Machine Learning–Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance

Genovese, Davide; Rashedi, Nina; Weinert, Lynn; Narang, Akhil; Addetia, Karima; Patel, Amit R.; Prater, David; Gonçalves, Alexandra; Mor‐Avi, Victor; Lang, Roberto M.

doi:10.1016/j.echo.2019.04.001

Cited by 85 publications

(74 citation statements)

References 25 publications

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“…More recently, ML‐based 3D echo algorithm to quantify RV volumes and RV ejection fraction was tested in a retrospective cohort 9 . Although quantification was feasible in all fifty‐six patients, the automatic approach was only accurate in 32% of the study population.…”

Section: Discussionmentioning

confidence: 99%

“…Conventional neural networks–based segmentation techniques have been applied to echo, though with focus primarily on left ventricular chamber size and systolic function quantification 8 . While a recent study examined a ML approach for three‐dimensional echocardiography (3DE) assessment of RV volume and EF, 9 limited clinical availability of 3DE is a known barrier for widespread utilization. Fully automated ML approaches have yet to be applied for RV assessment on standard 2D echo and have the potential to improve efficiency and accuracy without need for additional M‐mode, tissue velocity, or 3D image acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study

et al. 2020

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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 intraobserver 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. | 689BEECY Et al.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study

et al. 2020

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“…Once 3D images are acquired, automated postprocessing analysis generates RV volumes and EF that are comparable to MRI assessment. 10…”

Section: Three-dimensional (3d) Echocardiographymentioning

confidence: 99%

The Future of Imaging in Pulmonary Hypertension: Better Assessment of Structure, Function, and Flow

Narang

Freed

2019

Advances in Pulmonary Hypertension

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Cardiovascular imaging is essential in the evaluation and management of patients with pulmonary hypertension. Echocardiography and magnetic resonance imaging, in particular, provide basic measurements of pulmonary pressure and right ventricular function, but current technology allows for a much more comprehensive assessment. Many of these advancements have the potential to enhance risk stratification and provide additional phenotypic data that may strengthen or alter the therapeutic approach. This review will highlight multiple novel techniques from various imaging modalities and how this information can be applied in clinical practice.

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“…It involves training a computer to develop comprehensive algorithms through the analysis of large amounts of data rapidly, consistently, and accurately. ML models have been used in many aspects of echocardiography, including view classi cation [10][11][12], automated measurements [13][14][15][16][17][18], automated valve disease assessment [19][20][21][22], and the classi cation of pathological patterns [23,24]. Nonetheless, the application of ML models for the detection of MI using echocardiography is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

Deep Polar Residual Network for Identifying Myocardial Infarction in Mce Imaging

Shen

Xue

Guo

et al. 2020

Preprint

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Background: Ultrasonic diagnosis of myocardial infarction (MI) varies markedly among radiologists, and is also time-consuming. In addition, there is a high level of inter-observer variation in diagnosis of MI. The purpose of this study is to develop a deep learning-based model that automatically recognizes MI in myocardial contrast echocardiography(MCE) images, and to compare the model performance against radiologists with varying experience in echocardiography.Methods: In this study, a polar residual network (PResNet) model was employed to recognize MI segments in MCE images of Sprague-Dawley (SD) rats. All SD rats were randomly divided into the MI model group (n=15) and the sham group (n=10). Rats in MI model group were infarcted via left anterior descending coronary artery ligation. Ultimately, MCE was performed on SD rats to observe their myocardium. Left ventricle (LV) short-axis views of MCE at the basal and midpapillary muscle levels for each SD rat were divided into 6 segments separately. The PResNet model and six radiologists each diagnosed 276 MCE image segments. Masson’s trichrome staining was used as the gold standard for MI presence or absence. The PResNet model and radiologist diagnostic performance were evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Furthermore, analysis duration per rat and interobserver variability were also compared. Results: Masson’s trichrome stain revealed 30 infarcted segments out of the 276 total segments. The PResNet model achieved an AUC of 0.89 (0.85-0.93), which was comparable to that of senior radiologists (0.89; 0.85-0.93). Relative to senior radiologists, the PResNet model presented slightly lower sensitivity but significantly higher specificity. The mean sensitivity and specificity for the junior radiologists were significantly lower than both the PResNet model and the senior radiologists. In terms of analysis duration, the PResNet model (148-169sec) was markedly faster than both junior (314-366sec) and senior (193-235sec). Moreover, the intrasample agreement for the PResNet model (k=0.96) was better than the interobserver agreement for the junior (k=0.81) and senior radiologists (k=0.86). Conclusion: The PResNet model not only improved diagnostic performance and observer consensus but also improve the diagnose speed. It is a promising tool for identifying MI.

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Machine Learning–Based Three-Dimensional Echocardiographic Quantification of Right Ventricular Size and Function: Validation Against Cardiac Magnetic Resonance

Cited by 85 publications

References 25 publications

Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study

Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study

The Future of Imaging in Pulmonary Hypertension: Better Assessment of Structure, Function, and Flow

Deep Polar Residual Network for Identifying Myocardial Infarction in Mce Imaging

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