PCI

Assessing the Predictive Value of Kolmogorov-Arnold Networks for the No-Reflow Phenomenon in ST-Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study.

OBJECTIVE: The no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) is a significant clinical issue associated with poor cardiovascular outcomes. This study aimed to develop and compare multiple supervised machine learning algorithms, including the recently introduced Kolmogorov-Arnold Network (KAN), to predict the occurrence of the no-reflow phenomenon in patients with STEMI...

Machine learning survival model for personalised prevention of catheter-related thrombosis in tumour patients.

BACKGROUND: Central venous catheters for drug delivery introduce catheter-related thrombosis (CRT) and influence the survival of cancer patients. The key unmet needs to personalise CRT prevention include identifying high-risk patients and optimising extubation time. In this study, we aimed to develop a survival model to facilitate personalised CRT prevention strategies. METHODS: We prospectively c...

Interpretable Machine Learning for Feature-Based Classification of Platelet Activation in Rotary Blood Pumps.

BACKGROUND: Thrombosis in rotary blood pumps arises from complex flow conditions that remain difficult to translate into reliable and interpretable ri...

Multiphysics learning with graph neural networks for thrombosis prediction in intracranial aneurysms.

Intracranial aneurysms are pathological dilations of cerebral arteries affecting 3-5% of the population. Their rupture can cause subarachnoid hemorrha...

Beyond Percutaneous Coronary Intervention - Targeted Molecular Therapies for the Next Era of Coronary Care.

Coronary artery disease (CAD) remains the leading global cause of cardiovascular morbidity and mortality, driven by dysregulated lipid metabolism, chr...

Advanced neuroimaging for central nervous system infections: when, where, how, and why?

PURPOSE OF REVIEW: Central nervous system (CNS) infections remain a major cause of morbidity and mortality worldwide, particularly in children, older ...

Artificial Intelligence Applications in COVID-19-Associated Coagulopathy: Lessons Learned.

Coronavirus disease 2019 (COVID-19)-associated coagulopathy (CAC) is a thromboinflammatory syndrome marked by endothelial injury, micro- and macrovasc...

Percutaneous coronary intervention in high bleeding risk patients: a review of contemporary management strategies.

INTRODUCTION: High bleeding risk (HBR) affects over one-third of patients undergoing percutaneous coronary intervention (PCI) and is associated with e...

Interpretable Clinical-Radiomics Model for Prediction of Blood Stasis and Left Atrial Appendage Thrombus.

BACKGROUND: The left atrial (LA) morphological profile, anatomically contiguous with the left atrial appendage (LAA), exhibits hemodynamic properties ...

Risk factor analysis and machine learning-based prediction model for type 4b acute myocardial infarction induced by very late stent thrombosis.

Artificial intelligence-based analysis of thrombotic and infectious risk factors in peripherally inserted central catheters (PICCs).

INTRODUCTION: Peripherally inserted central catheters (PICCs) are increasingly used in France for prolonged intravenous therapies such as chemotherapy...

Optimal Single or Combined Parameters for Dyssynergic Defecation on Anorectal Manometry: A Proof-of-Concept Machine Learning Study.

INTRODUCTION: To perform a proof-of-concept study to evaluate the performance of individual and combined anorectal manometry and balloon expulsion tes...

Artificial intelligence for venous thromboembolism risk stratification in surgical patients: a systematic review.

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a significant complication in surgical patients. Ar...

Phenotypic clustering of myocardial infarction complicated by out-of-hospital cardiac arrest using unsupervised machine learning.

BACKGROUND: Patients with myocardial infarction (MI) complicated by out-of-hospital cardiac arrest (OHCA) represent a heterogeneous population with va...

Machine Learning of Acoustic Voice Outcomes in Idiopathic Subglottic Stenosis.

OBJECTIVE: There are no objective reliable non-invasive screening tools to identify disease severity in patients with idiopathic subglottic stenosis (...

A noninvasive model for predicting intrahepatic venovenous shunts before HVPG measurement: a multiple cohort study.

OBJECTIVES: This study aimed to construct a machine learning (ML) model to facilitate non-invasive identification of moderate-to-severe intrahepatic v...

Artificial intelligence applications in emergency triage for suspected acute coronary syndrome: a systematic review.

BACKGROUND: Acute coronary syndromes (ACS) are time-critical conditions requiring rapid and accurate triage in the emergency department. Traditional t...

Radiomics features and clinical factors for predicting restenosis following endovascular therapy in patients with peripheral artery disease.

Optimization of drug diffusion in drug-eluting stents for coronary artery based on deep reinforcement learning.

Drug-eluting stents (DES) are extensively used to treat coronary artery disease, and improving their therapeutic efficacy remains a long-standing rese...

Integrated approaches to preventing macrovascular complications in type 2 diabetes mellitus: Advances in secondary prevention.

BACKGROUND: Type 2 diabetes mellitus (T2DM) substantially increases the risk of macrovascular complications, including coronary artery disease, cerebr...