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Original Articles

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type

Jason Leo WalshVascular Medicine Program, Division of Cardiology, American University of Beirut Medical Center, Beirut, Lebanon;

Wael A. AlJaroudiDivision of Cardiovascular Medicine, Clemenceau Medical Center, Beirut, Lebanon;

Nader LamaaVascular Medicine Program, Division of Cardiology, American University of Beirut Medical Center, Beirut, Lebanon;

Ossama K. Abou HassanVascular Medicine Program, Division of Cardiology, American University of Beirut Medical Center, Beirut, Lebanon;

http://orcid.org/0000-0003-2618-4285

Khalil JalkhVascular Medicine Program, Division of Cardiology, American University of Beirut Medical Center, Beirut, Lebanon;

Imad H. ElhajjDepartment of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon;

George SakrComputer Engineering Department, St Joseph University of Beirut, Beirut, LebanonCorrespondence[email protected]

Hussain Isma’eelVascular Medicine Program, Division of Cardiology, American University of Beirut Medical Center, Beirut, Lebanon; Correspondence[email protected]

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Table 1. Demographic and electrocardiographic variables used in predictive models.

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Table 2. Echocardiographic variables used in predictive models.

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Figure 1. Sensitivity versus 1-specificity for strain-based artificial neural network (ANN) model to predict ischaemic cardiomyopathy in systolic heart failure.

Figure 2. ROC curve for the strain-based ANN model.

Table 3. Accuracy of predictive models.

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Figure 3. Diagnostic angiograms avoided in relationship with model sensitivity. The number of diagnostic angiograms avoided decreased exponentially as the sensitivity of the test increased.

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A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type

Table 1. Demographic and electrocardiographic variables used in predictive models.

Table 2. Echocardiographic variables used in predictive models.

Table 3. Accuracy of predictive models.

Supplemental Material

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A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type

Figures & data

Table 1. Demographic and electrocardiographic variables used in predictive models.

Table 2. Echocardiographic variables used in predictive models.

Table 3. Accuracy of predictive models.

Supplemental Material

Reprints and Corporate Permissions

Academic Permissions

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