## 4 Discussion  
The purpose of this study was (1) to develop a modeling and simulation framework to replicate/conduct an MVIC test in silico by using a combination of experimental data (i.e., EMG recordings, dynamometry data, and medical images) and computational tools (i.e., personalized MSK models) and (2) to identify the minimum level of personalization required to achieve sufficiently accurate predictions (within 10% from the experimentally measured values) in healthy female subjects. To minimize the influence of the modeler in the generation of the personalized MSK model, established procedures, already developed, tested, and validated, were adopted [11; 63; 64; 65; 66]. In addition, a sensitivity analysis was performed to account for uncertainties of the only personalized input highly influenced by the modeler's expertise (i.e., the segmentations of the muscles). Four MSK models with varying levels of personalization were built from MRI data for nine healthy young females. The muscle properties were adjusted based on generic muscle volumes representative of an age-matched population (i.e., \(\mathrm{M_{genPCSA}}\)), PCSAs from  
Figure 6: Results from the Montecarlo analysis: the orange dots represent the thousand simulated outcomes for each subject, while the black squares represent the experimental data with the standard deviation error of the dynamometry measurements. medical imaging data (M\({}_{\rm ssPCSA}\)), the muscle maximum activation level (M\({}_{\rm genPCSAssEMG}\)), or both the PCSAs and the muscle maximum activation level (M\({}_{\rm ssPCSAssEMG}\)). Our results showed that (1) digital twins are able to predict maximal knee extension torques close to the experimental MVIC torque values observed in vivo in a healthy female cohort, and (2) muscle volumes derived from medical images (i.e., segmented muscle volumes to compute MIF values) are not sufficient alone to achieve acceptable results (i.e., prediction error < 10%). Personalized muscle activation levels may be necessary (i.e., the M\({}_{\rm ssPCSAssEMG}\)). Moreover, the sensitivity analysis highlighted that the predicted outcomes are not influenced by the uncertainties that affect the muscle segmentation and, thus, the maximal isometric force of each muscle. The MVIC torques at 90\({}^{\circ}\) of knee extension experimentally acquired were in line with the literature for an age-matched population [67; 68; 69], as well as the predicted maximal torque while using all four different models. However, when population data was used to scale the PCSAs of the models, the models tended to overestimate the experimental values, and the prediction error was acceptable only for one of the subjects, thus suggesting that the M\({}_{\rm genPCSA}\) models lacked subject specificity. When personalized information from MRI data was employed to tune the MIF values (M\({}_{\rm ssPCSA}\)), the error was reduced in general, and for three out of nine subjects, it was within the pre-defined threshold value of 10%. This aligns with previous work showing the need for subject-specific information from medical imaging data [16; 70]. Nonetheless, only the combined personalization of both MIF values and maximal activation levels (extracted from experimental EMG data) achieved low errors for all participants, contrary to previous findings [33]. When only the EMG data were employed to personalize the models, the prediction error was comparable to the results of the M\({}_{\rm ssPCSA}\) models. This study has some limitations. The lack of kinematic data to accurately describe the knee joint motion throughout the execution of the MVIC test forced us to set a static pose. The isometric nature of the test justified the choice, although it is not ideal since the subject moves during the execution of the test, altering the knee flexion angle and causing misalignment between the joint and the dynamometer axes of rotation [71; 72].