'Spectromics': Holistic Optical Assessment of Human Cartilage via Complementary Vibrational Spectroscopy for Osteoarthritis Diagnosis

Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK. 1 There is an unmet need for patient friendly paradigms for clinical assessment that do not require ionising radiation (CT), exogenous contrast enhancing dyes (MRI), biopsy, and/or instrumentation approaches (arthroscopy or endoscopy). Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal providing for non-invasive, label-free and deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. Samples were excised from femoral heads post hip arthroplasty from OA patients (n=13) and age-matched control (osteoporosis) patients (n=14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes, and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, discriminatory spectral features in the enhanced fingerprint elucidated clinically relevant tissue components (OA biomarkers). In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic.