ArticleComputing in cardiology2024
Machine Learning Estimation of Myocardial Ischemia Severity Using Body Surface ECG.
Article in Computing in cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
Funding
Abstract
Acute myocardial ischemia (AMI) is one of the leading causes of cardiovascular deaths around the globe. Yet, clinical early detection and patient risk stratification of AMI remain an unmet need, in part due to poor performance of traditional electrocardiogram (ECG) interpretation. Machine learning (ML) techniques have shown promise in analysis of ECGs, even detecting cardiac diseases not identifiable via traditional analysis. However, there has been limited usage of ML tools in the case of AMI due to a lack of high-quality training data, especially detailed ECG recordings throughout the evolution of ischemic events. In this study, we applied ML to predict the ischemic tissue volume directly from body surface ECGs in an AMI animal model. The developed ML networks performed favorably, with an average R
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Registered trials
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