Key result
Cardiac ultrasomics ML model improves acute MI detection over deep features with an AUC of ~0.87.
Why the study?
Does an ultrasomics-based machine learning model improve the identification of acute myocardial infarction in patients undergoing echocardiography?
Observational (n=684)
Yes
Does an ultrasomics-based machine learning model improve the identification of acute myocardial infarction in patients undergoing echocardiography?
Odds Ratio: 1.03 (95% CI 1.01–1.05)
p-value: p=< 0.0001
Cardiac ultrasomics using machine learning models significantly improves the identification of acute myocardial infarction from echocardiographic images compared to conventional parameters.
Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium. This 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. We 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. The ML model showed a higher AUC than transfer learning-based deep features in identifying MI [AUCs: 0.87 [95% CI: 0.84-0.89] vs 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 (AUCs: 0.86 [95% CI: 0.80-0.91] vs 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. This 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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Jamthikar et al. (2025) conducted an observational in Acute myocardial infarction (n=684). Machine learning model using cardiac ultrasomics features vs. Transfer learning-based deep features was evaluated on Predicting acute myocardial infarction (adjusted OR 1.03, 95% CI 1.01-1.05, p=< 0.0001). A machine learning model using cardiac ultrasomics features identified acute myocardial infarction with a higher AUC than deep features (0.87 vs 0.74; P<0.0001).
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