ArticleJACC. Cardiovascular imaging2025
Ultrasonic Texture Analysis for Predicting Acute Myocardial Infarction.
Article in JACC. Cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deep Survival Modeling With Echocardiographic Myocardial Texture Radiomics for Prediction of Major Adverse Cardiovascular Events in Hypertrophic Cardiomyopathy.Journal of cardiovascular translational research · 2026Article
- Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2026Review
- The unmet promise: a critical review of antioxidant strategies in myocardial ischemia-reperfusion injury and the path towards precision medicine.Frontiers in pharmacology · 2025Review
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7 authors.
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Abstract
backgroundAcute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium.
objectivesThis study examines ultrasomics-a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images-for identifying ultrasonic texture and morphologic changes associated with infarcted myocardium.
methodsThe authors included 684 participants from multisource data: a) a retrospective single-center matched case-control dataset; b) a prospective multicenter matched clinical trial dataset; and c) an open-source international and multivendor dataset. Handcrafted and deep transfer learning-based ultrasomics features from 2- and 4-chamber echocardiographic views were used to train machine learning (ML) models with the use of leave-one-source-out cross-validation for external validation.
resultsThe ML model showed a higher AUC than transfer learning-based deep features in identifying MI (AUC: 0.87 [95% CI: 0.84-0.89] vs AUC: 0.74 [95% CI: 0.70-0.77]; P < 0.0001). ML probability was an independent predictor of MI even after adjusting for conventional echocardiographic parameters (adjusted OR: 1.03 [95% CI: 1.01-1.05]; P < 0.0001). ML probability showed diagnostic value in differentiating acute MI, even in the presence of myocardial dysfunction (averaged longitudinal strain [LS] <16%) (AUC: 0.84 [95% CI: 0.77-0.89]). In addition, combining averaged LS with ML probability significantly improved predictive performance compared with LS alone (AUC: 0.86 [95% CI: 0.80-0.91] vs AUC: 0.80 [95% CI: 0.72-0.87]; P = 0.02). Visualization of ultrasomics features with the use of a Manhattan plot discriminated infarcted and noninfarcted segments (P < 0.001) and facilitated parametric visualization of infarcted myocardium.
conclusionsThis study demonstrates the potential of cardiac ultrasomics to distinguish healthy from infarcted myocardium and highlights the need for validation in diverse populations to define its role and incremental value in myocardial tissue characterization beyond conventional echocardiography.
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