## 3 Knee FEA Workflow
### Verification and Validation  
When developing new models or approaches, they should be verified and validated before the results they produce can be trusted. The verification can be completed by comparing the computational model results with those of similar previous works. The reason behind this is to confirm that the new models offer logical results. Subsequently, the validation is crucial for demonstrating the capabilities of the model to reproduce and predict experimental results. The source of experimental data for validations includes in vitro and in vivo experiments. In vitro experiments include testing of tissue explants and complete joints from cadavers. Commonly, stress-relaxation test data with several strain steps are used to calibrate and validate material parameters of cartilage, especially for complex material models [63; 104; 153; 154]. In vivo experiments can include data from instrumented knee prostheses (with a force sensor) after total knee replacement (TKR) surgery, and data from non-invasive techniques such as MRI [155], and external transducers such as pressure sensors, electromyography, and markers for gait analysis [24; 124; 156]. Parameters for the validation of musculoskeletal and FE joint models include in vivo forces through tibofemoral and patellofemoral joints [3], cartilage deformations [155; 157; 158], and displacements of the patella and menisci, which are variables useful for the assessment of musculoskeletal disorders [13; 111; 159; 160; 161]. In this regard, MRI and fluoroscopy have been used to study the relative motion of internal structures of the knee in vivo [162; 163; 164; 165]. For instance, [PERSON] et al. [166] combined dual fluoroscopy with SSM to evaluate the kinematics of the knee joint. The dual fluoroscopy combined with the SSM technique was used to automatically calculate subject-specific geometries and the relative motion of bony structures. They studied the kinematics of the stance phase of three subjects walking at 3.2 kilometers per hour. Their method produced root mean square (RMS) errors as high as 1.77 mm in geometries, 3.3 degrees in rotations, and 2.4 mm in displacements, compared to a CT-based method. This study evaluated an approach to expedite the kinematic analysis of subjects and reduce radiation exposure compared to using CT scans. Regarding knee OA prediction models, they are currently verified and validated against qualitative and quantitative data. Qualitative assessments involve, for example, comparisons of the location of damage and morphology changes in cartilage [95; 149; 167]. On the other hand, quantitative assessments are more challenging due to experimental inaccuracies and difficulties in analyzing clinical image data properly. At a local tissue level, the OA Research Society International (OARSI) proposed grading the tissue damage based on histologic characteristics, resulting in a six-grade scale [168; 169]. At a whole-organ level, other grading systems can be used. They include the Collins grading system [168], which classifies tissue characteristics by direct observation of the tissue, commonly with the patient under surgery or autopsy. A recent study [170] found a high correlation (R = 0.887) between OARSI grading scores and Collins scores using 22 cartilage samples from six donors, indicating an equivalence between both physical scale evaluations. For conservative clinical practices, performing biopsies to collect tissue samples could be detrimental to the integrity of the joint. Thus, comparison against clinical history data can be considered the gold standard [18; 20; 171; 172]. Here, simulated predictions for OA development, estimated with the information obtained at the baseline and model outcomes, are compared against follow-up data (changes compared to baseline) utilizing quantitative data obtained from clinical imaging.