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Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms

Authors
 Sekeun Kim  ;  Hyung-Bok Park  ;  Jaeik Jeon  ;  Reza Arsanjani  ;  Ran Heo  ;  Sang-Eun Lee  ;  Inki Moon  ;  Sun Kook Yoo  ;  Hyuk-Jae Chang 
Citation
 INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING, Vol.38(5) : 1047-1059, 2022-05 
Journal Title
INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
ISSN
 1569-5794 
Issue Date
2022-05
Keywords
Deep learning ; Echocardiography ; Fully automated
Abstract
We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.
Full Text
https://link.springer.com/article/10.1007/s10554-021-02482-y
DOI
10.1007/s10554-021-02482-y
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Park, Hyung Bok(박형복)
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
Lee, Sang-Eun(이상은) ORCID logo https://orcid.org/0000-0001-6645-4038
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Heo, Ran(허란)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/189390
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