Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm−1, followed by peak normalization at 850 cm−1 and preprocessing by MSC.publishedVersio

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.Peer reviewe

Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects

OBJECTIVE: To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. METHOD: Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal joints of Shetland ponies and monitored at baseline (0 weeks) and at three follow-up time points (11, 23, and 39 weeks) by measuring near-infrared spectra in vivo at and around the grooves. The animals were sacrificed after 39 weeks and the joints were harvested. Spectra were reacquired ex vivo to ensure reliability of in vivo measurements and for reference analyses. Additionally, cartilage thickness and instantaneous modulus were determined via computed tomography and mechanical testing, respectively. The relationship between the ex vivo spectra and cartilage reference properties was determined using convolutional neural network. RESULTS: For the independent test, the trained networks yielded significant correlations for cartilage thickness (ρ=0.473) and instantaneous modulus (ρ=0.498). These networks were used to predict the reference properties at baseline and follow-ups. In the radiocarpal joint, cartilage thickness increased significantly with both groove types after baseline and remained swollen. Additionally, at 39 weeks, a significant difference was observed in cartilage thickness between controls and sharp grooves. For the instantaneous modulus, significant decrease was observed with both groove types in the radiocarpal joint from baseline to 23 and 39 weeks. CONCLUSION: NIRS combined with machine learning enabled determination of cartilage properties in vivo, thereby providing longitudinal evaluation of post-intervention injury development. Additionally, radiocarpal joints demonstrated more vulnerability to cartilage degeneration after damage than intercarpal joints

Structural, compositional, and functional effects of blunt and sharp cartilage damage on the joint: a 9-month equine groove model study

This study aimed to quantify the long-term progression of blunt and sharp cartilage defects and their effect on joint homeostasis and function of the equine carpus. In nine adult Shetland ponies, the cartilage in the radiocarpal and middle carpal joint of one front limb was grooved (blunt or sharp randomized). The ponies were subjected to an 8-week exercise protocol and sacrificed at 39 weeks. Structural and compositional alterations in joint tissues were evaluated in vivo using serial radiographs, synovial biopsies, and synovial fluid samples. Joint function was monitored by quantitative gait analysis. Macroscopic, microscopic, and biomechanical evaluation of the cartilage, and assessment of subchondral bone parameters were performed ex vivo. Grooved cartilage showed higher OARSI microscopy scores than the contra-lateral sham-operated controls (p <0.0001). Blunt-grooved cartilage scored higher than sharp-grooved cartilage (p = 0.007) and fixed charge density around these grooves was lower (p = 0.006). Equilibrium and instantaneous moduli trended lower in grooved cartilage than their controls (significant for radiocarpal joints). Changes in other tissues included a 3 to 7-fold change in IL-6 expression in synovium from grooved joints at week 23 (p = 0.042) and an increased CPII/C2C ratio in synovial fluid from blunt-grooved joints at week 35 (p = 0.010). Gait analysis outcome revealed mild, gradually increasing lameness. In conclusion, blunt and, to a lesser extent, sharp grooves in combination with a period of moderate exercise, lead to mild degeneration in equine carpal cartilage over a 9-month period, but the effect on overall joint health remains limited. This article is protected by copyright. All rights reserved

Variability of microcirculation detected by blood pulsation imaging.

The non-invasive assessment of blood flow is invaluable for the diagnostic and monitoring treatment of numerous vascular and neurological diseases. We developed a non-invasive and non-contact method of blood pulsation imaging capable of visualizing and monitoring of the two-dimensional distribution of two key parameters of peripheral blood flow: the blood pulsation amplitude and blood pulsation phase. The method is based on the photoplethysmographic imaging in the reflection mode. In contrast with previous imaging systems we use new algorithm for data processing which allows two dimensional mapping of blood pulsations in large object's areas after every cardiac cycle. In our study we carried out the occlusion test of the arm and found (i) the extensive variability of 2D-distribution of blood pulsation amplitude from one cardiac cycle to another, and (ii) existence of the adjacent spots to which the blood is asynchronously supplied. These observations show that the method can be used for studying of the multicomponent regulation of peripheral blood circulation. The proposed technique is technologically simple and cost-effective, which makes it applicable for monitoring the peripheral microcirculation in clinical settings for example, in diagnostics or testing the efficiency of new medicines

Mid-infrared and near infrared spectroscopic analysis of mechanically and enzymatically damaged cartilage

In this study, we demonstrate the potential of mid-infrared (MIR) and near infrared (NIR) spectroscopies to reveal and differentiate between superficial changes in articular cartilage (AC) after mechanical or enzymatic degradation