Heart valve disease is frequently implicated as both a primary and secondary contributor to heart failure. Mitral valve (MV) disease, such as insufficiency or stenosis, can arise from structural valve abnormalities, be influenced by ventricular dysfunction, or as a combination of both [1]. Patient-specific simulations have the possibility to enhance our understanding of valvular dynamics under various flow conditions and predict the outcomes of valve repair. We thus aimed to develop a computational framework using Fluid-Structure interaction (FSI) to simulate patient-specific mitral valves to investigate the velocity through the valve, the valve opening, and valvular dynamics and validate simulation results against echocardiography (Echo) data. Four out of ten planned healthy volunteers, aged 28 ± 7.6 years, have so far been recruited. The patient-specific MV geometry was obtained from 3D Echo data with an automated segmentation process and inserted into a generic left heart model (Fig. 1A). The contraction of the heart was numerically modeled by applying the ventricular volume change as a mass flow boundary condition at the apex. The boundary at the MV was set to a zero-pressure outlet and the boundary at the aortic valve was set to a zero-pressure boundary during systole and to a wall condition during diastole. FSI was achieved with the software Star-CCM+ and Abaqus/Standard. The MV tissue and chordae tendineae were modeled as linear elastic with Young’s modulus of 1 MPa and 22 MPa. The Carreau model was employed to model the blood viscosity behavior, assuming laminar flow. Results of the first simulated volunteer show satisfactory agreement of the valvular dynamics during the systolic phase, 0-0.29 % of the cardiac cycle, compared with Echo data. The simulated valve starts to close after the filling phase of diastole at 0.45 % of the cardiac cycle when it is expected to close at the next contraction. The maximum valve opening differs by 9 %, and the mean trans-valvular velocity differs by 18 % compared to Echo data. Further improvements lie in the material modeling of the MV, for improved estimation of the valve opening and thus, the velocity estimation. We believe the model has the potential to predict person-to-person differences in patient-specific simulations and may thus, in future applications, be used to predict valve repair outcomes in children born with heart disease.