## 4 Discussion
In the future, these unmodifiable characteristics (age, gender) of patients could be considered in models through correlations and by utilizing statistical methods or machine learning algorithms, together with in vivo properties measured using noninvasive techniques. FEA approaches coexist with other computational methods to study OA. For instance, several studies have utilized mathematical models of the patellofemoral joint (PFJ), assuming the joint as a two-dimensional mechanism, to determine contact areas and forces resulting in contact pressure distributions [194, 195, 196, 197]. This approach has demonstrated good results by relating the biomechanical parameters of the PFJ to OA. These studies highlight that this method works rapidly compared to FEA, although the range of movement is limited to the sagittal plane and the articular cartilage is modeled as an isotropic linear elastic material. Similarly, discrete element analysis (DEA) has been utilized to model hip and knee joint contacts [198, 199, 200]. DEA demonstrated a similar contact response compared to FEA models of the hip joint [199], and similar contact pressure distributions to experimental measurements in the PFJ [198]. DEA does not require a volumetric mesh, using shell elements instead, with which elastic springs are associated, relating to the thickness of tissue (cartilage) and elastic properties (Young modulus and Poisson ratio). In this method, contact is enforced by the springs when penetration of surfaces is detected. DEA offers solutions in seconds, although cartilage is assumed to be isotropic and linear elastic. Statistical and AI tools are active fields to study knee OA. From this point of view, [PERSON] et al. [20] modeled the longitudinal progression of knee OA based on OAI data of medical visits of 1243 patients, including X-ray measurements and WOMAC scores. With this information, they built regression models to classify subjects into different clusters according to the observed radiographic and pain progressions. Their approach was able to predict radiographic progression utilizing the information from two visits separated by one year and predicted the pain progression using only one observation. [PERSON] et al. [121] reviewed many applications of machine learning related to knee OA. These covered the prediction of disease onset and progression based on a variety of patient characteristics [201], correlation of features that could allow the early diagnosis of knee OA, and techniques for fast medical image segmentation [202] and classification [203]. However, AI methods cannot be used to simulate the effects of clinical interventions on the mechanics of the knee, including the subsequent adaptation processes in articular cartilage. Based on the investigations mentioned in the present review, studying knee OA via FEA is becoming faster and more reliable in terms of model input (pre-processing). Some gaps and difficulties are being filled and solved for translating these exploratory advances to the clinical environment. We believe the fields of active research related to studying knee OA via FEA would be:  
* Automate pre-processing tasks using a minimum amount of information (geometry generation and loading specification);
* Incorporate site-specific tissue composition and mechanical properties;
* Develop accurate algorithms for OA prognosis (theory and validation);
* Develop efficient multiscale modeling methods (from joint to tissue level);
* Combine advances in FEA, musculoskeletal modeling, and AI. Thus, in the near future, coming investigations will efficiently combine machine learning approaches, faster and more affordable imaging techniques for rapidly generating subject-specific geometries and meshes, and personalized mechanical inputs such as tissue properties and loading scenarios. Similar phenomena will occur within processing and post-processing with accelerated task performance via coupling analysis methods (e.g., FEA and AI).