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The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses. © 2022 JoVE Journal of Visualized Experiments.