In silico database of ~500 000 virtual patients with diverse cardiovascular disease

<p>The presented dataset contains a large virtual cohort of more than 50'000 heart failure patients characterized by realistic traces of volumes, pressures, flows, and regional mechanics. We used the well-established CircAdapt model <a href="https://www.circadapt.org/">(https://www.circadapt.org/</a> ,&nbsp; <a href="http://framework.circadapt.org/">http://framework.circadapt.org/</a>) of the human heart and circulation to simulate a large cohort of virtual patients, covering a wide range of HF-related disease heterogeneity and severity.&nbsp;<br>In the construction of virtual patients, generating a variety of parameter sets for the initial population is essential and can be accomplished using various sampling techniques. In our investigation, we utilized the Sobol-low discrepancy sequence to ensure uniformity across the high-dimensional parameter space. For a more detailed explanation please refer to the document <em>"MARCIUS_deliverable_D1.5_VirtualDatabase.pdf"</em></p> <p><strong>Acknowledgments:</strong> This work was supported by the European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie grant agreement No. 86074, <strong>"MARCIUS - MARie Curie Intelligent UltraSound"(<a href="https://www.marcius-project.com/">https://www.marcius-project.com/</a>)</strong>. MARCIUS rationale was to develop a comprehensive in silico simulation platform comprising both the generation of virtual patients (<em>presented dataset</em>) and their associated realistic image data. Such approach would make it possible to learn the most relevant patterns within a wide representative set of patients to lead the training of ML-based image processing algorithms in order to analyze real-world clinical data.</p> <p>&nbsp;</p>