## 3 Knee FEA Workflow
### Onset and Progression of OA  
Several factors are associated with increased risk of developing OA, such as obesity, diabetes, genetics, age, and gender [7, 140, 141]. In addition to these factors, joint injuries may cause lesions in articular cartilage, contributing to the development of post-traumatic OA [21, 95, 142]. However, the causative mechanisms for the initiation and progression of OA are not fully understood. FE models have been used to test hypotheses regarding the OA mechanisms at different scales. On the one hand, at the tissue level, researchers have studied the effects of the depletion of cartilage constituents on the mechanical response of the tissue, such as reaction forces and displacements in unconfined compression relaxation tests of cartilage samples [143, 144, 145]. Others investigated the cross-talk between tissues that could address tissue remodeling when the mechanical environment changes [28]. Mechanobiological models have also intended to explain the interplay of tissue constituents with the degradation of cartilage, including cell signaling to modify the mechanical response [21, 146, 147, 148]. On the other hand, at the joint level, researchers have simulated a variety of diseased conditions. These studies include investigations of the effect of size and location of cartilage superficial defects on strain and stress concentrations [139], the effect of knee structures' relative orientation on cartilage thinning [149] and the effect of collagen disorganization on the load-bearing functions of cartilage [150, 151]. Moreover, to understand possible OA pathways, [PERSON] et al. [146] developed a mechanobiological knee joint model to predict FCD alterations involving cartilage lesions in ACL ruptured and reconstructed patients. Their results were contrasted with quantitative longitudinal changes observed in MRI maps. That study postulated that excessive shear deformations and fluid velocity are responsible for the FCD loss in the injured cartilage tissue. In addition, related to an ACL deficient knee, [PERSON] et al. [148] studied the subchondral bone remodeling after simulating a three-month walking loading condition. They used a multiscale approach and compared the simulated cell signaling between healthy and ACL deficient states, which would drive bone remodeling and the subsequent cartilage inflammatory response. Results showed higher strain at the zone of calcified cartilage and a higher concentration of proinflammatory mediators in the ACL deficient knee compared to the healthy condition. Aiming for future clinical applications of knee FEAs, [PERSON] et al. [152] implemented a FE-based method to estimate the onset and progression of knee OA in 21 knees. In their method, the cartilage collagen network was assumed to soften when a stress threshold for failure was exceeded, mimicking the tissue degradation. Models included subject-specific MRI-based geometries only for the medial compartment, based on the OA prevalence, and a typical joint loading share between the medial and lateral sides. The menisci were subtracted from the models, but their load-bearing effect was considered when applying the generic gait loading condition (scaled with subject body weight) through the tibofemoral joint. The researchers simulated the future cartilage degradation from a baseline condition when all subjects were healthy, with a Kellgren-Lawrence grade of 0 (KL0). Then, they verified the predictions against OAI data by classifying follow-up KL grades of the 21 knees in healthy (KL0) and diseased (KL2 and KL3) groups. In order to include more subjects efficiently, [PERSON] et al. [26] proposed a FEA template-based method for the above approach (Figure 5). They parameterized the model geometries to scale a template mesh from an atlas of 21 subjects to different individuals. In addition, they used stress thresholds for damage linked to the age of the subject [140]. By this method, the time to create a subject-specific model was reduced from one week to 2 min.