ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function
This addresses the high cost and labor-intensive nature of echocardiogram screenings for cardiologists and patients, potentially saving lives through improved preventative care.
The paper tackles the problem of automating the estimation of Ejection Fraction and Left Ventricular Wall Thickness from echocardiograms to assist in early detection of heart failure, with the result being a novel deep learning model called ConFormer that aims to make heart health monitoring more cost-effective and accessible.
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.