Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study.
Purpose: Echocardiography (echo) is widely used for right ventricular (RV) assessment. Current techniques for RV evaluation require additional imaging and manual analysis; machine learning (ML) approaches have the potential to provide efficient, fully automated quantification of RV function. Methods...
| Published in: | Echocardiography Vol. 37; no. 5; pp. 688 - 698 |
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| Main Authors: | , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
May2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143431717&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143431717 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07422822 GSE jtl: Echocardiography issn: 07422822 maglogo: Y pubinfo: dt: May2020 vid: 37 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 143431717 143431717 145862586 143431717 10.1111/echo.14674 143431717 ppf: 688 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study. aug: au: Beecy, Ashley N. Bratt, Alex Yum, Brian Sultana, Razia Das, Mukund Sherifi, Ines Devereux, Richard B. Weinsaft, Jonathan W. Kim, Jiwon affil: Greenberg Cardiology Division, Department of Medicine, Weill Cornell Medicine, New York NY,, USA sug: subj: Program Development Machine Learning Echocardiography Models, Theoretical Human ab: Purpose: Echocardiography (echo) is widely used for right ventricular (RV) assessment. Current techniques for RV evaluation require additional imaging and manual analysis; machine learning (ML) approaches have the potential to provide efficient, fully automated quantification of RV function. Methods: An automated ML model was developed to track the tricuspid annulus on echo using a convolutional neural network approach. The model was trained using 7791 image frames, and automated linear and circumferential indices quantifying annular displacement were generated. Automated indices were compared to an independent reference of cardiac magnetic resonance (CMR) defined RV dysfunction (RVEF < 50%). Results: A total of 101 patients prospectively underwent echo and CMR: Fully automated annular tracking was uniformly successful; analyses entailed minimal processing time (<1 second for all) and no user editing. Findings demonstrate all automated annular shortening indices to be lower among patients with CMR‐quantified RV dysfunction (all P <.001). Magnitude of ML annular displacement decreased stepwise in relation to population‐based tertiles of TAPSE, with similar results when ML analyses were localized to the septal or lateral annulus (all P ≤.001). Automated segmentation techniques provided good diagnostic performance (AUC 0.69–0.73) in relation to CMR reference and compared to conventional RV indices (TAPSE and S′) with high negative predictive value (NPV 84%–87% vs 83%–88%). Reproducibility was higher for ML algorithm as compared to manual segmentation with zero inter‐ and intra‐observer variability and ICC 1.0 (manual ICC: 0.87–0.91). Conclusions: This study provides an initial validation of a deep learning system for RV assessment using automated tracking of the tricuspid annulus. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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