Real-time guidance and automated measurements using deep learning to improve echocardiographic assessment of left ventricular size and function.

ABSTRACT:

Aims

The low reproducibility of echocardiographic measurements challenges the identification of subtle changes in left ventricular (LV) function. Deep learning (DL) methods enable real-time analysis of acquisitions and may improve echocardiography. The aim of this study was to evaluate the impact of DL-based guidance and automated measurements on the reproducibility of LV global longitudinal strain (GLS), end-diastolic (EDV) and end-systolic (ESV) volume, and ejection fraction (EF).

Methods and results

Forty-six patients (24 breast cancer and 22 general cardiology patients) were included and underwent four consecutive echocardiograms. Six were included twice, totalling 52 inclusions and 208 echocardiograms. One sonographer-cardiologist pair used DL guidance and measurements

SUBMITTER: Sabo S 

PROVIDER: S-EPMC12311362 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature