## 6 Alternative Methods of Articular Cartilage Diagnosis
### Numerical Methods and Artificial Intelligence  
Numerical methods and Artificial Intelligence (AI) are becoming the answer to the increasing demand for early, non-invasive and accurate diagnosis [359,360]. The use of numerical methods, especially finite element analysis (FEA), enables precise mapping of the biomechanics of the knee joint. These models take into account the complex biomechanical properties of tissues, including non-linear mechanical behaviour, anisotropies, or structural porosity [361]. Combining these approaches is an important step toward non-invasive and modern diagnosis of degenerative diseases [362,363]. Current AC biomechanical models include single-phase elastic structures, as well as advanced multiphase models [364]. [PERSON] et al. [364] extensively reviewed such models, pointing out their usefulness for analysing the distribution of stresses and displacements under dynamic loading conditions. [PERSON] et al. [365] created a nonlinear, three-dimensional poroelastic model to simulate fluid flow, as well as pressure changes within tissues. In the work of [PERSON] et al. [362], a three-dimensional FEA model of cartilage, meniscus and ligaments was developed, allowing the local stress distribution to be assessed under physiological conditions. In turn, [PERSON] et al. [363], using poroelastic properties and fibril-reinforced cartilage, proposed the implementation of gait cycle data into an FEA model, which contributed to a realistic simulation of loading during walking. Such models should be further extended to simulate how mechanical stimuli propagate as vibroacoustic signals, making it possible to investigate the effects of AC degeneration on signal characteristics. Changes in the mechanical properties of AC, such as a decrease in elastic modulus, affect the way mechanical waves propagate through the joint. The output from a poroelastic model, such as contact displacement or varying pressure, can be used as a source of mechanical vibrations, and then analysed in the time-frequency domain to obtain a synthetic acoustic signal. This approach makes it possible to investigate the effect of changes in AC properties on the spectral characteristics of the recorded signals, which may be of reference in the clinical application of vibroarthrography [366]. The data generated using the models described above can be used as a training base for artificial intelligence models. It is possible to teach machine learning algorithms to classify the degree of AC degeneration based on acoustic features [366]. Reconstruction of mechanical properties can be assisted by neural networks, based on recorded signals, and deep learning algorithms can be used to analyse time-frequency signals showing high sensitivity to structural changes. There has also been significant development in AI applications, particularly deep neural networks, used in medical image analysis. Segmentation of AC from MRI images is challenging due to its low thickness as well as low signal contrast in T1 and T2 sequences. In such cases, U-Net neural networks and their variants are highly effective, enabling high precision with Dice Similarity Coefficient (DSC) above 0.90 [367,368]. Another example of the use of AI with qualitative analysis is the CartiMorph tool, which aims to integrate deep learning-based segmentation with automated depth and AC volume measurements. The study by [PERSON] et al. [369] showed very high precision, with a correlation >0.95 and a DSC >0.90. An interesting approach is the analysis of multimodal data, such as Quantitative Susceptibility Mapping (QSM) images combined with nnU-Net segmentation. The use of this method allows for structural and biochemical assessment of AC, obtaining AUC (Area Under the Curve) values exceeding 0.94 [370]. In addition to structural imaging, methods using vibroacoustic signals are emerging. In the work of [PERSON] et al.