Computer modelling and artificial intelligence with big data for better diagnostics and therapy of cardiovascular disease
Abstract
In silico clinical trials are the future of medicine and virtual testing and simulation are the future of medical engineering. The use of a computational platform can reduce costs and time required for developing new models of medical devices and drugs. The computational platform in different projects, such as SILICOFCM, was developed using state-of-the-art finite element modelling for macro simulation of fluid-structure interaction with micro modelling at the molecular level for drug interaction with the cardiac cells. SILICOFCM platform is used for risk prediction and optimal drug therapy of familial cardiomyopathy in a specific patient. STRATIFYHF project is to develop and clinically validate a truly innovative AI-based Decision Support System for predicting the risk of heart failure, facilitating its early diagnosis and progression prediction that will radically change how heart failure is managed in both primary and secondary care. This rapid expansion in computer modelling, image modalities and data collection, leads to a generation of so-called “Big Data” which are time-consuming to be analyzed by medical experts. In order to obtain 3D image reconstruction, the U-net architecture was used to determine geometric parameters for the left ventricle which were extracted from the echocardiographic apical and M-mode views. A micro-mechanics cellular model which includes three kinetic processes of sarcomeric proteins interactions was developed. It allows simulation of the drugs which are divided into three major groups defined by the principal action of each drug. The presented results were obtained with the parametric model of the left ventricle, where pressure-volume (PV) diagrams depend on the change of Ca2+. It directly affects the ejection fraction. The presented approach with the variation of the left ventricle (LV) geometry and simulations which include the influence of different parameters on the PV diagrams are directly interlinked with drug effects on the heart function. It includes different drugs such as Entresto and Digoxin that directly affect the cardiac PV diagrams and ejection fraction. Computational platforms such as the SILICOFCM and STRATIFYHF platforms are novel tools for risk prediction of cardiac disease in a specific patient that will certainly open a new avenue for in silico clinical trials in the future.
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