Acute Coronary Syndrome

The Prediction of Venous Thromboembolism Using Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Systematic Review.

BACKGROUND: Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a common and serious complication following lower extremity arthroplasty, such as total hip and knee arthroplasty. Due to the increasing number of these surgeries, accurately predicting VTE risk is crucial. Traditional clinical prediction models often fall short due to their complexity and limited a...

Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction models demonstrate limited accuracy. This study aimed to evaluate the clinical utility of machine learning (ML) and Bayesian-learning models for CRT prediction in a large cohort of breast cancer patients undergoing catheterization.

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in...

Development of an artificial intelligence-enhanced warfarin interaction checker platform.

Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvan...

Anticoagulation colloidal microrobots based on heparin-mimicking polymers.

Coagulation within blood vessels is a major cause of cardiovascular disease and global mortality, highlighting the urgent need for effective anticoagu...

Computational discovery of novel PI3KC2α inhibitors using structure-based pharmacophore modeling, machine learning and molecular dynamic simulation.

PI3KC2α is a lipid kinase associated with cancer metastasis and thrombosis. In this study, we present a novel computational workflow integrating struc...

Sensing the Future of Thrombosis Management: Integrating Vessel-on-a-Chip Models, Advanced Biosensors, and AI-Driven Digital Twins.

Thrombotic events, such as strokes and deep vein thrombosis, remain a significant global health burden, with traditional diagnostic methods often fail...

The Application of Machine Learning Models to Predict Stillbirths.

: This study aims to evaluate the predictive value of comprehensive data obtained in obstetric clinics for the detection of stillbirth and the predict...

Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.

BACKGROUND: Late Gadolinium-enhancement in cardiac magnetic resonance imaging (LGE-CMR) is the gold standard for assessing myocardial infarction (MI) ...

Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food.

The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critica...

The Evolving Paradigm of Myocardial Infarction in the Era of Artificial Intelligence.

The classification and treatment of myocardial infarction (MI) have evolved significantly over the past few decades, with the ST-segment elevation myo...

Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic s...

A machine learning based death risk analysis and prediction of ST-segment elevation myocardial infarction (STEMI) patients.

Acute myocardial infarction is a condition in which a part of the heart muscle cannot receive enough blood due to the narrowing and blockage of the ve...

Prognostic Implications of Machine Learning Algorithm-Supported Diagnostic Classification of Myocardial Injury Using the Fourth Universal Definition of Myocardial Infarction.

BACKGROUND: With widespread adoption of high-sensitivity troponin assays, more individuals with myocardial injury are now identified, with type 1 myoc...

Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

BACKGROUND: Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong anticoagulation, but optimal patient selection remains ...

Advancements and challenges in high-sensitivity cardiac troponin assays: diagnostic, pathophysiological, and clinical perspectives.

Although significant progress has been made in recent years, some important questions remain regarding the analytical performance, pathophysiological ...

Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.

BACKGROUND: Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in pos...

Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.

Democratizing biomarker testing at the point-of-care requires innovations that match laboratory-grade sensitivity and precision in an accessible forma...

Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.

This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require careful anticoagulation management. With rising r...

Development and validation of machine learning-based prediction model for central venous access device-related thrombosis in children.

BACKGROUND: Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of centra...