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Updated: Jun 26, 2025
Ultrasonic Assessment of Myocardial Microstructure
Published on: January 14, 2014
The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images
Błażej Michalski1, Sławomir Skonieczka2, Michał Strzelecki2
11st Department and Chair of Cardiology, Medical University of Lodz, Poland. bwmichalski@op.pl.
Insights
Artificial intelligence enhances echocardiography for diagnosing myocardial viability after acute coronary syndrome. AI analysis of echo images aids in predicting heart function recovery and scar extent.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Acute coronary syndrome (ACS) diagnosis and management require accurate assessment of myocardial viability and function recovery.
- Standard echocardiography is a key imaging modality, but its diagnostic capabilities can be enhanced with advanced analytical techniques.
Purpose of the Study:
- To evaluate the efficacy of artificial intelligence (AI) in analyzing echocardiographic images for myocardial viability and function recovery prediction post-ACS.
- To compare AI-based texture analysis with cardiac magnetic resonance (CMR) for assessing necrosis extent and predicting viability.
Main Methods:
- Sixty-one ACS patients underwent percutaneous coronary intervention (PCI) and subsequent echocardiographic and CMR evaluations.
- Texture analysis of 533 heart echo segments was performed using custom software and machine learning techniques (ANN, SVM, Adaboost).
- AI methods were correlated with CMR findings for necrosis extent and viability prediction after 12 months.
Main Results:
- The concordance between AI classification models and CMR for viability ranged from 42% to 76%.
- AI-based echo analysis showed higher sensitivity in detecting non-viable tissue with significant transmural scar thickness.
- Contrast enhancement in echocardiography improved prediction accuracy to 74% for viable tissue detection.
Conclusions:
- AI-based analysis of echocardiographic images enables feasible detection and semi-quantification of myocardial scar transmurality.
- Selected AI methods demonstrate comparable accuracy in predicting myocardial viability.
- Contrast-enhanced echocardiography, analyzed with AI, significantly contributes to predicting myocardial viability 12 months post-myocardial infarction.
Background:
Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome.
Methods:
Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm.
Results:
The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images.
Conclusions:
Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.
