Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance - Bonnemain, Jean; Zeller, Matthias; Pegolotti, Luca; Deparis, Simone; Liaudet, Lucas (Frontiers in Cardiovascular Medicine, 2021)
Characterizing left ventricle (LV) systolic function in the presence of an LV assist device (LVAD) is extremely challenging. We developed a framework comprising a deep neural network (DNN) and a 0D model of the cardiovascular system to predict parameters of LV systolic function. DNN input data were systemic and pulmonary arterial pressure signals, and rotation speeds of the device. Output data were parameters of LV systolic function, including end-systolic maximal elastance (E_max,lv), a variable essential for adequate haemodynamic assessment of the LV. A 0D model of the cardiovascular system, including a wide range of LVAD settings and incorporating the whole spectrum of heart failure, was used to generate data for the training procedure of the DNN. The DNN predicted E_max,lv with a mean relative error of 10.1%, and all other parameters of LV function with a mean relative error of less than 13%. The framework was then able to retrieve a number of LV physiological variables (i.e. pressures, volumes, and ejection fraction) with a mean relative error of less than 5%. Our method provides an innovative tool to assess LV haemodynamics under device assistance, which could be helpful for a better understanding of LV-LVAD interactions, and for therapeutic optimization.