## 4 Discussion
The reason for this can be found in the inclusion criteria for selecting suitable subjects for this study. In the current study, knee dimensions were collected from radiographically healthy knees without visually detectable signs of KOA. As the results of the GPR2 model have already shown, the joint geometry and cartilage thickness can be predicted with reasonable accuracy from this patient population. This led to surprisingly good results in predicting the risk of osteoarthritis onset with GPR1 + GPR2 models. As AUC values of 0.7-0.8 can be considered an acceptable discrimination accuracy, this study emphasizes that FEA is not necessarily needed to offer personalized predictions for the onset and progression of KOA, as it can be replaced by well-trained ML models using GPR algorithms. However, it should be considered that when developing an ML-based classifier based only on those inputs (predictor variables) needed in the GPR1 + GPR2 model, the output is a binary classifier (KL01 vs. KL34) that is not capable of showing the effect of different interventions (such as weight loss) on the predicted risk for the onset and progression of KOA. Therefore, a switch from regression-based ML models to ML classifications is not recommended. In GPR1- and GPR1 + GPR2-based approaches, the influence of the change in the input parameters (weight) on the prediction can be quantified. In terms of patient engagement with conservative interventions, e.g., weight loss, it is important to show how individual lifestyle changes may make a difference in disease progression [22]. Because the GPR models in this study were trained on a large patient dataset including healthy knees with a variety of subject characteristics, they will perform reasonably well for a wide range of patients in terms of age, height, weight, and gender. However, it must be considered that the cartilage in the tibiofemoral joint must be intact without signs of excessive thinning or cartilage injuries. The best thing about the GPR models developed in this study is that they work in real time. This is a great asset, as the atlas-based FEA workflow utilized in this study requires approximately two hours of simulation time per knee joint. The trained ML models based on the GPR algorithms will provide a novel technology to evaluate mechanical responses within the knee for researchers or clinicians who have no former knowledge of FEA (Figure 5). Furthermore, the introduced GPR models will enable the development of new types of exploitable technologies. In the current approaches, the gait loading in the atlas-based FEA workflow that was used for training (GPR1) was based on the generic loading conditions based on the body weight of the subject [21; 25]. In the future, generic loading conditions could be replaced with personalized knee joint loading data based on IMU sensors or video data, e.g., from smartphones [32; 33]. This would then allow new conservative non-clinical interventions for the prevention of osteoarthritis. For instance, such technology could be used for selecting a shoe or an insole for optimized joint health or gait re-training. Comorbidities or significant anatomical abnormalities were not included in our model. It is really difficult to estimate how much they would affect the presented results. Before this could be investigated with the help of our model, we would need to understand how these variables affect the FEA model variables (e.g., joint movement and load) that are needed to generate training data. If these would have no effect, then the results would remain the same for these subjects. On the other hand, if they would have a large effect, then the simulated and predicted KOA levels would be different, but the same between the FEA and the trained GPR models. Whether these results would be any more accurate cannot be determined until this is further investigated.