Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.
Background: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evalua...
| Publicado en: | Echocardiography Vol. 41; no. 4; pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177589670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177589670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07422822 GSE jtl: Echocardiography issn: 07422822 maglogo: Y pubinfo: dt: Apr2024 vid: 41 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 177589670 177589670 177589670 10.1111/echo.15812 177589670 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension. aug: au: Michito Murayama Hiroyuki Sugimori Takaaki Yoshimura Sanae Kaga Hideki Shima Satonori Tsuneta Aoi Mukai Yui Nagai Shinobu Yokoyama Hisao Nishino Junichi Nakamura Takahiro Sato Ichizo Tsujino affil: Department of Medical Laboratory Science, Faculty of Health Sciences, Hokkaido University, Sapporo, Japan sug: subj: Deep Learning Ventricular Ejection Fraction Evaluation Ventricular Function, Right Echocardiography Methods Pulmonary Arterial Hypertension Human Automation Magnetic Resonance Imaging Regression Artificial Intelligence Descriptive Statistics ab: Background: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)-based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four-chamber views in patients with precapillary PH. Methods: We identified 85 patients with suspected precapillary PH who underwent cardiac magnetic resonance imaging (MRI) and echocardiography. The data was divided into training (80%) and testing (20%) datasets, and a regression model was constructed using 3D-ResNet50. Accuracy was assessed using five-fold cross validation. Results: The DL model predicted the cardiac MRI-derived RVEF with a mean absolute error of 7.67%. The DL model identified severe RV systolic dysfunction (defined as cardiacMRI-derived RVEF < 37%) with an area under the curve (AUC) of .84, which was comparable to the AUC of RV fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE) measured by experienced sonographers (.87 and .72, respectively). To detect mild RV systolic dysfunction (defined as RVEF ≤ 45%), the AUC from the DL-predicted RVEF also demonstrated a high discriminatory power of .87, comparable to that of FAC (.90), and significantly higher than that of TAPSE (.67). Conclusion: The fully automated DL-based tool using 2D echocardiography could accurately estimate RVEF and exhibited a diagnostic performance for RV systolic dysfunction comparable to that of human readers. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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