Literature DB >> 28159984

Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study.

Diego Medvedofsky1, Victor Mor-Avi1, Mihaela Amzulescu2, Covadonga Fernández-Golfín3, Rocio Hinojar3, Mark J Monaghan4, Kyoko Otani5, Joseph Reiken4, Masaaki Takeuchi5, Wendy Tsang6, Jean-Louis Vanoverschelde2, Mathivathana Indrajith4, Lynn Weinert1, Jose Luis Zamorano3, Roberto M Lang1.   

Abstract

Aims: Although recommended by current guidelines, adoption of three-dimensional echocardiographic (3DE) chamber quantification in clinical practice has lagged because of time-consuming analysis. We recently validated an automated algorithm that measures left atrial (LA) and left ventricular (LV) volumes and ejection fraction (EF). This study aimed to determine the accuracy and reproducibility of these measurements in a multicentre setting. Methods and results: 180 patients underwent 3DE imaging (Philips) at six sites. Images were analysed using automated HeartModel (HM) software with endocardial border correction when necessary and by manual tracing. Measurements were performed by each site and by the Core Laboratory (CL) as the reference. Inter-technique comparisons included HM measurements by the sites against manual tracing by CL, and showed strong correlations (r-values: LVEDV: 0.97, LVESV: 0.97, LVEF: 0.88, LAV: 0.96), with the automated technique slightly underestimating LV volumes (biases: LVEDV: -14 ± 20 ml, LVESV: -6 ± 20 ml), LVEF (-2 ± 7%) and LAV (-9 ± 10 ml). Intra-technique comparisons included HM measurements by the sites against CL, with and without corrections. Corrections were unnecessary or minimal in most patients, and improved the measurements only modestly. Comparisons without corrections showed perfect agreement for all parameters. With corrections, correlations were better (r-values: LVEDV: 0.99, LVESV: 0.99, LVEF: 0.94, LAV: 0.99) and biases (LVEDV: -8 ± 12 ml, LVESV: -6 ± 12 ml, LVEF: 1 ± 5%, LAV: -10 ± 6 ml) smaller than in inter-technique comparison. All automated measurements with corrections were more reproducible than manual measurements.
Conclusion: Automated 3DE analysis of left-heart chambers is an accurate alternative to conventional manual methodology, which yields almost the same values across laboratories and is more reproducible. This technique may contribute towards full integration of 3DE quantification into clinical routine. Published on behalf of the European Society of Cardiology. All rights reserved.
© The Author 2017. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  3D echocardiography; automated analysis; cardiac chamber quantification; multicentre study

Mesh:

Year:  2018        PMID: 28159984     DOI: 10.1093/ehjci/jew328

Source DB:  PubMed          Journal:  Eur Heart J Cardiovasc Imaging        ISSN: 2047-2404            Impact factor:   6.875


  20 in total

Review 1.  Artificial Intelligence and Machine Learning in Cardiovascular Imaging.

Authors:  Karthik Seetharam; James K Min
Journal:  Methodist Debakey Cardiovasc J       Date:  2020 Oct-Dec

2.  Machine learning based automated dynamic quantification of left heart chamber volumes.

Authors:  Akhil Narang; Victor Mor-Avi; Aldo Prado; Valentina Volpato; David Prater; Gloria Tamborini; Laura Fusini; Mauro Pepi; Neha Goyal; Karima Addetia; Alexandra Gonçalves; Amit R Patel; Roberto M Lang
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2019-05-01       Impact factor: 6.875

3.  Three-dimensional full automated software in the evaluation of the left ventricle function: from theory to clinical practice.

Authors:  Valentina Barletta; Rocio Hinojar; Alejandra Carbonell; Ariana González-Gómez; Iacopo Fabiani; Vitantonio Di Bello; José Julio Jiménez-Nacher; José Zamorano; Covadonga Fernández-Golfín
Journal:  Int J Cardiovasc Imaging       Date:  2018-03-31       Impact factor: 2.357

4.  The Role of Automated 3D Echocardiography for Left Ventricular Ejection Fraction Assessment.

Authors:  Ernest Spitzer; Ben Ren; Felix Zijlstra; Nicolas M Van Mieghem; Marcel L Geleijnse
Journal:  Card Fail Rev       Date:  2017-11

5.  Three-Dimensional Echocardiographic Automated Quantification of Left Heart Chamber Volumes Using an Adaptive Analytics Algorithm: Feasibility and Impact of Image Quality in Nonselected Patients.

Authors:  Diego Medvedofsky; Victor Mor-Avi; Isida Byku; Amita Singh; Lynn Weinert; Megan Yamat; Eric Kruse; Boguslawa Ciszek; Alma Nelson; Kyoko Otani; Masaaki Takeuchi; Roberto M Lang
Journal:  J Am Soc Echocardiogr       Date:  2017-07-06       Impact factor: 5.251

6.  A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features.

Authors:  Ali Ahmad; Zahi Ibrahim; Georges Sakr; Abdallah El-Bizri; Lara Masri; Imad H Elhajj; Nehme El-Hachem; Hussain Isma'eel
Journal:  Cardiovasc Diagn Ther       Date:  2020-08

Review 7.  Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.

Authors:  Ikram U Haq; Karanjot Chhatwal; Krishna Sanaka; Bo Xu
Journal:  Vasc Health Risk Manag       Date:  2022-07-12

8.  Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

Authors:  Federico M Asch; Nicolas Poilvert; Theodore Abraham; Madeline Jankowski; Jayne Cleve; Michael Adams; Nathanael Romano; Ha Hong; Victor Mor-Avi; Randolph P Martin; Roberto M Lang
Journal:  Circ Cardiovasc Imaging       Date:  2019-09-16       Impact factor: 7.792

9.  Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.

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

Review 10.  Artificial intelligence: improving the efficiency of cardiovascular imaging.

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

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