Latest AI and machine learning research in myocardial infarction for healthcare professionals.
Synthetic data can be the solution to privacy requirements, can enrich datasets limited by underrepresentation of certain subgroups/minorities, combat data shortage, and reduce annotation costs, to facilitate the development of data-hungry machine-learning applications. An important limitation of current synthetic data is the missing link to patient characteristics and outcomes. This metadata is e...
Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and benchmarking hospital performance. This study aimed to identify pre-procedural factors to predict the risk of 30-day all-cause mortality post-PCI using machine learning (ML) approaches. The study analysed 93,055 consecutive PCI procedures. Boruta feature ...
Deep learning models have shown remarkable performance in electrocardiogram (ECG) analysis, but the limited availability and size of ECG datasets have...
Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. ...
Electrocardiogram (ECG) analysis plays a critical role in the early detection and diagnosis of cardiac abnormalities. In this study, we propose a fusi...
In an analysis of 69,173 UK Biobank participants, we paired MRI-based measurements of the ascending aortic diameter with ECG signal. We trained a 1D c...
Machine learning models for predicting structural heart disease (SHD) from electrocardiography (ECG) traditionally required structured echocardiograph...
Artificial intelligence applied to electrocardiography (AI-ECG) can derive a heart age or ECG-age, potentially reflecting waveform patterns that indic...
Cardiac amyloidosis (CA) is an underdiagnosed infiltrative cardiomyopathy associated with poor outcomes if not detected early. Artificial intelligence...
Acute ischemic stroke (AIS) management has evolved substantially over the past two decades, with mechanical thrombectomy adding complexity that requir...
Many ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. The underrepresentation of women in cardiovascular disease ...
Advancements in artificial intelligence have enabled estimation of cardiac age from raw ECG waveforms. ECG-age is a novel metric that provides insight...
Traditional ECG criteria for left ventricular hypertrophy (LVH) have modest diagnostic yield. Develop and validate machine learning models for LVH dia...
Sudden cardiac death (SCD) in young individuals—especially athletes—remains difficult to diagnose due to overlapping physiological and pathological el...
Heart failure (HF) is a life-threatening syndrome with significant morbidity and mortality. While evidence-based drug treatments have effectively redu...
Monitoring the effectiveness of statin therapy in patients with dyslipidemia is essential for ensuring optimal treatment outcomes. The current standar...
This study presents a novel two-stage framework to enhance the reliability of resting electrocardiogram (ECG) signals by addressing motion artifacts t...
Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria m...
Objective assessment of left ventricular function remains a key prognosticator that is used to guide therapeutic decisions for patients with heart fai...
Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery and is associated with longer hospital stays and adv...