Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity.

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Visible and Near-Infrared Spectroscopy Enables Differentiation of Normal and Early Osteoarthritic Human Knee Joint Articular Cartilage.

Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0-1) from general osteoarthritic cartilage (OARSI: 2-5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2-3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400-600 nm), collagen content (1000-1300 nm) and proteoglycan content (1600-1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.

Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties.

OBJECTIVE: To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN: Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. RESULTS: The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). CONCLUSION: RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.

Nondestructive Evaluation of Mechanical and Histological Properties of the Human Aorta With Near-Infrared Spectroscopy.

INTRODUCTION: Ascending aortic dilatation is a well-known risk factor for aortic rupture. Indications for aortic replacement in its dilatation concomitant to other open-heart surgery exist; however, cut-off values based solely on aortic diameter may fail to identify patients with weakened aortic tissue. We introduce near-infrared spectroscopy (NIRS) as a diagnostic tool to nondestructively evaluate the structural and compositional properties of the human ascending aorta during open-heart surgeries. During open-heart surgery, NIRS could provide information regarding tissue viability in situ and thus contribute to the decision of optimal surgical repair. MATERIALS AND METHODS: Samples were collected from patients with ascending aortic aneurysm (n = 23) undergoing elective aortic reconstruction surgery and from healthy subjects (n = 4). The samples were subjected to spectroscopic measurements, biomechanical testing, and histological analysis. The relationship between the near-infrared spectra and biomechanical and histological properties was investigated by adapting partial least squares regression. RESULTS: Moderate prediction performance was achieved with biomechanical properties (r = 0.681, normalized root-mean-square error of cross-validation = 17.9%) and histological properties (r = 0.602, normalized root-mean-square error of cross-validation = 22.2%). Especially the performance with parameters describing the aorta's ultimate strength, for example, failure strain (r = 0.658), and elasticity (phase difference, r = 0.875) were promising and could, therefore, provide quantitative information on the rupture sensitivity of the aorta. For the estimation of histological properties, the results with α-smooth muscle actin (r = 0.581), elastin density (r = 0.973), mucoid extracellular matrix accumulation(r = 0.708), and media thickness (r = 0.866) were promising. CONCLUSIONS: NIRS could be a potential technique for in situ evaluation of biomechanical and histological properties of human aorta and therefore useful in patient-specific treatment planning.

Near-Infrared Spectroscopy Enables Arthroscopic Histologic Grading of Human Knee Articular Cartilage.

Purpose: To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS). Methods: In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (ρ) and root-mean-square error (RMSE). Results: The PCA-LME models performed well (independent test: average ρ = 0.668, RMSE = 0.892, P < .001) in the prediction of the reference measurements based on in vitro data. The performance on ex vivo arthroscopic data was poorer but improved substantially after outlier exclusion (independent test: average ρ = 0.462 to 0.614, RMSE = 1.078 to 0.950, P = .019 to .008). Conclusions: NIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy. Clinical Relevance: There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.

Wall Shear Stress Predicts Media Degeneration and Biomechanical Changes in Thoracic Aorta.

Objectives: In thoracic aortic aneurysm (TAA) of the ascending aorta (AA), AA is progressively dilating due to the weakening of the aortic wall. Predicting and preventing aortic dissections and ruptures in TAA continues to be challenging, and more accurate assessment of the AA dilatation, identification of high-risk patients, and timing of repair surgery are required. We investigated whether wall shear stress (WSS) predicts pathological and biomechanical changes in the aortic wall in TAA. Methods: The study included 12 patients with bicuspid (BAV) and 20 patients with the tricuspid aortic valve (TAV). 4D flow magnetic resonance imaging (MRI) was performed a day before aortic replacement surgery. Biomechanical and histological parameters, including assessing of wall strength, media degeneration, elastin, and cell content were analyzed from the resected AA samples. Results: WSSs were greater in the outer curves of the AA compared to the inner curves in all TAA patients. WSSs correlated with media degeneration of the aortic wall (ρ = -0.48, p < 0.01), elastin content (ρ = 0.47, p < 0.01), and aortic wall strength (ρ = -0.49, p = 0.029). Subsequently, the media of the outer curves was thinner, more rigid, and tolerated lower failure strains. Failure values were shown to correlate with smooth muscle cell (SMC) density (ρ = -0.45, p < 0.02), and indicated the more MYH10+ SMCs the lower the strength of the aortic wall structure. More macrophages were detected in patients with severe media degeneration and the areas with lower WSSs. Conclusion: The findings indicate that MRI-derived WSS predicts pathological and biomechanical changes in the aortic wall in patients with TAA and could be used for identification of high-risk patients.

Near infrared spectroscopic evaluation of biochemical and crimp properties of knee joint ligaments and patellar tendon.

Knee ligaments and tendons play an important role in stabilizing and controlling the motions of the knee. Injuries to the ligaments can lead to abnormal mechanical loading of the other supporting tissues (e.g., cartilage and meniscus) and even osteoarthritis. While the condition of knee ligaments can be examined during arthroscopic repair procedures, the arthroscopic evaluation suffers from subjectivity and poor repeatability. Near infrared spectroscopy (NIRS) is capable of non-destructively quantifying the composition and structure of collagen-rich connective tissues, such as articular cartilage and meniscus. Despite the similarities, NIRS-based evaluation of ligament composition has not been previously attempted. In this study, ligaments and patellar tendon of ten bovine stifle joints were measured with NIRS, followed by chemical and histological reference analysis. The relationship between the reference properties of the tissue and NIR spectra was investigated using partial least squares regression. NIRS was found to be sensitive towards the water (R2CV = .65) and collagen (R2CV = .57) contents, while elastin, proteoglycans, and the internal crimp structure remained undetectable. As collagen largely determines the mechanical response of ligaments, we conclude that NIRS demonstrates potential for quantitative evaluation of knee ligaments.

Dual-contrast computed tomography enables detection of equine posttraumatic osteoarthritis in vitro.

To prevent the progression of posttraumatic osteoarthritis, assessment of cartilage composition is critical for effective treatment planning. Posttraumatic changes include proteoglycan (PG) loss and elevated water content. Quantitative dual-energy computed tomography (QDECT) provides a means to diagnose these changes. Here, we determine the potential of QDECT to evaluate tissue quality surrounding cartilage lesions in an equine model, hypothesizing that QDECT allows detection of posttraumatic degeneration by providing quantitative information on PG and water contents based on the partitions of cationic and nonionic agents in a contrast mixture. Posttraumatic osteoarthritic samples were obtained from a cartilage repair study in which full-thickness chondral defects were created surgically in both stifles of seven Shetland ponies. Control samples were collected from three nonoperated ponies. The experimental (n = 14) and control samples (n = 6) were immersed in the contrast agent mixture and the distributions of the agents were determined at various diffusion time points. As a reference, equilibrium moduli, dynamic moduli, and PG content were measured. Significant differences (p < 0.05) in partitions between the experimental and control samples were demonstrated with cationic contrast agent at 30 min, 60 min, and 20 h, and with non-ionic agent at 60 and 120 min. Significant Spearman's rank correlations were obtained at 20 and 24 h (ρ = 0.482-0.693) between the partition of cationic contrast agent, cartilage biomechanical properties, and PG content. QDECT enables evaluation of posttraumatic changes surrounding a lesion and quantification of PG content, thus advancing the diagnostics of the extent and severity of cartilage injuries.

Characterization of connective tissues using near-infrared spectroscopy and imaging.

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis.

Chondral lesions lead to degenerative changes in the surrounding cartilage tissue, increasing the risk of developing post-traumatic osteoarthritis (PTOA). This study aimed to investigate the feasibility of quantitative magnetic resonance imaging (qMRI) for evaluation of articular cartilage in PTOA. Articular explants containing surgically induced and repaired chondral lesions were obtained from the stifle joints of seven Shetland ponies (14 samples). Three age-matched nonoperated ponies served as controls (six samples). The samples were imaged at 9.4 T. The measured qMRI parameters included T1 , T2 , continuous-wave T1ρ (CWT1ρ ), adiabatic T1ρ (AdT1ρ ), and T2ρ (AdT2ρ ) and relaxation along a fictitious field (TRAFF ). For reference, cartilage equilibrium and dynamic moduli, proteoglycan content and collagen fiber orientation were determined. Mean values and profiles from full-thickness cartilage regions of interest, at increasing distances from the lesions, were used to compare experimental against control and to correlate qMRI with the references. Significant alterations were detected by qMRI parameters, including prolonged T1 , CWT1ρ , and AdT1ρ in the regions adjacent to the lesions. The changes were confirmed by the reference methods. CWT1ρ was more strongly associated with the reference measurements and prolonged in the affected regions at lower spin-locking amplitudes. Moderate to strong correlations were found between all qMRI parameters and the reference parameters (ρ = -0.531 to -0.757). T1 , low spin-lock amplitude CWT1ρ , and AdT1ρ were most responsive to changes in visually intact cartilage adjacent to the lesions. In the context of PTOA, these findings highlight the potential of T1 , CWT1ρ , and AdT1ρ in evaluation of compositional and structural changes in cartilage.

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy.

INTRODUCTION: Assessment of cartilage integrity during arthroscopy is limited by the subjective visual nature of the technique. To address this shortcoming in diagnostic evaluation of articular cartilage, near infrared spectroscopy (NIRS) has been proposed. In this study, we evaluated the capacity of NIRS, combined with machine learning techniques, to classify cartilage integrity. METHODS: Rabbit (n = 14) knee joints with artificial injury, induced via unilateral anterior cruciate ligament transection (ACLT), and the corresponding contra-lateral (CL) joints, including joints from separate non-operated control (CNTRL) animals (n = 8), were used. After sacrifice, NIR spectra (1000-2500 nm) were acquired from different anatomical locations of the joints (n TOTAL  = 313: n CNTRL = 111, n CL = 97, n ACLT = 105). Machine and deep learning methods (support vector machines-SVM, logistic regression-LR, and deep neural networks-DNN) were then used to develop models for classifying the samples based solely on their NIR spectra. RESULTS: The results show that the model based on SVM is optimal of distinguishing between ACLT and CNTRL samples (ROC_AUC = 0.93, kappa = 0.86), LR is capable of distinguishing between CL and CNTRL samples (ROC_AUC = 0.91, kappa = 0.81), while DNN is optimal for discriminating between the different classes (multi-class classification, kappa = 0.48). CONCLUSION: We show that NIR spectroscopy, when combined with machine learning techniques, is capable of holistic assessment of cartilage integrity, with potential for accurately distinguishing between healthy and diseased cartilage.

Open-source python module for automated preprocessing of near infrared spectroscopic data.

Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral region are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets.

Sequential Collinear Photofragmentation and Atomic Absorption Spectroscopy for Online Laser Monitoring of Triatomic Metal Species.

Industrial chemical processes are struggling with adverse effects, such as corrosion and deposition, caused by gaseous alkali and heavy metal species. Mitigation of these problems requires novel monitoring concepts that provide information on gas-phase chemistry. However, selective optical online monitoring of the most problematic diatomic and triatomic species is challenging due to overlapping spectral features. In this work, a selective, all-optical, in situ gas-phase monitoring technique for triatomic molecules containing metallic atoms was developed and demonstrated with detection of PbCl2. Sequential collinear photofragmentation and atomic absorption spectroscopy (CPFAAS) enables determination of the triatomic PbCl2 concentration through detection of released Pb atoms after two consecutive photofragmentation processes. Absorption cross-sections of PbCl2, PbCl, and Pb were determined experimentally in a laboratory-scale reactor to enable calibration-free quantitative determination of the precursor molecule concentration in an arbitrary environment. Limit of detection for PbCl2 in the laboratory reactor was determined to be 0.25 ppm. Furthermore, the method was introduced for in situ monitoring of PbCl2 concentration in a 120 MWth power plant using demolition wood as its main fuel. In addition to industrial applications, the method can provide information on chemical reaction kinetics of the intermediate species that can be utilized in reaction simulations.

Dataset on equine cartilage near infrared spectra, composition, and functional properties.

Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.

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

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: ρin vitro, Val= 0.691, ρin vivo= 0.676; collagen orientation angle: ρin vitro, Val= 0.626, ρin vivo= 0.574) from NIR spectral data. In addition, the networks enabled reliable prediction of PG content in superficial (25%) cartilage (ρin vitro, Val= 0.650, ρin vivo= 0.613) and cartilage thickness (ρin vitro, Val= 0.797, ρin 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.

Near Infrared Spectroscopic Evaluation of Ligament and Tendon Biomechanical Properties.

Knee ligaments and tendons are collagen-rich viscoelastic connective tissues that provide vital mechanical stabilization and support to the knee joint. Deterioration of ligaments has an adverse effect on the health of the knee and can eventually lead to ligament rupture and osteoarthritis. In this study, the feasibility of near infrared spectroscopy (NIRS) was, for the first time, tested for evaluation of ligament and tendon mechanical properties by performing measurements on bovine stifle joint ligament (N = 40) and patellar tendon (N = 10) samples. The mechanical properties of the samples were determined using a uniaxial tensile testing protocol. Partial least squares regression models were then developed to determine if morphological, viscoelastic, and quasi-static properties of the samples could be predicted from the NIR spectra. Best performance of NIRS in predicting mechanical properties was observed for toughness at yield point (median [Formula: see text], median normalized [Formula: see text]), toughness at failure point (median [Formula: see text], median normalized [Formula: see text]), and the ultimate strength of the ligament/tendon (median [Formula: see text], median normalized [Formula: see text]). Thus, we show that NIRS is capable of estimating ligament and tendon biomechanical properties, especially in parameters related to tissue failure. We believe this method could substantially enhance the currently limited arthroscopic evaluation of ligaments and tendons.

Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo.

Arthroscopic assessment of articular tissues is highly subjective and poorly reproducible. To ensure optimal patient care, quantitative techniques (e.g., near infrared spectroscopy (NIRS)) could substantially enhance arthroscopic diagnosis of initial signs of post-traumatic osteoarthritis (PTOA). Here, we demonstrate, for the first time, the potential of arthroscopic NIRS to simultaneously monitor progressive degeneration of cartilage and subchondral bone in vivo in Shetland ponies undergoing different experimental cartilage repair procedures. Osteochondral tissues adjacent to the repair sites were evaluated using an arthroscopic NIRS probe and significant (p < 0.05) degenerative changes were observed in the tissue properties when compared with tissues from healthy joints. Artificial neural networks (ANN) enabled reliable (ρ = 0.63-0.87, NMRSE = 8.5-17.2%, RPIQ = 1.93-3.03) estimation of articular cartilage biomechanical properties, subchondral bone plate thickness and bone mineral density (BMD), and subchondral trabecular bone thickness, bone volume fraction (BV), BMD, and structure model index (SMI) from in vitro spectral data. The trained ANNs also reliably predicted the properties of an independent in vitro test group (ρ = 0.54-0.91, NMRSE = 5.9-17.6%, RPIQ = 1.68-3.36). However, predictions based on arthroscopic NIR spectra were less reliable (ρ = 0.27-0.74, NMRSE = 14.5-24.0%, RPIQ = 1.35-1.70), possibly due to errors introduced during arthroscopic spectral acquisition. Adaptation of NIRS could address the limitations of conventional arthroscopy through quantitative assessment of lesion severity and extent, thereby enhancing detection of initial signs of PTOA. This would be of high clinical significance, for example, when conducting orthopaedic repair surgeries.

Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure.

Conventional arthroscopic evaluation of articular cartilage is subjective and poorly reproducible. Therefore, implementation of quantitative diagnostic techniques, such as near infrared spectroscopy (NIRS) and optical coherence tomography (OCT), is essential. Locations (n = 44) with various cartilage conditions were selected from mature equine fetlock joints (n = 5). These locations and their surroundings were measured with NIRS and OCT (n = 530). As a reference, cartilage proteoglycan (PG) and collagen contents, and collagen network organization were determined using quantitative microscopy. Additionally, lesion severity visualized in OCT images was graded with an automatic algorithm according to International Cartilage Research Society (ICRS) scoring system. Artificial neural network with variable selection was then employed to predict cartilage composition in the superficial and deep zones from NIRS data, and the performance of two models, generalized (including all samples) and condition-specific models (based on ICRS-grades), was compared. Spectral data correlated significantly (p < 0.002) with PG and collagen contents, and collagen orientation in the superficial and deep zones. The combination of NIRS and OCT provided the most reliable outcome, with condition-specific models having lower prediction errors (9.2%) compared to generalized models (10.4%). Therefore, the results highlight the potential of combining both modalities for comprehensive evaluation of cartilage during arthroscopy.

Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage.

Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R2Tissue thickness = 75.6%, R2Dynamic modulus = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.

Near Infrared Spectroscopic Mapping of Functional Properties of Equine Articular Cartilage.

Mechanical properties of articular cartilage are vital for normal joint function, which can be severely compromised by injuries. Quantitative characterization of cartilage injuries, and evaluation of cartilage stiffness and thickness by means of conventional arthroscopy is poorly reproducible or impossible. In this study, we demonstrate the potential of near infrared (NIR) spectroscopy for predicting and mapping the functional properties of equine articular cartilage at and around lesion sites. Lesion and non-lesion areas of interests (AI, N = 44) of equine joints (N = 5) were divided into grids and NIR spectra were acquired from all grid points (N = 869). Partial least squares (PLS) regression was used to investigate the correlation between the absorbance spectra and thickness, equilibrium modulus, dynamic modulus, and instantaneous modulus at the grid points of 41 AIs. Subsequently, the developed PLS models were validated with spectral data from the grid points of 3 independent AIs. Significant correlations were obtained between spectral data and cartilage thickness (R 2 = 70.3%, p < 0.0001), equilibrium modulus (R 2 = 67.8%, p < 0.0001), dynamic modulus (R 2 = 68.9%, p < 0.0001) and instantaneous modulus (R 2 = 41.8%, p < 0.0001). Relatively low errors were observed in the predicted thickness (5.9%) and instantaneous modulus (9.0%) maps. Thus, if well implemented, NIR spectroscopy could enable arthroscopic evaluation and mapping of cartilage functional properties at and around lesion sites.