Development and validation of a deep learning pipeline to measure pericardial effusion in echocardiography - Cheng, Chi-Yung; Wu, Cheng-Ching; Chen, Huang-Chung; Hung, Chun-Hui; Chen, Tien-Yu; Lin, Chun-Hung Richard; Chiu, I-Min (Frontiers in Cardiovascular Medicine, 2023)
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