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

Abstract

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.

Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy

“…In conclusion, NIRS combined with machine learning techniques, could provide a powerful tool for classification of cartilage integrity, with the potential for accurately distinguishing between normal and early osteoarthritic cartilage. This finding, combined with recent application of NIR for estimating cartilage biomechanical properties in human cadaver knee joints, 25,29 is insofar significant as it suggests that NIRS can be adapted for rapid diagnosis of cartilage integrity during knee arthroscopy, where it may be critical to correctly discriminate between healthy and degenerated cartilage prior to removal of the degenerated tissue. However, our approach needs further validation with human samples prior to clinical applications in cartilage/joint repair surgery.…”

T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: assessment of mechanical and structural properties of articular cartilage

“…Depth-wise profiles from the 3-D T2*-and QS-maps were obtained using cylindrical 3-D ROIs of 1-mm diameter and carefully matched with the biomechanical testing points based on mCT measurements and photographs of the samples. 25 Prior to the analysis, these profiles were interpolated into 100 depth-wise points. The profiles were then used to predict reference variables (i.e., equilibrium and dynamic moduli, proteoglycan content, collagen fiber angle and collagen anisotropy) using PLSR.…”

“…Osteochondral samples were obtained from a cartilage repair study involving seven Shetland ponies (N ¼ 7, 6 females and 1 male, Age ¼ 8.8 ± 3.5 years). 25 For the repair study, surgical lesions were induced in the medial trochlear cartilage in both hinds and repaired with a combination of chondrons and mesenchymal stem cells in different carrier hydrogels. The ponies were sacrificed after 1 year.…”

“…1), leading to a total number of 235 biomechanical testing locations (two samples had less than 12 locations). 25 Adjacent points could be measured after each other in quick succession since the utilized indender had a small diameter (~0.5 mm) compared to the distance between the adjacent points (~3e4 mm). 27 Equilibrium and dynamic moduli were determined from the biomechanical testing results.…”

“…24 Our aim was to validate the use of QSM and T2* for prediction of cartilage biomechanical and structural properties using samples from an equine post-traumatic OA study. 25 We hypothesized (1) that cartilage properties can be predicted from depth-wise profiles of quantitative MRI-parameters using partial least squares regression (PLSR) and (2) that this is a more sophisticated way of utilizing MRI data than direct Spearman's rank correlation analysis between bulk values of quantitative MRI and reference parameters.…”