Proof-of-concept for the detection of early osteoarthritis pathology by clinically applicable endomicroscopy and quantitative AI-supported optical biopsy

Objective: Clinical trials for osteoarthritis (OA), the leading cause of global disability, are unable to pinpoint the early, potentially reversible disease with clinical technology. Hence, disease-modifying drug candidates cannot be tested early in the disease. To overcome this obstacle, we asked whether early OA pathology detection is possible with current clinical technology. Methods: We determined the relationship between two sensitive early OA markers, atomic force microscopy (AFM)-measured human articular cartilage (AC) surface stiffness, and location-matched superficial zone chondrocyte spatial organizations (SCSOs), asking whether a significant loss of surface stiffness can be detected in early OA SCSO stages. We then tested whether current clinical technology can visualize and accurately diagnose the SCSOs using an approved probe-based confocal laser-endomicroscope and a random forest (RF) model. Results: We demonstrated a correlation between AC surface stiffness and the SCSO (r(rm) = -0.91; 95% CI: -0.97, -0.73), and an extensive loss of surface stiffness specifically in those ACs with early OA-typical SCSO (95%CIs: string SCSO: 269-173 kPa, double string SCSO: 77-46 kPa). This established the SCSO as a visualizable, functionally relevant surrogate marker of early OA AC surface pathology. Moreover, SCSO-based stiffness discrimination worked well in each patient's AC. We then demonstrated feasibility of visualizing the SCSO by clinical laser-endomicroscopy and, importantly, accurate SCSO diagnosis using RF. Conclusion: We present the proof-of-concept of early OA-pathology detection with available clinical technology, introducing a future-oriented, AI-supported, non-destructive quantitative optical biopsy for early disease detection. Operationalizing SCSO recognition, this approach allows testing for correlations between local tissue architectures with other experimental and clinical read-outs, but needs clinical validation and a larger sample size for defining diagnostic thresholds. (c) 2020 The Author(s). Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International.