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% Le R2 ≤ 90.8%).e. 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%), E (9.6%), E (8.4%), and M (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

Near-infrared spectroscopy for mapping of human meniscus biochemical constituents

Abstract 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