An automatic and real-time echocardiography quality scoring system based on deep learning to improve reproducible assessment of left ventricular ejection fraction.

ABSTRACT:

Background

Echocardiography can conveniently, rapidly, and economically evaluate the structure and function of the heart, and has important value in the diagnosis and evaluation of cardiovascular diseases (CVDs). However, echocardiography still exhibits significant variability in image acquisition and diagnosis, with a heavy dependency on the operator's experience. Image quality affects disease diagnosis in the later stage, and even image quality assessment still has variability in human evaluation. This study aimed to develop an automated and real-time quality assessment system using deep learning (DL) techniques while decreasing the measurement error of left ventricular ejection fraction (LVEF).

Methods

This study involved over 5,000 echocardiography datasets from 2,461 pa

SUBMITTER: Li X 

PROVIDER: S-EPMC11744150 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature