Name
Title | ||
|---|---|---|
Ejection Fraction Classification in Transthoracic Echocardiography Using a Deep Learning Approach |
Abstract | ||
|---|---|---|
Cardiovascular diseases are the leading cause of death worldwide. These diseases are related with a broad range of factors but usually show high correlation with diminished left ventricle function, which can be evaluated by measuring the ventricular ejection fraction through transthoracic echocardiography (TTE), a cost-effective and highly portable first-line diagnosing technique. Ejection fraction (EF) is currently determined through a semi-automatic process that requires manual delineation of the left ventricle area both in a diastolic and systolic frame of the patient's exam. To remove this manual annotation step, which is both time-consuming and user dependent, automatic Computer-Aided Diagnosis (CAD) systems can be used. Herein, we propose the first steps for such a system that classifies ejection fraction in four classes, based on TTE exams, with the objective of automatically providing valuable information to physicians. Our classification method is based on a 3D-Convolutional Neural Network (3D-CNN) trained on a dataset constructed with exams from a cardiology reference center. The dataset creation consisted of three main steps: firstly, for each exam, cine-loops showing the apical 4 chambers view were manually selected; then, 30 sequential frames were extracted from each cine-loop; finally, each frame was pre-processed to mask burned-in metadata. The neural network was designed to explore concepts such as convolutions using asymmetric filters and residual learning blocks. The model was trained on a dataset with 4000 TTE exams and tested on a separate dataset containing 1600 TTE cases. We obtained an accuracy of 78% and a F1 score of 71.3% for unhealthy EF (below 45%), 63.3% for intermediate EF (45-55%), 72.3% for healthy EF (55-75%) and 54.6% for abnormally high EF (above 75%). These results are promising and show that convolutional neural networks can be applied to this domain. Furthermore, this work will serve as a foundation for future research where other relevant cardiac metrics will be determined. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CBMS.2018.00029 | 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS) |
Keywords | Field | DocType |
3D CNN,Ejection Fraction,Transthoracic Echocardiography,Cardiology | CAD,Computer vision,F1 score,Ventricular Ejection Fraction,Pattern recognition,Ejection fraction,Computer science,Convolutional neural network,Artificial intelligence,Ventricle,Deep learning,Artificial neural network | Conference |
ISSN | ISBN | Citations |
2372-9198 | 978-1-5386-6061-4 | 1 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Figueira Silva | 1 | 1 | 2.03 |
| Jorge Miguel Silva | 2 | 1 | 0.68 |
| António Guerra | 3 | 1 | 1.02 |
| Sérgio Matos | 4 | 415 | 29.51 |
| Carlos Costa | 5 | 4 | 3.50 |