RESUMO
Objective: A prototype infrared attenuated total reflection (IR-ATR) laser spectroscopic system designed for in vivo classification of human cartilage tissue according to its histological health status during arthroscopic surgery is presented. Prior to real-world in vivo applications, this so-called osteoarthritis (OA) scanner has been tested at in vitro conditions revealing the challenges associated with complex sample matrices and the accordingly obtained sparse spectral datasets. Methods: In vitro studies on human knee cartilage samples at different contact pressures (i.e., 0.2-0.5 âMPa) allowed recording cartilage degeneration characteristic IR signatures comparable to in vivo conditions with high temporal resolution. Afterwards, the cartilage samples were assessed based on the clinically acknowledged osteoarthritis cartilage histopathology assessment (OARSI) system and correlated with the obtained sparse IR data. Results: Amide and carbohydrate signal behavior was observed to be almost identical between the obtained sparse IR data and previously measured FTIR data used for sparse partial least squares discriminant analysis (SPLSDA) to identify the spectral regions relevant to cartilage condition. Contact pressures between 0.3 and 0.4 âMPa seem to provide the best sparse IR spectra for cylindrical (d â= â3 âmm) probe tips. Conclusion: Laser-irradiating IR-ATR spectroscopy is a promising analytical technique for future arthroscopic applications to differentiate healthy and osteoarthritic cartilage tissue. However, this study also revealed that the flexible connection between the laser-based analyzer and the arthroscopic ATR-probe via IR-transparent fiberoptic cables may affect the robustness of the obtained IR data and requires further improvements.
RESUMO
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 timepoints (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: In an independent test set, 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 at follow-up time points. 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, a 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 were found more vulnerable to cartilage degeneration after damage than intercarpal joints.