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Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms.
JACC Asia
January 2025
Department of Frontier Cardiovascular Science, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive.
Objectives: This study aimed to develop a deep learning model for comprehensive prediction of echocardiographic findings from ECGs.
Emerging Potential and Challenges of AI-Based ECG Analysis in Clinical Medicine.
JACC Asia
January 2025
Department of Cardiovascular Medicine, Institute of Science Tokyo, Tokyo, Japan.
Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.
PLoS One
January 2025
School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India.
Background: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood flow. The leading cause of death for middle-aged and older people worldwide is myocardial infarction (MI), which is difficult to diagnose because it has no symptoms. Clinicians must evaluate electrocardiography (ECG) signals to diagnose MI, which is difficult and prone to observer bias.
View Article and Find Full Text PDFHigh-risk electrocardiogram presentations in the acute coronary syndrome patient - Beyond ST-segment elevation myocardial infarction.
Turk J Emerg Med
January 2025
Department of Emergency Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
This review considers high-risk electrocardiographic patterns in the acute coronary syndrome (ACS) patient; we review 7 electrocardiogram presentations lacking diagnostic criteria for ST-segment elevation myocardial infarction (STEMI) yet likely representing either STEMI equivalent syndromes or ACS presentations with significant short-and long-term risk. The STEMI equivalent presentations include acute posterior wall myocardial infarction, the hyperacute T-wave of early STEMI, de Winter syndrome, first diagonal of the left anterior descending artery occlusion, and left bundle branch block modified Sgarbossa positive findings. High-risk presentation, not felt to be STEMI equivalent entities yet still possessing significant risk of short-and long-term adverse outcome, include lead aVR ST-segment elevation and Wellens syndrome.
View Article and Find Full Text PDFArtificial intelligence-enhanced electrocardiography improves the detection of coronary artery disease.
Comput Struct Biotechnol J
December 2024
Institute of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli 350, Taiwan.
An AI-assisted algorithm has been developed to improve the detection of significant coronary artery disease (CAD) in high-risk individuals who have normal electrocardiograms (ECGs). This retrospective study analyzed ECGs from patients aged ≥ 18 years who were undergoing coronary angiography to obtain a clinical diagnosis at Chang Gung Memorial Hospital in Taiwan. Utilizing 12-lead ECG datasets, the algorithm integrated features like time intervals, amplitudes, and slope between peaks, a total of 561 features, with the XGBoost model yielding the best performance.
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