AUTHOR=Cheng Chi-Yung , Wu Cheng-Ching , Chen Huang-Chung , Hung Chun-Hui , Chen Tien-Yu , Lin Chun-Hung Richard , Chiu I-Min 

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

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1195235

DOI=10.3389/fcvm.2023.1195235

ISSN=2297-055X

ABSTRACT=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.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