Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.

In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen and proteoglycans, mimicking an osteoarthritis condition in ex-vivo bovine cartilage samples. Most analyzed metrics showed significant changes as the degradation progressed, especially with collagenase treatment. We propose for the first time a combination of these ultrasound parameters through machine learning models aimed at automatically identifying healthy and degraded cartilage samples. The random forest model showed good performance in distinguishing healthy cartilage from trypsin-treated samples, with an accuracy of 60%. The support vector machine demonstrated excellent accuracy (96%) in differentiating healthy cartilage from collagenase-degraded samples. Histological and mechanical analyses further confirmed these findings, with collagenase having a more pronounced impact on both mechanical and histological properties, compared to trypsin. These metrics were obtained using an ultrasound probe having a transmission frequency of 15 MHz, typically used for the diagnosis of musculoskeletal diseases, enabling a fully non-invasive procedure without requiring arthroscopic probes. As a perspective, the proposed quantitative ultrasound assessment has the potential to become a new standard for monitoring cartilage health, enabling the early detection of cartilage pathologies and timely interventions.