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Myocardial Infarction

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In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives.

Heart, lung & circulation
Electrocardiography (ECG) remains an irreplaceable tool in the management of the patients with myocardial infarction, with evaluation of the QRS and ST segment being the present major focus. Several ECG parameters have already been proposed to have p...

Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning.

BMC medical informatics and decision making
BACKGROUND: The Elliot wave principle commonly characterizes the impulsive and corrective wave trends for both financial market trends and electrocardiograms. The impulsive wave trends of electrocardiograms can annotate several wave components of hea...

Application of a machine learning-driven, multibiomarker panel for prediction of incident cardiovascular events in patients with suspected myocardial infarction.

Biomarkers in medicine
In patients with suspected myocardial infarction (MI), we sought to validate a machine learning-driven, multibiomarker panel for prediction of incident major adverse cardiovascular events (MACE). A previously described prognostic panel for MACE con...

Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction.

Scientific reports
Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis in the sense that cardiologists assess the waveforms presented in a 2-dimensional image. We hypothe...

Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study.

Chest
BACKGROUND: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained duri...

Development and application of artificial intelligence in cardiac imaging.

The British journal of radiology
In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac imaging, starting with radiomics, basic algorithms of deep learning and application tasks of algorithms, until recently the availability of the public databa...

Comprehensive electrocardiographic diagnosis based on deep learning.

Artificial intelligence in medicine
Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irre...

Super-resolution of cardiac magnetic resonance images using Laplacian Pyramid based on Generative Adversarial Networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND AND OBJECTIVE: Cardiac magnetic resonance imaging (MRI) can assist in both functional and structural analysis of the heart, but due to hardware and physical limitations, high-resolution MRI scans is time consuming and peak signal-to-noise ...

Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death.

Journal of biomedical semantics
BACKGROUND: Free text in electronic health records (EHR) may contain additional phenotypic information beyond structured (coded) information. For major health events - heart attack and death - there is a lack of studies evaluating the extent to which...