External validation of a deep learning algorithm for automated echocardiographic strain measurements.

ABSTRACT:

Aims

Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging.

Methods and results

We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myoca

SUBMITTER: Myhre PL 

PROVIDER: S-EPMC10802824 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature