Osteoarthritis (OA) and osteochondral degeneration present a significant clinical burden characterized by the complex interplay of extracellular matrix degradation and chronic inflammation. While biochemical profiling has matured, a critical translational gap remains in transitioning from benchtop assays to systems capable of continuous, intra-articular monitoring. This review provides a comprehensive synthesis of experimentally validated biosensing technologies, including optical, electrochemical, and piezoelectric Quartz Crystal Microbalance (QCM) platforms, evaluated through the lens of sensing architecture, biomarker specificity, and matrix compatibility. Our analysis reveals that while optical sensors offer superior sensitivity, electrochemical platforms show the greatest promise for miniaturized, implantable integration. However, a pivot in the field is identified: the primary bottleneck has shifted from analytical detection limits to operational stability within the hostile synovial environment. Current research is largely restricted to single-analyte detection in simplified media, failing to address the multifactorial nature of OA. We propose that the next generation of osteochondral diagnostics must prioritize multiplexed arrays, mechanically compliant architectures, and machine-learning-assisted signal processing. By bridging these engineering frontiers, biosensors will evolve from passive diagnostic tools into intelligent, personalized platforms for real-time disease management.
Olayiwola et al. (Thu,) studied this question.