Myocardial Infarction

Predicting Intramyocardial Hemorrhage Before Reperfusion in STEMI Patients With Intrinsically Explainable Artificial Intelligence.

BACKGROUND: Intramyocardial hemorrhage (IMH) complicates approximately 40% of reperfused ST-segment elevation myocardial infarctions (STEMIs) and is associated with worse outcomes. No method identifies patients at risk before reperfusion. OBJECTIVES: The objective of the study was to develop and evaluate an explainable artificial intelligence approach for pre-reperfusion IMH prediction and transla...

IoMT-Blockchain framework for secure and real-time heart disease monitoring using hybrid black-winged optimized spherical structural graph convolutional neural networks.

Cardiovascular diseases are a primary global health concern, requiring continuous monitoring and accurate diagnostic mechanisms for early detection and prevention. The integration of the Internet of Medical Things (IoMT), deep learning, and blockchain technologies has enabled intelligent healthcare systems capable of real-time cardiac assessment and enhanced security in the management of medical i...

Two-stage robust 3D CTA-2D DSA alignment via vascular-aware rigid and pyramid-based hierarchical non-rigid registration.

Accurate vascular structural alignment between 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) can signif...

Combinations of single gene predictors with AI-driven polygenic precision therapies in cardiovascular diseases.

Pharmacogenomics (PGx) is transforming how we treat cardiovascular disease (CVD) by enabling us to select and dose drugs based on our genetic profiles...

Machine learning-enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support.

Electrocardiogram (ECG) signals play a critical role in the early detection of cardiac arrhythmias, which remain a major cause of morbidity and mortal...

A scoping review on aging and cardiovascular diseases - Molecular mediators and artificial intelligence-based advanced diagnostic methods.

BACKGROUND: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, and their risk increases with age. Biological age reflects...

Prediction of Elevated Troponin T Levels from Prehospital Electrocardiograms.

BACKGROUND: Early and reliable predictions of elevated cardiac troponin levels from electrocardiograms (ECGs) in the prehospital setting could serve a...

Interpretable Machine Learning to Predict Anxiety or Depression in Acute Ischemic Stroke after IV Thrombolysis: A Retrospective Cohort Study.

BACKGROUND: Acute Ischemic Stroke (AIS) represents a prevalent cerebrovascular condition characterized by significant levels of disability and mortali...

A quarter-century of progress: innovations in lipid medicine that transformed cardiovascular prevention.

This TimeCapsule reviews the major transformations in lipid metabolism and cardiovascular disease prevention during the first quarter of the 21st cent...

Large Language Model-Based Identification of Acute Coronary Syndrome Management Delays.

Acute coronary syndrome (ACS) requires prompt treatment, yet management delays are difficult to identify. In this study, we developed a large language...

First-trimester multi-modal cell-free DNA analysis for prediction of preterm and term preeclampsia.

BACKGROUND: Preeclampsia, which is a leading cause of maternal and perinatal mortality and morbidity, represents a biologically heterogeneous syndrome...

Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.

BACKGROUND: Acute coronary syndrome (ACS) remains a leading cause of morbidity and mortality worldwide, where timely diagnosis is critical. Prehospita...

No-reflow phenomenon during percutaneous coronary intervention: Contemporary insights into mechanisms, prediction, and management.

No-reflow phenomenon remains a common and prognostically adverse complication of percutaneous coronary intervention (PCI), characterized by impaired m...

A Flexible Wireless Passive Platform for Decoupled Electrolyte and Temperature Sensing Toward Heat‑Stress Assessment.

Strenuous exercise and heat exposure can significantly elevate core body temperature and induce profuse sweating, leading to dehydration, electrolyte ...

Electrocardiogram-based short-term risk stratification for acute kidney injury using time-frequency deep learning analysis.

BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) is a common and clinically important complication in critically ill and perioperative patients. C...

Demographic-aware temporal graph attention for fair and accurate cardiac abnormality detection in 12-lead ECG.

Automated 12-lead ECG interpretation has achieved strong diagnostic accuracy through deep learning, yet systematic demographic disparities-particularl...

Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation.

BACKGROUND: Vision-enabled large language models (VE-LLMs) have the potential to provide flexible and explainable medical image interpretation. Howeve...

pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.

Long QT syndrome (LQTS) is a primary risk factor for ventricular arrhythmias and sudden cardiac death in children. Accurate corrected QT intervals (QT...

Detection of coronary microvascular dysfunction based on machine learning algorithm with multidimensional temporal-spatial features from electrocardiogram.

Coronary microvascular dysfunction (CMD) causes myocardial ischemia and is associated with adverse cardiovascular events. This study explored the valu...

ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.

Cardiovascular diseases are the leading cause of death in the world, requiring the accurate and timely detection of arrhythmias to prevent sudden card...