Abstract
Objective 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 infarcted myocardium.
Methodology A retrospective multicenter cohort of 380 participants was split into two groups: a model development cohort (n=296; 101 MI cases, 195 controls) and an external validation cohort (n=84; 40 MI cases, 44 controls). Handcrafted and transfer learning-derived deep ultrasomics features were extracted from 2-chamber and 4-chamber echocardiographic views and ML models were built to detect patients with MI and infarcted myocardium within individual views. Myocardial infarct localization via texture features was determined using Shapley additive explanations. All the ML models were trained using 10-fold cross-validation and assessed on an external test dataset, using the area under the curve (AUC).
Results The ML model, leveraging segment-level handcrafted ultrasomics features identified MI with AUCs of 0.93 (95% CI: 0.97-0.97) and 0.83 (95% CI: 0.74-0.89) at the patient-level and view-level, respectively. A model combining handcrafted and deep ultrasomics provided incremental information over deep ultrasomics alone (AUC: 0.79, 95% CI: 0.71-0.85 vs. 0.75, 95% CI: 0.66-0.82). Using a view-level ultrasomic model we identified texture features that effectively discriminated between infarcted and non-infarcted segments (p<0.001) and facilitated parametric visualization of infarcted myocardium.
Conclusion This pilot study highlights the potential of cardiac ultrasomics in distinguishing healthy and infarcted myocardium and opens new opportunities for advancing myocardial tissue characterization using echocardiography.
Competing Interest Statement
Dr. Sengupta is a consultant for RCE Technologies. Dr. Yanamala is an advisor to Research Spark Hub Inc., Turnkey Learning, LLC, and Turnkey Insights (I) Pvt Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Funding Statement
This work was supported by a National Science Foundation grant (Grant Number: 2125872).
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
Institutional Review Board (IRB) at Robert Wood Johnson University Hospital (RWJBH). Study is exempt under category 4(iii) with approval ID: FWA00003913
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Yes
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Footnotes
Disclosures: Dr. Sengupta is a consultant for RCE Technologies. Dr. Yanamala is an advisor to Research Spark Hub Inc., Turnkey Learning, LLC, and Turnkey Insights (I) Pvt Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Data Availability
All data produced in the present study are available upon reasonable request to the authors.