”

“…Analysis of NIR spectra for assessment of cartilage integrity has so far relied on traditional multivariate linear methods, particularly partial least squares regression (PLSR), e.g., for prediction of specific tissue properties from cartilage NIR spectrum. Only few studies have applied machine learning techniques, such as support vector machines (SVM) 3 and neural networks (ANN), 28,30 for analysis of cartilage NIR spectral data, specifically regression analysis. However, no study has applied machine learning techniques for classification of connective tissue integrity based on NIRS.…”

Near Infrared Spectroscopy Enables Differentiation of Mechanically and Enzymatically Induced Cartilage Injuries

“…5 Furthermore, NIR spectroscopy has been shown to be a versatile technique for arthroscopic evaluation of connective tissue integrity during surgery. This was first demonstrated by Spahn et al 38 for evaluation of human cartilage, and more recently with more accurate results by Sarin et al 32 and Prakash et al 27 using a prototype NIR fiber optic arthroscopic probe for estimating the integrity of equine and human cartilage, respectively. Problems associated with NIR spectroscopy, including overlapping spectral bands, can be overcome by using multivariate data analysis techniques, such as principal component analysis (PCA), 2 partial least square regression (PLSR) 28 and partial least squares discriminant analysis (PLS-DA), coupled with spectral preprocessing.…”