Machine Learning–Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance

Davide Genovese, Nina Rashedi, Lynn Weinert, A. Narang, Karima Addetia, Amit R. Patel, D. Prater, Alexandra Gonçalves, Victor Mor-Avi, Roberto M. Lang*

*Corresponding author for this work

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)969-977
Number of pages9
JournalJournal of the American Society of Echocardiography
Volume32
Issue number8
DOIs
StatePublished - Aug 2019

Keywords

  • Artificial intelligence
  • Diagnostic techniques
  • Machine learning
  • Right ventricle
  • Right ventricular volume and ejection fraction
  • Three-dimensional echocardiography

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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