## 4 Discussion  
In this study, atlas-based FEA workflow-based GPR models were extensively evaluated to determine their capabilities of simulating/predicting the FEA outputs and anatomical dimensions from knee X-Rays. The first set of ML models (GPR1) was trained to estimate cartilage mechanical responses and cartilage degeneration against the atlas-based FEM-based workflow, based on age, weight, and anatomical knee joint dimensions. The second set of ML models (GPR2) was trained to predict personalized knee joint geometries based on age, weight, height, and gender. The GPR models had good (GPR2) to excellent (GPR1) prediction accuracies. Moreover, to investigate a more generalized approach to predicting cartilage degeneration, i.e., the development of OA, at a population level, GPR1 and GPR2 models were combined. Surprisingly, the combined model, which did not include any clinical image data, was also able to classify osteoarthritic and healthy knees. In recent years, a trend of developing ML/DL-based models to predict the onset and progression of KOA has emerged [11; 12; 13]. In these studies, various sets of predictor variables have been tested, providing different capabilities to offer predictions for cartilage degeneration. The superiority of the models is assessed by AUC values, but AUC value comparison is insufficient for judging whether one model is better than another since the target variables and time periods for prediction may differ a lot in those models. For example, in one study [27], progressive OA is defined when cartilage thickness is decreased by more than 0.7 mm, whereas other studies usually define progressive/severe OA as an increase in KL grade to 2-4 [11; 12; 13]. Therefore, it is extremely crucial to adjust the data properly to avoid uncertainty when interpreting the usefulness of the developed models. For example, a 1 mm reduction in cartilage thickness in patients with initially thick cartilage (JS > 1 cm) would likely be diagnosed as healthy. Cartilage thinning or joint space narrowing is natural during aging, and it may be faster during certain ages [28; 29]. It would therefore be preferable to use threshold values that are somehow linked to the severity of KOA, like KL grades. This is precisely why, in this study, the KL grades were used as the target variable to classify the severity of KOA. The prediction accuracy of the GPR2 models for knee shape was surprisingly high. The results indicate that in addition to knee size, cartilage thickness (in a healthy knee) can be estimated with high accuracy based on only age, weight, height, and gender. Similar findings have been presented in earlier studies [28; 30]. This further underlines that instead of predicting cartilage thinning, a more appropriate approach would be to predict different levels of OA severity based on commonly agreed criteria because cartilage thinning occurs naturally during aging and, therefore, possesses no need for computational methods to predict it. Instead of cartilage thinning, the ability to predict the necessity of joint replacement surgery would be of higher importance. This would help in targeting preventative conservative treatment options to those who would actually benefit from them. It has been shown that subjects with KL grade 3 or 4 have an increased risk of knee joint replacements and a decrease in quality of life compared to those with KL grades 0, 1, or 2 [6; 31]. Thus, future studies should prioritize the ability to predict individuals at high risk of developing KL3 or 4 knees. Prediction accuracies for the KOA progression for atlas-based FEA workflow, GPR1, and GPR1 + GPR2 models were very similar with AUC values varying between 0.73 and 0.74 for each. This can be considered puzzling especially as the GPR1 + GPR2 model provides such comparative prediction accuracy without the need for any clinical image data input parameters from subjects, whereas FEA and GPR1 require that subject-specific information.