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.
Highlights
Osteoarthritis (OA) is a disabling disease associated with joint pain and restricted mobility, especially in the elderly[1,2]
This highlights the need for novel quantitative arthroscopic techniques, such as ultrasound[11], optical coherence tomography (OCT12), and near infrared spectroscopy (NIRS13)
Arthroscopic and in vitro NIRS measurements were conducted on tissue surrounding experimental cartilage repair sites in equine joints at the 12-month end-point and compared with unaffected tissue harvested from matching sites in healthy control ponies
Summary
Osteoarthritis (OA) is a disabling disease associated with joint pain and restricted mobility, especially in the elderly[1,2]. Robust and effective analytical techniques, such as neural networks, could enable more accurate and reliable estimation of cartilage properties from the near infrared (NIR) spectral data. Artificial neural networks (ANN) combined with variable selection methods have recently been introduced for analysis of cartilage spectral data[22,29,30] and have shown potential for evaluation of tissue properties. We hypothesize that arthroscopic NIRS enables reliable simultaneous evaluation of articular cartilage and subchondral bone in vivo via adaptation of ANN. To test this hypothesis, arthroscopic and in vitro NIRS measurements were conducted on tissue surrounding experimental cartilage repair sites in equine joints at the 12-month end-point and compared with unaffected tissue harvested from matching sites in healthy control ponies. To investigate the relationship between the spectral data and reference parameters, ANN with forward variable selection technique was adapted
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