ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function

Abstract

Abstract Cardiovascular diseases, particularly heart failure, are a leading cause of death globally. The early detection of heart failure through routine echocardiogram screenings is often impeded by the high cost and labor-intensive nature of these procedures, a barrier that can mean the difference between life and death. This paper presents ConFormer, a novel, light-weight, convolutional neural network based deep learning model designed to automate the estimation of Left Ventricular Ejection Fraction (LVEF) and Left Ventricular Dimensions (LVD) from echocardiograms. Results obtained are comparable to SOTA algorithms but with reduced computational complexity. The implementation of ConFormer has the potential to enhance preventative cardiology by enabling cost-effective, accessible, and comprehensive heart health monitoring. The source code is available at https://github.com/Aether111/ConFormer.