Stowell, Catherine C., Howard, James P., Ng, Tiffany, Cole, Graham D., Bhattacharyya, Sanjeev, Sehmi, Jobanpreet, Alzetani, Maysaa, Demetrescu, Camelia D., Hartley, Adam, Singh, Amar, Ghosh, Arjun, Vimalesvaran, Kavitha, Mangion, Kenneth, Rajani, Ronak, Rana, Bushra S., Zolgharni, Massoud, Francis, Darrel P. and Shun-Shin, Matthew J. ORCID: https://orcid.org/0000-0002-1179-0867 (2024) 2-Dimensional Echocardiographic Global Longitudinal Strain With Artificial Intelligence Using Open Data From a UK-Wide Collaborative. JACC: Cardiovascular Imaging, 17 (8). pp. 865-876. ISSN 1936-878X

Preview

PDF stowell-et-al-2024-2-dimensional-echocardiographic-global-longitudinal-strain-with-artificial-intelligence-using-open.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Background
Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake.
Objectives
The authors developed open machine-learning–based GLS methodology and validate it using multiexpert consensus from the Unity UK Echocardiography AI Collaborative.
Methods
We trained a multi-image neural network (Unity-GLS) to identify annulus, apex, and endocardial curve on 6,819 apical 4-, 2-, and 3-chamber images. The external validation dataset comprised those 3 views from 100 echocardiograms. End-systolic and -diastolic frames were each labelled by 11 experts to form consensus tracings and points. They also ordered the echocardiograms by visual grading of longitudinal function. One expert calculated global strain using 2 proprietary packages.
Results
The median GLS, averaged across the 11 individual experts, was −16.1 (IQR: −19.3 to −12.5). Using each case’s expert consensus measurement as the reference standard, individual expert measurements had a median absolute error of 2.00 GLS units. In comparison, the errors of the machine methods were: Unity-GLS 1.3, proprietary A 2.5, proprietary B 2.2. The correlations with the expert consensus values were for individual experts 0.85, Unity-GLS 0.91, proprietary A 0.73, proprietary B 0.79. Using the multiexpert visual ranking as the reference, individual expert strain measurements found a median rank correlation of 0.72, Unity-GLS 0.77, proprietary A 0.70, and proprietary B 0.74.
Conclusions
Our open-source approach to calculating GLS agrees with experts’ consensus as strongly as the individual expert measurements and proprietary machine solutions. The training data, code, and trained networks are freely available online.

Item Type:	Article
Identifier:	10.1016/j.jcmg.2024.04.017
Keywords:	artificial intelligenceglobal longitudinal strainechocardiography
Date Deposited:	01 Apr 2026
URI:	https://repository.uwl.ac.uk/id/eprint/14805

Downloads

Actions (admin access)