Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography.

ABSTRACT:

Objectives

The aim of this study was to develop a deep-learning pipeline for the measurement of pericardial effusion (PE) based on raw echocardiography clips, as current methods for PE measurement can be operator-dependent and present challenges in certain situations.

Methods

The proposed pipeline consisted of three distinct steps: moving window view selection (MWVS), automated segmentation, and width calculation from a segmented mask. The MWVS model utilized the ResNet architecture to classify each frame of the extracted raw echocardiography files into selected view types. The automated segmentation step then generated a mask for the PE area from the extracted echocardiography clip, and a computer vision technique was used to calculate the largest width of the PE from the s

SUBMITTER: Cheng CY 

PROVIDER: S-EPMC10436508 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature