AIMC Topic: ST Elevation Myocardial Infarction

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Research on the prediction of slow blood flow in pPCI of STEMI patients based on CatBoost.

European journal of medical research
BACKGROUND: In recent years, the incidence of ST-segment elevation myocardial infarction (STEMI) has been on the rise, leading to an increase in the number of patients undergoing direct percutaneous coronary intervention (pPCI). However, some patient...

A novel deep learning system for STEMI prognostic prediction from multi-sequence cardiac magnetic resonance.

Science bulletin
ST-elevation myocardial infarction (STEMI) remains a leading cause of cardiovascular morbidity and mortality worldwide, and accurate early risk stratification is critical for implementing precision therapies in clinical practice. However, existing cl...

Machine learning prediction of STEMI incidence with SHAP interpretation of environmental determinants.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is a life-threatening cardiovascular event influenced by meteorological conditions and air pollution. Traditional statistical methods often fail to capture the complex, nonlinear relationships betwee...

Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning.

The Analyst
: Analysis of small molecule metabolites found in blood plasma of patients undergoing treatment for STEMI has the potential to be used as a clinical diagnostic and prognostic tool, capable of predicting disease progression, risk of negative outcomes,...

Comparison of synthetic LGE with optimal inversion time vs. conventional LGE via representation learning: Quantification of Bias in Population Analysis.

Computers in biology and medicine
PURPOSE: Late Gadolinium Enhancement (LGE) images are crucial elements of CMR protocols for evaluating myocardial infarct (MI) severity and size. However, these images rely on signal intensity changes and manual inversion time (TI) settings, leading ...

An interpretable radiomics-based machine learning model for predicting reverse left ventricular remodeling in STEMI patients using late gadolinium enhancement of myocardial scar.

European radiology
OBJECTIVES: To evaluate the added value of the late gadolinium enhancement (LGE)-scar radiomics features in predicting reverse left ventricular remodeling (r-LVR) in ST-segment elevation myocardial infarction (STEMI) patients using machine learning (...

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more...

Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.

International journal of cardiology
BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) size. Texture-based probability mapping (TPM) is a novel machine learning-based analysis of LGE imag...