Neural Networks Modelling after Myocardial Infarction in Rats

Abstract

Cardiac function is reduced after acute myocardial infarction due to myocardial injury and to changes in the viable non-ischemic myocardium, a process known as cardiac remodeling. Current treatment of patients with acute myocardial infarction (AMI) reduces infarct size, preserves left ventricular function, and improves survival. However, it does not prevent remodeling which leads to heart failure. The aim of the present study was to model the echocardiographically estimated data with respect to the surgically collected data using Neural Networks. In particular, we attempted to analyze the relationship between cardiac remodeling variables obtained from echo and the infarct variables obtained from surgical data using neural networks. Towards that purpose, 199 rats were separated in two groups. The first group was subjected to coronary artery ligation, while the second underwent a sham operation. Echocardiography was used for rat monitoring. Scar weight and area were estimated after surgical incision. It appeared that several factors could be modelled with neural networks. Such modeling approaches could be developed to enable the simulation of the pathophysiological process after an Acute Myocardial Infarction (AMI) and predict with accuracy the effects of novel or current treatments that act via modulation of tissue injury, Left Ventricular dilation, geometry and hypertrophy.