Augmenting expert detection of early coronary artery occlusion from 12 lead electrocardiograms using deep learning

Early diagnosis of acute coronary artery occlusion based on electrocardiogram (ECG) findings is essential for prompt delivery of primary percutaneous coronary intervention. Current ST elevation (STE) criteria are specific but insensitive. Consequently, it is likely that many patients are missing out...

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Bibliographic Details
Published inarXiv.org
Main Authors Brisk, Rob, Raymond R Bond Dewar D Finlay, McLaughlin, James, Piadlo, Alicja, Leslie, Stephen J, Gossman, David E, Menown, Ian B A, McEneaney, David J
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.11.2019
Subjects
Change detection
Coronary vessels
Deep learning
Echocardiography
Electrocardiography
Occlusion
Sensitivity analysis
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Summary:Early diagnosis of acute coronary artery occlusion based on electrocardiogram (ECG) findings is essential for prompt delivery of primary percutaneous coronary intervention. Current ST elevation (STE) criteria are specific but insensitive. Consequently, it is likely that many patients are missing out on potentially life-saving treatment. Experts combining non-specific ECG changes with STE detect ischaemia with higher sensitivity, but at the cost of specificity. We show that a deep learning model can detect ischaemia caused by acute coronary artery occlusion with a better balance of sensitivity and specificity than STE criteria, existing computerised analysers or expert cardiologists.
ISSN:2331-8422