  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 deep learning model designed
to automate the estimation of Ejection Fraction (EF) and Left Ventricular Wall
Thickness from echocardiograms. The implementation of ConFormer has the
potential to enhance preventative cardiology by enabling cost-effective,
accessible, and comprehensive heart health monitoring, thereby saving countless
lives. The source code is available at https://github.com/Aether111/ConFormer.
