prediction of Pulmonary artery and peripheral vascular pressure based on echocardiography data and artificial neural network

Nowadays measuring the peripheral resistance of pulmonary artery is of high importance for crucial deciding on accomplishment of many different types of cardiac surgeries. If the resistance is higher than a special threshold the patient is deemed to be inoperable. Even thought there are remedies which have been proposed for dropping the pulmonary artery resistance in initial stages of diseases, re-measuring of this resistance is indispensable for reduction monitoring. Concurrently catheterization is the sole invasive approach that has its own side effects. Due to high number of cardiac patients, it seems necessary to introduce a precise non-invasive approach for measuring the resistance of pulmonary artery. The aim of this study was to find a replacement for catheterization for estimation and measurement of pulmonary vascular resistance (PVR) and to evaluate the right ventricular function and converting them into a form of software (according to neural network) at different pressures of the pulmonary vessels.