An Automatized Online Platform for Left Ventricular Behavior Assessment Based on Echocardiographic Recordings
摘要
Since its beginnings, cardiovascular (CV) medicine has relied on professionals’ judgment to diagnose various types of studies carried out on patients. Today, with the support of technologies such as machine learning (ML) and artificial neural networks, medical professionals can automate, speed up or improve their diagnoses by obtaining complementary information on the detection of diseases and CV conditions. This work proposes an online platform based on ML methods, applied to the analysis of echocardiographic recordings. The aim is to obtain dynamic measurements of an individual left ventricle behavior, and contributing to the detection of different types of heart conditions, thereby improving diagnostic precision.