## 3 Knee FEA Workflow
### Pre-Processing
#### 3.1.5 Model Configuration  
This refers to the decisions regarding what knee structures to include in the models, what level of detail is needed, and what assumptions will be included in the final model. [PERSON] et al. [126] showed how this decision-making process results in different strategies to answer the same research question concerning the knee joint using FEA. In that study, five teams developed FE models of two knees to simulate a passive knee flexion between 0 deg and 90 deg, considering the patellofemoral and tiboifemoral joints. In general, the teams had similar workflows, although they integrated some knee structures differently. In brief, the teams considered bones as rigid bodies, just one included deformable cartilages, one of them excluded the menisci, and one of them excluded the tendons. The time required to solve the models varied between 30 min and 9 h.  
Sometimes, a multi-scale model is needed to answer the research question [125, 127, 128, 129, 130, 131]. In these cases, researchers look for the concurrent effects of mechanical stimuli through different physics scales [131]. For instance, it is possible to even simulate the chondrocyte mechanical environment within cartilage under unconfined compression [132] or gait cycle loading in healthy and meniscectomized knees [129]. In more detail, [PERSON] et al. [130] combined FEA with musculoskeletal models assisted by EMG to develop a subject-specific knee joint model. They formulated bones as rigid bodies, cartilage as fibril-reinforced poroviscoelastic, menisci as fibril-reinforced poroelastic, and ligaments as non-linear springs. The loading corresponded to a gait cycle, and the muscle forces were estimated by an EMG-assisted musculoskeletal model. They performed a sensitivity analysis to study the effect of ligament pre-strain on the mechanical response of cartilage. The results indicated that the pre-strain of ACL, PCL, and MCL highly affected contact forces and contact areas, while it less affected cartilage stresses, fluid pressures, and fibril strains. Thus, the authors recommended being more rigorous in calibrating ligament pre-strain when the interest is the kinematics and kinetics of the joint compared to studies focused on the mechanical response of articular cartilage. Regarding the time, authors spent one month in the pre-processing stage, the FE model ran in 30 h and the post-processing stage was automated.  
[PERSON] et al. [32] explored expediting FE models for potential clinical use in the future. They compared articular cartilage biomechanics between kinetic and kinetic-kinematic driven knee joint models simulating a gait cycle. In the most complex kinetic model, forces and moments drove the simulation during the stance phase of gait. In the simplest kinetic-kinematic driven model, only forces and rotations were used to control the simulated gait motion. Comparisons between models revealed similar cartilage mechanical responses (i.e., maximum principal stress, maximum principal strain, and pore pressure). In conclusion, combining kinematic and kinetic inputs, which is easier to implement than kinetic alone, saves weeks of workload compared to the most complex model without sacrificing much accuracy.