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Updated: Jan 5, 2026
Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type
Jason Leo Walsh1, Wael A AlJaroudi2, Nader Lamaa1
1Vascular Medicine Program, Division of Cardiology, American University of Beirut Medical Center, Beirut, Lebanon.
Insights
Artificial neural network (ANN) models accurately differentiate heart failure causes using echocardiographic strain. These models show promise for improving diagnosis of ischaemic versus non-ischaemic cardiomyopathy.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Invasive angiography is standard for differentiating ischaemic and non-ischaemic cardiomyopathy in heart failure.
- Accurate differentiation is crucial for guiding appropriate treatment strategies.
- Non-invasive methods to predict cardiomyopathy aetiology are highly desirable.
Purpose of the Study:
- To evaluate the predictive capability of echocardiographic strain features for differentiating ischaemic from non-ischaemic cardiomyopathy.
- To compare the performance of artificial neural network (ANN) models against logistic regression.
- To assess the combined utility of strain parameters with other clinical and electrocardiographic features.
Main Methods:
- Retrospective analysis of 204 heart failure patients with ejection fraction <50% and diagnostic angiograms.
- Development of logistic regression and ANN models using echocardiographic strain, regional wall motion abnormalities, ECG, and demographic features.
- Models were trained on a derivation cohort and validated on a separate validation cohort.
Main Results:
- A strain-based ANN model achieved an F1 score of 0.82.
- A full feature ANN model achieved an F1 score of 0.79.
- The full feature logistic regression model yielded an F1 score of 0.63.
Conclusions:
- ANN models demonstrated superior accuracy in predicting cardiomyopathy type compared to logistic regression.
- The strain-based ANN model shows significant potential for aiding heart failure aetiology diagnosis.
- Further validation in diverse cohorts and integration into clinical tools like online calculators or echocardiogram software are recommended.
Abstract:
Objectives. In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differentiate ischaemic from non-ischaemic cardiomyopathy, using artificial neural network (ANN) and logistic regression modelling. Design. We retrospectively identified 204 consecutive patients with an ejection fraction <50% and a diagnostic angiogram. Patients were categorized as either ischaemic (n = 146) or non-ischaemic cardiomyopathy (n = 58). For each patient, left ventricular strain parameters were obtained. Additionally, regional wall motion abnormality, 13 electrocardiographic (ECG) features and six demographic features were retrieved for analysis. The entire cohort was randomly divided into a derivation and a validation cohort. Using the parameters retrieved, logistic regression and ANN models were developed in the derivation cohort to differentiate ischaemic from non-ischaemic cardiomyopathy, the models were then tested in the validation cohort. Results. A final strain-based ANN model, full feature ANN model and full feature logistic regression model were developed and validated, F1 scores were 0.82, 0.79 and 0.63, respectively. Conclusions. Both ANN models were more accurate at predicting cardiomyopathy type than the logistic regression model. The strain-based ANN model should be validated in other cohorts. This model or similar models could be used to aid the diagnosis of underlying heart failure aetiology in the form of the online calculator (https://cimti.usj.edu.lb/strain/index.html) or built into echocardiogram software.
