Deep learning improves test-retest reproducibility of regional strain in echocardiography.

ABSTRACT:

Aims

The clinical utility of regional strain measurements in echocardiography is challenged by suboptimal reproducibility. In this study, we aimed to evaluate the test-retest reproducibility of regional longitudinal strain (RLS) per coronary artery perfusion territory (RLS_Territory) and basal-to-apical level of the left ventricle (RLS_Level), measured by a novel fully automated deep learning (DL) method based on point tracking.

Methods and results

We measured strain in a dual-centre test-retest data set that included 40 controls and 40 patients with suspected non-ST elevation acute coronary syndrome. Two consecutive echocardiograms per subject were recorded by different operators. The reproducibility of RLS_Territory and RLS_Level mea

SUBMITTER: Nyberg J 

PROVIDER: S-EPMC11498295 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature