Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Oct;14(10):1918-1928.
doi: 10.1016/j.jcmg.2021.04.018. Epub 2021 Jun 16.

Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography

Ivar M Salte  1 , Andreas Østvik  2 , Erik Smistad  2 , Daniela Melichova  3 , Thuy Mi Nguyen  1 , Sigve Karlsen  4 , Harald Brunvand  4 , Kristina H Haugaa  5 , Thor Edvardsen  5 , Lasse Lovstakken  2 , Bjørnar Grenne  6
Affiliations
Free article

Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography

Ivar M Salte et al. JACC Cardiovasc Imaging. 2021 Oct.
Free article

Abstract

Objectives: This study sought to examine if fully automated measurements of global longitudinal strain (GLS) using a novel motion estimation technology based on deep learning and artificial intelligence (AI) are feasible and comparable with a conventional speckle-tracking application.

Background: GLS is an important parameter when evaluating left ventricular function. However, analyses of GLS are time consuming and demand expertise, and thus are underused in clinical practice.

Methods: In this study, 200 patients with a wide range of left ventricle (LV) function were included. Three standard apical cine-loops were analyzed using the AI pipeline. The AI method measured GLS and was compared with a commercially available semiautomatic speckle-tracking software (EchoPAC v202, GE Healthcare.

Results: The AI method succeeded to both correctly classify all 3 standard apical views and perform timing of cardiac events in 89% of patients. Furthermore, the method successfully performed automatic segmentation, motion estimates, and measurements of GLS in all examinations, across different cardiac pathologies and throughout the spectrum of LV function. GLS was -12.0 ± 4.1% for the AI method and -13.5 ± 5.3% for the reference method. Bias was -1.4 ± 0.3% (95% limits of agreement: 2.3 to -5.1), which is comparable with intervendor studies. The AI method eliminated measurement variability and a complete GLS analysis was processed within 15 s.

Conclusions: Through the range of LV function this novel AI method succeeds, without any operator input, to automatically identify the 3 standard apical views, perform timing of cardiac events, trace the myocardium, perform motion estimation, and measure GLS. Fully automated measurements based on AI could facilitate the clinical implementation of GLS.

Keywords: artificial intelligence; artificial neural networks; deep learning; echocardiography; global longitudinal strain; machine learning.

PubMed Disclaimer

Conflict of interest statement

Funding Support and Author Disclosures This work was supported by the Research Council of Norway (Project number 237887), Norwegian Health Association, South-Eastern Norway regional health authority, national program for clinical therapy research (project number 2017207), and Centre for Innovative Ultrasound Solutions, a Norwegian Research Council center for research-based innovation. All authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Comment in

  • Let AI Take the Strain.
    Leeson P, Fletcher AJ. Leeson P, et al. JACC Cardiovasc Imaging. 2021 Oct;14(10):1929-1931. doi: 10.1016/j.jcmg.2021.05.012. Epub 2021 Jun 16. JACC Cardiovasc Imaging. 2021. PMID: 34147450 No abstract available.

Publication types

LinkOut - more resources