Machine Learning-Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance.
Artificial intelligence
Diagnostic techniques
Machine learning
Right ventricle
Right ventricular volume and ejection fraction
Three-dimensional echocardiography
Journal
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
ISSN: 1097-6795
Titre abrégé: J Am Soc Echocardiogr
Pays: United States
ID NLM: 8801388
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
21
01
2019
revised:
03
04
2019
accepted:
03
04
2019
pubmed:
9
6
2019
medline:
15
12
2020
entrez:
9
6
2019
Statut:
ppublish
Résumé
Three-dimensional echocardiography (3DE) allows accurate and reproducible measurements of right ventricular (RV) size and function. However, widespread implementation of 3DE in routine clinical practice is limited because the existing software packages are relatively time-consuming and skill demanding. The aim of this study was to test the accuracy and reproducibility of new machine learning- (ML-) based, fully automated software for three-dimensional quantification of RV size and function. Fifty-six unselected patients with a wide range of RV size and function and image quality, referred for clinically indicated cardiac magnetic resonance (CMR) imaging, underwent a transthoracic 3DE exam on the same day. End-systolic and end-diastolic RV volumes (ESV, EDV) and ejection fraction (EF) were measured using the ML-based algorithm and compared with CMR reference values using Bland-Altman and linear regression analyses. RV function quantification by echocardiography was feasible in all patients. The automatic approach was accurate in 32% patients with analysis time of 15 ± 1 seconds and 100% reproducible. Endocardial contour editing was necessary after the automated postprocessing in the remaining 68% patients, prolonging analysis time to 114 ± 71 seconds. With these minimal adjustments, RV volumes and EF measurements were accurate in comparison with CMR reference (biases: EDV, -25.6 ± 21.1 mL; ESV, -7.4 ± 16 mL; EF, -3.3% ± 5.2%) and showed excellent reproducibility reflected by coefficients of variation <7% and intraclass correlations ≥0.95 for all measurements. The new ML-based 3DE algorithm provided accurate and completely reproducible RV volume and EF measurements in one-third of unselected patients without any boundary editing. In the remaining patients, quick minimal editing resulted in reasonably accurate measurements with excellent reproducibility. This approach provides a promising solution for fast three-dimensional quantification of RV size and function.
Sections du résumé
BACKGROUND
Three-dimensional echocardiography (3DE) allows accurate and reproducible measurements of right ventricular (RV) size and function. However, widespread implementation of 3DE in routine clinical practice is limited because the existing software packages are relatively time-consuming and skill demanding. The aim of this study was to test the accuracy and reproducibility of new machine learning- (ML-) based, fully automated software for three-dimensional quantification of RV size and function.
METHODS
Fifty-six unselected patients with a wide range of RV size and function and image quality, referred for clinically indicated cardiac magnetic resonance (CMR) imaging, underwent a transthoracic 3DE exam on the same day. End-systolic and end-diastolic RV volumes (ESV, EDV) and ejection fraction (EF) were measured using the ML-based algorithm and compared with CMR reference values using Bland-Altman and linear regression analyses.
RESULTS
RV function quantification by echocardiography was feasible in all patients. The automatic approach was accurate in 32% patients with analysis time of 15 ± 1 seconds and 100% reproducible. Endocardial contour editing was necessary after the automated postprocessing in the remaining 68% patients, prolonging analysis time to 114 ± 71 seconds. With these minimal adjustments, RV volumes and EF measurements were accurate in comparison with CMR reference (biases: EDV, -25.6 ± 21.1 mL; ESV, -7.4 ± 16 mL; EF, -3.3% ± 5.2%) and showed excellent reproducibility reflected by coefficients of variation <7% and intraclass correlations ≥0.95 for all measurements.
CONCLUSIONS
The new ML-based 3DE algorithm provided accurate and completely reproducible RV volume and EF measurements in one-third of unselected patients without any boundary editing. In the remaining patients, quick minimal editing resulted in reasonably accurate measurements with excellent reproducibility. This approach provides a promising solution for fast three-dimensional quantification of RV size and function.
Identifiants
pubmed: 31174940
pii: S0894-7317(19)30197-X
doi: 10.1016/j.echo.2019.04.001
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
969-977Informations de copyright
Copyright © 2019 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.