Myocardial Infarction

Multiparametric AI-based perfusion analysis outperforms Tmax thresholding for critically hypoperfused tissue estimation in acute ischemic stroke undergoing admission MRI.

OBJECTIVE: Perfusion imaging is used to estimate critically hypoperfused tissue in acute ischemic stroke (AIS), commonly using threshold-based methods. This study compared an artificial intelligence (AI)-driven multiparametric approach with conventional thresholding for estimating ischemic core and hypoperfused tissue volumes. MATERIALS AND METHODS: We analyzed 186 AIS patients from the HIBISCUS-S...

Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?

BACKGROUND: In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoing reperfusion treatment. However, its immediate availability remains limited, especially in resource-constrained regions. We sought to evaluate the association of initial core volume (ICV) measured on non-contrast computed tomography (NCCT) by a deep...

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is wide...

Multimodal imaging in left main and bifurcation revascularization: contemporary evidence and comparative modalities review.

Left main (LM) and bifurcation coronary artery disease (CAD) are anatomically complex lesions which require detailed planning and precise execution fo...

AI-enhanced approaches for personalized cardiac treatment: insights from ECG data.

The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always ac...

Machine learning-based stratification of chagas heart failure severity using ECG power spectral biomarkers.

AI-enabled privacy-preserving cardiac diagnostics via electrocardiograms.

Electrocardiogram (ECG) is a widely available, non-invasive diagnostic tool used for cardiovascular screening and provides essential insights into hea...

Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF Trial Analysis.

BACKGROUND: Screening for atrial fibrillation (AF) may lead to earlier detection and initiation of preventive measures. Current AF screening approache...

Wearable ECG and PPG for anxiety detection: a translational digital medicine perspective.

Anxiety disorders affect hundreds of millions of people worldwide, yet objective and continuous assessment remains limited in clinical practice. To ou...

Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.

Artificial Intelligence-Empowered Image Fusion of Coronary Computed Tomography Angiography and Invasive Angiography for Guiding Chronic Total Occlusion Percutaneous Coronary Intervention.

Coronary computed tomography angiography (CCTA) provides qualitative and quantitative characteristics of atherosclerotic plaques, quantified percentag...

A machine learning approach to predicting dyspnea with noninvasive biomarkers.

Dyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its p...

Association between aspirin use and mortality in critically ill patients with heart failure: a retrospective study using the MIMIC-IV database.

BACKGROUND: Despite the established antithrombotic benefits of aspirin in cardiovascular disease, its efficacy remains controversial for critically il...

Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches.

Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals o...

Can an artificial intelligence electrocardiogram algorithm improve diagnostic accuracy for acute coronary occlusion in the difficult subset of canceled catheterization lab activations?

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) and its equivalents describe the electrocardiogram (ECG) findings of acute coronary occ...

Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases.

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failu...

Personalized artificial intelligence based left ventricular ejection fraction and systolic dysfunction assessment.

Left-ventricular (LV) ejection fraction (LVEF) is a fundamental measure of cardiac function, typically assessed with resource-intensive imaging techni...

Equiflow: An open-source software package for evaluating changes in cohort composition.

Clinical research studies routinely apply exclusion criteria and data preprocessing steps that can substantially alter dataset composition, potentiall...

Multicenter validation of an artificial intelligence-enabled ECG model to predict 1-year risk of atrial fibrillation or flutter.

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia associated with an increased risk of stroke and heart failure. AF is often ...

Electrocardiogram-Based Mental Stress Detection Amid Everyday Activities Using Machine Learning: Model Development and Validation Study.

BACKGROUND: Frequent, sustained stress is linked to poor health and requires monitoring for early intervention. Electrocardiograms (ECG) are promising...