Grading cartilage damage with diffuse reflectance spectroscopy: Optical markers and mechanical properties.

Osteoarthritis (OA) is one of the most common joint diseases worldwide. Unfortunately, clinical methods lack the ability to detect OA in the early stages. Timely detection of the knee joint degradation at the level of tissue changes can prevent its progressive damage. Here, diffuse reflectance spectroscopy (DRS) in the NIR range was used to obtain optical markers of the cartilage damage grades and to assess its mechanical properties. It was observed that the water content obtained by DRS strongly correlates with the cartilage thickness (R = .82) and viscoelastic relaxation time (R = .7). Moreover, the spectral parameters, including water content (OH-band), protein content (CH-band), and scattering parameters allowed for discrimination between the cartilage damage grades (10-4 < P ≤ 10-3 ). The developed approach may become a valuable addition to arthroscopy, helping to identify lesions at the microscopic level in the early stages of OA and complement the surgical analysis.

Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon?

PURPOSE: This study aims to describe and assess the current stage of the artificial intelligence (AI) technology integration in preventive orthopaedics of the knee and hip joints. MATERIALS AND METHODS: The study was conducted in strict compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Literature databases were searched for articles describing the development and validation of AI models aimed at diagnosing knee or hip joint pathologies or predicting their development or course in patients. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and QUADAS-AI tools. RESULTS: 56 articles were found that meet all the inclusion criteria. We identified two problems that block the full integration of AI into the routine of an orthopaedic physician. The first of them is related to the insufficient amount, variety and quality of data for training, and validation and testing of AI models. The second problem is the rarity of rational evaluation of models, which is why their real quality cannot always be evaluated. CONCLUSION: The vastness and relevance of the studied topic are beyond doubt. Qualitative and optimally validated models exist in all four scopes considered. Additional optimization and confirmation of the models' quality on various datasets are the last technical stumbling blocks for creating usable software and integrating them into the routine of an orthopaedic physician.