A Novel Approach for Detecting Abnormality in Ejection Fraction Using Transthoracic Echocardiography with Deep Learning

Prattay Guha Sarkar, Vishal Chandra

Abstract


Cardiovascular diseases (CVD) are the prime cause of mortality in people worldwide. Mortality in CVD has been strongly linked to Ejection Fraction (EF) in various studies1. Left ventricular ejection fraction (LVEF) is the central measure of left ventricular systolic function. LVEF is the fraction of chamber volume ejected in systole (stroke volume) in relation to the volume of the blood in the ventricle at the end of diastole (end-diastolic volume)2. Evaluation of left ventricular systolic function by left ventricle ejection fraction (EF) using Transthoracic echocardiography is usually a first line investigation. Determination of Ejection fraction (EF) is done most commonly by a semi-automatic process in which echocardiographer segments the left ventricle in both systolic and diastolic frames to generate systolic and diastolic chamber dimensions. The whole process in time consuming and highly dependent on operator experience causing a lot of inter-observer and intra-observer variations. Our goal is to develop algorithms so as to reduce the time consumed during whole process and make it more reliable and reproducible. We have used M-Mode of Left ventricle in PLAX view to measure chamber dimensions and calculate EF by Teich method. EF >50% has been categorized as normal ejection fraction. EF < 50% has been categorized as reduced ejection fraction and LV systolic dysfunction. In this research we have used fine-tuned ResNet 50 and trained it with 200 cases. We observed an accuracy of 98% and a F1 score of 77% for reduced EF (<50%) and 77% for normal EF (>50%). Although this is a small dataset, it shows that deep learning algorithms can be applied to medical imaging. ResNet50 is a preferred choice in terms of accuracy. This research will serve as a stepping stone for future research and will determine other cardiac matrices.


Keywords


Echocardiography, CNNs, Ejection fraction, Deep learning

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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