Myocardial Infarction

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drivers of this damage remain unclear. This study explored the dynamics of inflammatory, fibrotic, and endothelial biomarkers in hospitalized COVID-19 patients. Plasma levels of MMP2, MMP7, MMP9, KL-6, IL-6, TNF-α, angiopoietin-2, thrombomodulin, and hyaluronan were measured in 73 hospitalized COVID-19 p...

Artificial intelligence-enhanced ECG detection of acute coronary occlusion in chest pain patients with ST-elevation in lead aVR - A direct comparison to conventional ECG criteria.

Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, emphasizing the need for accurate and early diagnosis. Electrocardiograms...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with signif...

Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in clinical applications. Although deep learning model...

Artificial Intelligence-Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial.

BACKGROUND: Atrial fibrillation (AF) is often underdiagnosed and undertreated by noncardiologists. This study evaluated whether artificial intelligenc...

Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respiratory patterns during sleep. This disruption can hav...

Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review.

BACKGROUND: Perioperative electrocardiographic monitoring can offer immediate detection of myocardial ischaemia, yet its application in perioperative ...

Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients.

Many stroke patients have poor outcomes despite successful endovascular therapy (EVT). We hypothesized that machine learning (ML)-based analysis of va...

Semantic ECG hash similarity graph.

Graph-based methods have made significant progress in addressing the dependent correlations among ECG time series variables. However, most existing gr...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Occult Atrial Fibrillation in Patients With Stroke Who Undergo Prolonged Cardiac Monitoring.

OBJECTIVE: To evaluate the performance of an artificial intelligence (AI)-enhanced electrocardiography (ECG; AI-ECG) algorithm to predict atrial fibri...

A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX).

Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to understanding how AI models interpret electrocardiogram...

Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.

BACKGROUND: Non-ST segment elevation myocardial infarction (Non-STEMI) is a severe cardiovascular condition mainly affecting individuals aged 75 and a...

DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection.

Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely intervention in cardiac disorders. Existing models, ...

Metabolomic biomarkers could be molecular clocks in timing stroke onset.

The preferred treatment for acute ischaemic stroke (AIS) is intravenous thrombolysis (IVT) administered within 4.5 hours (h) of symptom onset. This st...

Machine learning evaluation model of pilot workload in a low-visibility environment.

To analyze the variation trend of pilots' workload in a low-visibility flight environment and then put forward a scientific evaluation method, this st...

In vitro to in vivo translation of artificial intelligence for clinical use: screening for acute coronary syndrome to identify ST-elevation myocardial infarction.

OBJECTIVE: The integration of predictive models into live clinical care requires scientific testing before implementation to ensure patient safety. We...

Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention.

While static risk models may identify key driving risk factors, the dynamic nature of risk requires up-to-date risk information to guide treatment dec...

A Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma.

Asthma is a chronic inflammatory disease of the small airways, affecting over 200 million people globally. Cold air exposure is a potential risk facto...

Electrocardiogram-Based Artificial Intelligence for Detection of Low Ejection Fraction: A Contemporary Review.

Artificial intelligence (AI) is transforming the role of electrocardiography (ECG) in cardiovascular care, enabling early disease detection, improved ...