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.
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.
Authors: A Malagoli; A Albini; G E Mandoli; A Baggiano; G Vinco; F Bandera; A D'Andrea; R Esposito; F D'Ascenzi; R Sorrentino; C Santoro; G Benfari; F Contorni; M Cameli Journal: Int J Cardiovasc Imaging Date: 2021-06-10 Impact factor: 2.357
Authors: Akhil Narang; Richard Bae; Ha Hong; Yngvil Thomas; Samuel Surette; Charles Cadieu; Ali Chaudhry; Randolph P Martin; Patrick M McCarthy; David S Rubenson; Steven Goldstein; Stephen H Little; Roberto M Lang; Neil J Weissman; James D Thomas Journal: JAMA Cardiol Date: 2021-06-01 Impact factor: 14.676
Authors: Andrew Lin; Márton Kolossváry; Ivana Išgum; Pál Maurovich-Horvat; Piotr J Slomka; Damini Dey Journal: Expert Rev Med Devices Date: 2020-06-16 Impact factor: 3.166
Authors: Ashley N Beecy; Alex Bratt; Brian Yum; Razia Sultana; Mukund Das; Ines Sherifi; Richard B Devereux; Jonathan W Weinsaft; Jiwon Kim Journal: Echocardiography Date: 2020-05-12 Impact factor: 1.724
Authors: Matthias Schneider; Philipp Bartko; Welf Geller; Varius Dannenberg; Andreas König; Christina Binder; Georg Goliasch; Christian Hengstenberg; Thomas Binder Journal: Int J Cardiovasc Imaging Date: 2020-10-08 Impact factor: 2.357