## 1 Introduction  
Knee osteoarthritis (KOA) is a common joint disease, and it is estimated that the prevalence of KOA among adults aged 45 years and over is around 19% [1]. KOA is a whole joint disease involving all joint tissues (cartilage and meniscal degeneration, subchondral bone remodeling, inflammation, and fibrosis of both the infrapatellar fat pad and the synovial membrane) [2; 3]. The disease usually progresses slowly with aging [4; 5]. Therefore, the first symptoms, such as joint pain and tenderness, are ignored until they alter normal gait or decrease quality of life [6; 7; 8]. Other risk factors in addition to aging include gender, heredity, joint injuries, and joint misalignment [2]. Usually, the cause of the pain is not clinically confirmed until the pain is unbearable. For this reason, patients often seek medical attention too late, and the only treatment available is pain relief. This usually only prolongs the decision to have joint replacement surgery, which, in many cases, is unavoidable due to excessive joint pain. Since joint replacement surgery due to KOAhas a significant social cost of billions of euros each year [9], new and novel preventative methods for KOA management are needed. Although some modifiable risk factors are well known, such as weight or lifestyle (active vs. inactive) [2,10], few people are willing to make lifestyle changes to reduce their risk factors. One explanation is that there are no clinically validated methods to quantify the impact of a change on the future condition of the joint, e.g., risk for development of KOA. Current research on the prevention of osteoarthritis mainly focuses on developing quantitative methods and models to classify patients between healthy and osteoarthritic patients [11, 12, 13]. They are mainly based on machine learning (ML) and/or deep learning (DL) algorithms, where the models are trained to classify radiographically healthy patients into different degrees of KOA over a given time period with different prognostic variables. However, the major problem is that although ML/DL models may outperform other approaches such as FEA, the trained models have not been tested or validated against independent data from different sources [14]. Furthermore, many classifiers need inputs from various predictor variables that are either difficult, time consuming, or too unpractical to collect from patients (e.g., a certain MRI sequence, or blood or urine samples). This raises the question of the rationality and accuracy of trained ML/DL models. Finite element analysis (FEA) can be used to estimate internal forces within the knee joint tissues during certain physical activities [15,16]. Thus, these models are capable of offering personalized information from stresses and strains inside cartilage and/or bone and the effect of alterations in subject weight or gait [17,18]. Recent studies on KOA prediction using FEA workflow [19,20] have shown promising results, which could be translated into a future tool to assess the future risk of KOA, and further, how different interventions would reduce the risk. The FEA workflow has some important limitations that cannot be tolerated in a clinical tool. First, model generation in FEA usually takes days per subject, which can be considered a bottleneck for subject-specific predictions in clinical use. However, this can be avoided by using an atlas-based FEA workflow, where the existing atlas FEA model is scaled to the anatomical dimensions of the knee [21]. Second, the computational time needed to generate a quantitative output from FEA can take several hours. This is unfeasible when simulating the effects of different interventions when every modification of the model input and rerun takes several hours. A final limitation associated with both the ML/DL models and the FEA method is the need for a clinical image [16]. The clinical image typically reveals structural changes associated with KOA or damage inflicted by an injury that explains pain in the joint [8].