Near-Infrared Spectroscopy for Mapping of Human Meniscus Biochemical Constituents.

Degenerative changes in meniscus are diagnosed during surgery by means of mechanical testing and visual evaluation. This method is qualitative and highly subjective, providing very little information on the internal state of the meniscus. Thus, there is need for novel quantitative methods that can support decision-making during arthroscopic surgery. In this study, we investigate the potential of near-infrared spectroscopy (NIRS) for mapping the biochemical constituents of human meniscus, including water, uronic acid, and hydroxyproline contents. Partial least squares regression models were developed using data from 115 measurement locations of menisci samples extracted from 7 cadavers and 11 surgery patient donors. Model performance was evaluated using an independent test set consisting of 55 measurement locations within a meniscus sample obtained from a separate cadaver. The correlation coefficient of calibration (ρtraining), test set (ρtest), and root-mean-squared error of test set (RMSEP) were as follows: water (ρtraining = 0.61, ρtest = 0.39, and RMSEP = 2.27 percentage points), uronic acid (ρtraining = 0.68, ρtest = 0.69, and RMSEP = 6.09 basis points), and hydroxyproline (ρtraining = 0.84, ρtest = 0.58, and error = 0.54 percentage points). In conclusion, the results suggest that NIRS could enable rapid arthroscopic mapping of changes in meniscus biochemical constituents, thus providing means for quantitative assessment of meniscus degeneration.

Optical spectroscopic characterization of human meniscus biomechanical properties.

This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development (83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eϵ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery.

Optical spectroscopic determination of human meniscus composition.

This study investigates the correlation between the composition of human meniscus and its absorption spectrum in the visible (VIS) and near infrared (NIR) spectral range. Meniscus samples (n = 24) were obtained from nonarthritic knees of human cadavers with no history of joint diseases. Specimens (n = 72) were obtained from three distinct sections of the meniscus, namely; anterior, center, posterior. Absorption spectra were acquired from each specimen in the VIS and NIR spectral range (400-1,100 nm). Following spectroscopic probing, the specimens were subjected to biochemical analyses to determine the matrix composition, that is water, hydroxyproline, and uronic acid contents. Multivariate analytical techniques, including principal component analysis (PCA) and partial least squares (PLS) regression, were then used to investigate the correlation between the matrix composition and it spectral response. Our results indicate that the optical absorption of meniscus matrix is related to its composition, and this relationship is optimal in the NIR spectral range (750-1,100 nm). High correlations (R(2) (uronic) = 86.9%, R(2) (water) = 83.8%, R(2) (hydroxyproline) = 81.7%, p < 0.0001) were obtained between the spectral predicted and measured meniscus composition, thus suggesting that spectral data in the NIR range can be utilized for estimating the matrix composition of human meniscus. In conclusion, optical spectroscopy, particularly in the NIR spectral range, is a potential method for evaluating the composition of human meniscus. This presents a promising technique for rapid and nondestructive evaluation of meniscus integrity in real-time during arthroscopic surgery.