Acute Coronary Syndrome

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis (MPN-SVT).

Machine Learning Prediction for 6-Month Outcomes in Traumatic Cerebral Venous Sinus Thrombosis.

This study developed and externally validated a multicenter machine learning framework to predict 6-...

Unveiling the Microvasculature: The Index of Microcirculatory Resistance and Its Expanding Role in Cardiovascular Care.

Index of microcirculatory resistance (IMR) is a cutting-edge, wire-based tool that advances the capa...

Development of Models for Predicting the 7-Month Risk of Venous Thromboembolism and Clinically Relevant Bleeding in Ambulatory Patients with Cancer: Analysis from the AVERT Trial.

BACKGROUND: Patients with cancer are at elevated risk of venous thromboembolism (VTE). While primary...

Consistency analysis of AI cell recognition results between non anticoagulant and anticoagulant marrow smear after Wright's staining.

The integration of artificial intelligence with bone marrow cytology represents a significant trend ...

Machine learning-based model for predicting recanalization in isolated distal deep vein thrombosis and analysis of predictors.

BACKGROUND: Isolated distal deep vein thrombosis (IDDVT) is common, yet tools for predicting poor re...

Thrombosis at the crossroads of inflammation, coagulation, and vascular biology: Recent advances and emerging therapeutic strategies.

Thrombosis is a multifaceted pathological process involving intravascular clot formation that drives...

Machine learning in bleeding risk assessment for low-molecular-weight heparin or fondaparinux: a predictive model study.

BACKGROUD:  No universally accepted model exists for predicting bleeding risk in patients receiving ...

A large-scale 12-lead electrocardiogram dataset for acute coronary syndrome prediction containing 19,955 ECGs.

Acute coronary syndrome(ACS) is a common cardiovascular disease and a severe type of coronary heart ...

An integrated microneedle device for skin interstitial fluid multiplexed detection and deep learning-based analysis of acute myocardial infarction biomarkers.

Acute myocardial infarction (AMI) remains difficult to diagnose rapidly outside hospital settings be...

Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.

BACKGROUND: ST-elevation myocardial infarction (STEMI) exhibits substantial clinical heterogeneity c...

Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.

BACKGROUND: Accurately differentiating severe from nonsevere COVID-19 clinical types is critical for...

[Machine learning models to predict intestinal necrosis in acute mesenteric venous thrombosis].

Objective: To develop and validate machine learning-based models for predicting the risk of transmur...

Decoding Kounis syndrome with artificial intelligence: from diagnosis to precision medicine.

Kounis syndrome-acute coronary events triggered by allergic or hypersensitivity reactions-remains un...

External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.

An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital bio...

AI in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability.

BACKGROUND: Artificial intelligence (AI) integrated with point-of-care imaging is a promising approa...

Machine Learning-Based Magnetocardiography Model Aids in Diagnosing Non-ST-Segment Elevation Acute Coronary Syndrome in Acute Chest Pain.

BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a leading cause of acute ...

Anatomically parameterised statistical shape modelling of LVAD-supported left ventricles for thrombosis risk assessment.

Heart failure remains a major global burden, and limited donor availability has made left ventricula...

A Deep Neural Network for Interpreting Wearable Electrocardiogram Data in Atrial Fibrillation: Prospective Observational Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF) and atrial flutter (AFL) are common arrhythmias associated with...

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical ...

Deep learning-based non-contrast cine CMR for optimized prediction of left ventricular adverse remodeling after ST-elevation myocardial infarction.

OBJECTIVES: To evaluate the feasibility of a non-contrast cardiac magnetic resonance (CMR)-based dee...