## 6 Alternative Methods of Articular Cartilage Diagnosis
### Numerical Methods and Artificial Intelligence
[371], a vibroarthography method was used to analyse the vibration of the knee joint and classify the AC condition using artificial neural networks, such as the Multilayer Perceptron (MLP) and Radial Basis Function Network (RBF). By applying feature selection using neighbourhood component analysis (NCA), a classification efficiency of >95% was achieved. The use of vibroacoustic methods provides information complementary to structural imaging. In particular, insights gained from FEA-based biomechanical models can provide input for generating or interpreting a synthetic signal. The combination of numerical methods and AI makes it possible to create clinical decision support systems (CDSS) using both imaging and clinical and biomechanical data to recommend therapies, as well as predict OA progression [372]. Such systems can benefit from the synthetic data that are generated by numerical models. The benefit of this approach is to streamline the training of AI tools for limited clinical data. In the context of tissue engineering, the use of FEA models supports the design of scaffolds and the simulation of biomaterial-tissue interactions, while AI finds application in the generative design of microstructures with optimal mechanical as well as biological properties [373]. Thus, the integration of numerical methods and AI contributes to new perspectives for more effective diagnosis of AC diseases. The combination of these technologies with modern imaging techniques, biomechanical analysis and tissue engineering approaches offers the potential to develop diagnostic tools with improved accuracy and precision, reproducibility and predictive ability, which may be reflected in improved quality and efficacy of therapy. Despite the promising nature of advanced diagnostic techniques for articular cartilage assessment, each modality possesses specific limitations that constrain its clinical or research utility. For instance, Optical Coherence Tomography (OCT), while offering near-histological resolution, is limited in in vivo applications by shallow penetration depth and the need for intra-articular access. Similarly, Magnetic Resonance Elastography (MRE) and T1\(\rho\) or T2 mapping require specialised sequences and long acquisition times, which may reduce their feasibility in routine practice. Raman spectroscopy, despite its potential for molecular analysis, suffers from high sensitivity to motion artefacts and signal attenuation in deeper tissues. A concise overview of the key advantages and limitations of each modality is presented in Table 4 to assist clinicians and researchers in selecting the appropriate diagnostic tool depending on the clinical scenario or research focus. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Modality** & **Main Strengths** & **Key Limitations** \\ \hline \multirow{4}{*}{MRI} & High-resolution soft tissue imaging; & Limited sensitivity to early \\  & good for cartilage & biochemical changes; \\  & morphology; & long scan times; \\  & non-invasive & expensive \\ \hline \multirow{4}{*}{CT-Arthrography} & Excellent surface detail; & Radiation exposure; \\  & useful in patients with MRI & requires contrast agent; \\  & contraindications & not suitable for repeated use \\ \hline \multirow{4}{*}{Ultrasound} & Low cost; & Low spatial resolution; \\  & widely available; & operator dependent; \\  & real-time assessment & limited to surface evaluation \\ \hline \multirow{4}{*}{MRE} & Quantitative stiffness & Specialised sequences \\  & mapping; & required; \\ \cline{1-1}  & non-invasive biomechanical insight & limited availability; \\ \cline{1-1}  & long acquisition times \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advantages and limitations of alternative methods of articular cartilage diagnosis. Given the growing number of available diagnostic techniques--both conventional and emerging--it is important to systematise their key characteristics in a comparative manner.