###### Abstract  
Osteoarthritis (OA) is a degenerative disease that affects the synovial joints, especially the knee joint, diminishing the ability of patients to perform daily physical activities. Unfortunately, there is no cure for this nearly irreversible musculoskeletal disorder. Nowadays, many researchers aim for in silico-based methods to simulate personalized risks for the onset and progression of OA and evaluate the effects of different conservative preventative actions. Finite element analysis (FEA) has been considered a promising method to be developed for knee OA management. The FEA pipeline consists of three well-established phases: pre-processing, processing, and post-processing. Currently, these phases are time-consuming, making the FEA workflow cumbersome for the clinical environment. Hence, in this narrative review, we overviewed present-day trends towards clinical methods for subject-specific knee OA studies utilizing FEA. We reviewed studies focused on understanding mechanisms that initiate knee OA and expediting the FEA workflow applied to the whole-organ level. Based on the current trends we observed, we believe that forthcoming knee FEAs will provide nearly real-time predictions for the personalized risk of developing knee OA. These analyses will integrate subject-specific geometries, loading conditions, and estimations of local tissue mechanical properties. This will be achieved by combining state-of-the-art FEA workflows with automated approaches aided by machine learning techniques. osteoarthritis; knee joint; articular cartilage; finite element analysis +
Footnote †: journal: MedicalOA is a degenerative disease that weakens the mechanical properties of articular cartilage and bone [5; 7]. Knee OA was responsible for sequelae in more than 250 million people worldwide and caused 14 million years lived with disability (YLDs) globally in 2010 [8]. Symptom treatments and surgical interventions make OA an expensive illness for countries, causing an economic burden of approximately 2% of GDP [9; 10]. For instance, it has been estimated that a total knee joint replacement (TKR) surgery may cost up to 50,000 USD, and monitoring the success of the surgery may double those costs [11]. Furthermore, over 600,000 TKR surgeries take place in the US annually [12], exposing the necessity of preventative alternatives to surgery. The major risk factors for the onset and progression of OA comprise age, gender, genetics, overweightness, joint misalignment, and joint injuries [5; 7; 13; 14; 15; 16; 17; 18]. These risk factors can be modifiable, such as weight and misalignment, or unmodifiable, such as age and genetics. However, due to the patient-specific nature of the disease, predicting its progression is extremely challenging. Therefore, there is an unmet need for clinical tools that would give answers to two questions: (1) Will the current knee joint state and physical activity habits lead to severe OA? (2) If yes, can OA be prevented or its progression delayed by conservative treatments (e.g., weight loss, gait retraining, physical exercise)? Promising computational methods are intended to solve them. Artificial intelligence- (AI-) based solutions have been developed to predict joint condition using baseline information [19; 20]. These methods have shown capabilities to classify patients into low-risk and high-risk groups based on subject characteristics. However, they have limitations in simulating the effects of different interventions because they require a large amount of training data to include all possible treatment options. Currently, such data are not available. Figure 1: Frontal (**left**) and coronal (**right**) views of the internal structures of a left knee joint. Light yellow represents bone structures, orange ligaments and tendons, red menisci, and light blue articular cartilages. Finite element (FE) analysis (FEA) is another computational approach that solves physics-based problems using constitutive and governing equations.