Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning

Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constrained settings, electrocardiograms (ECGs) are a cos...

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Veröffentlicht in:Scientific reports 2024-03, Vol.14 (1), p.7523-7523, Article 7523
Hauptverfasser: Boribalburephan, Atirut, Treewaree, Sukrit, Tantisiriwat, Noppawat, Yindeengam, Ahthit, Achakulvisut, Titipat, Krittayaphong, Rungroj
Format: Artikel
Sprache:eng
Schlagworte:
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Cardiology
Cardiovascular disease
Cicatrix - diagnostic imaging
Classification
Computer vision
Datasets
Deep Learning
Ejection fraction
EKG
Electrocardiography
Electrocardiography - methods
Heart
Humanities and Social Sciences
Humans
Magnetic Resonance Imaging, Cine
multidisciplinary
Scars
Science
Science (multidisciplinary)
Stroke Volume
Ventricle
Ventricular Function, Left
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Zusammenfassung:Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constrained settings, electrocardiograms (ECGs) are a cost-effective alternative. We developed computer vision-based multi-task deep learning models to analyze 12-lead ECG 2D images, predicting MS and LVEF 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-58131-6