## 4 Discussion
The presented results suggest that trained GPR models can be considered a viable alternative or even replacement for FEA when making predictions of mechanical responses or future joint conditions (Figure 5), specifically KOA, when predictions are offered for individuals who have no history of a severe knee injury. The GPR models provide a quick and easy tool to assess the risk of onset and progression of osteoarthritis without previous knowledge of FEA methods. Real-time GPR models (with a simulation time of less than a second compared to the several hours needed in FEA approaches) provide a rapid tool for planning preventive care and the technology to engage patients in preventive interventions. **Supplementary Materials:** The supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14209538/s1](https://www.mdpi.com/article/10.3390/app14209538/s1)]([https://www.mdpi.com/article/10.3390/app14209538/s1](https://www.mdpi.com/article/10.3390/app14209538/s1)), Table S1: Material parameters of the HTIPE material model for the femoral and tibial carilages. References from Supplementary file are cited [19, 21, 23, 25, 34-37]. **Author Contributions:** M.E.M. : conceptualization, methodology, software, validation, formal analysis, investigation, writing--review and editing, visualization, project administration, funding acquisition. M.K.L. : methodology, software, validation, investigation, formal analysis, writing--review and editing. M.J.T. : methodology, software, validation, investigation, formal analysis, writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** The research leading to these results received funding from the Research Council of Finland (grants 324994, 328920, 352666), the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (Grant Number: 5654242, 50 TP006), and the Sigrid Juselius Foundation (Grant Number: 230123). The funding sources had no role in the study design, collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data created and analyzed during the current study are available from the corresponding author upon reasonable request. **Acknowledgments:** The authors wish to thank the patients and staff of all the hospitals who have contributed data to the Osteoarthritis Initiative. The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01- AR-2-2260; N01-AR-2-2261; and N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public-use dataset and does not reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners. **Conflicts of Interest:** [PERSON] and [PERSON] own shares in Aikoa Technologies Oy. Figure 5: A UI example from the software for real-time evaluation of mechanical responses within knees for researchers or clinicians who have no former knowledge of FEA (**A**). The user may select between imaging modalities and add subject details and anatomical dimension measurements (input data). Based on this info, the user may ask to have different mechanical output parameters based on trained GPR models (output data). The trained GPR models also enable one to conduct “virtual interventions” by allowing one to make changes in input parameters (age, weight, and loading).