Assessment of degenerative states of articular cartilage via sparse laser-based mid-infrared attenuated total reflectance spectroscopy.

Objective: To investigate capability of sparse mid-infrared spectral data obtained by a prototype infrared attenuated total reflection (IR-ATR) laser spectroscopic system to classify human cartilage degenerative state. Methods: Sparse IR spectra of human knee cartilage samples acquired at different contact pressures (i.e., 0.2-0.5 MPa) were analyzed using a variety of chemometric methods (i.e., principal component analysis, balanced random forest, multiple logistic regression). Several data analyses strategies were tested considering different measurement phases (i.e., probe approach vs. sample contact) or cartilage loading i.e., compression vs. relaxation, and relevant spectral characteristics i.e., absorbance or/and slope parameters. In addition, confounding factors were identified ensuring an optimized correlation of sparse IR data with histological evaluation based on osteoarthritis cartilage histopathology assessment system (OARSI grading). Results: Classification model results using balanced random forest and multiple logistic regression classifier for binary classification of cartilage tissue into healthy (OARSI ≤2) and damaged (OARSI >2) cartilage tissue samples achieved area under the curve (AUC) values of up to 0.86%. Models that combine absorption and slope parameters derived from the fin-shaped measurement signals result in reliable classifications. Conclusions: Laser-based IR-ATR spectroscopy is a promising analytical technique for future arthroscopic applications facilitating the differentiation between healthy and osteoarthritic cartilage tissue. Acquisition of sparse IR spectra could allow reliable diagnostics if the optical probe is integrated with miniaturized pressure transducers in an arthroscopic probe.