Acute Coronary Syndrome

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 adults, and immunocompromised patients. Timely recognition and classification of CNS infections are critical, yet early clinical and cerebrospinal fluid (CSF) findings are often non-specific. This review discusses when advanced neuroimaging adds valu...

AI-Enhanced Electrocardiogram for Detection of Occlusive Myocardial Infarction in High-Risk Non-ST-Segment Elevation Acute Coronary Syndrome.

BACKGROUND: Whether an artificial intelligence-enhanced electrocardiogram (AI-ECG) improves detection of occlusive myocardial infarction (OMI) in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) compared with standard of care (SOC) remains uncertain. OBJECTIVES: The objective of the study was to evaluate an AI-ECG model for detecting OMI among patients with suspected high-risk NSTE-ACS ...

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...

Performance of Artificial Intelligence-Powered ECG Analysis in Suspected ST-Segment Elevation Myocardial Infarction.

BACKGROUND: Artificial intelligence (AI)-based electrocardiogram (ECG) analysis has emerged as a promising adjunct to human ECG interpretation in susp...

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 ...

Establishing the laboratory error database: rationale, methodology, and a practical example.

Laboratory errors represent a critical yet underestimated threat to patient safety, with 26-30 % of reported errors adversely affecting patient care. ...

On Demographic Group Fairness Guarantees in Deep Learning.

We present a comprehensive theoretical framework analyzing the relationship between data distributions and fairness guarantees in equitable deep learn...

Comparative study of different artificial intelligence (AI)-assisted compressed sensing factors in inner ear heavily T2-weighted imaging.

AIM: Artificial intelligence (AI)-assisted compressed sensing (ACS) is a cutting-edge magnetic resonance imaging (MRI) acceleration technique based on...

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...

Colonoscopy surveillance in Lynch syndrome: what it prevents and what it does not.

Lynch syndrome (LS), synonymous with hereditary non-polyposis colorectal cancer (HNPCC), is caused by germline pathogenic variants in MLH1, MSH2, MSH6...

CSSL-ISRVN: consistency self-supervised learning integrating ISTANet and sensitivity refinement-enhanced variational network for accelerated MRI reconstruction.

OBJECTIVE: Magnetic resonance imaging is essential in clinical practice due to its non-invasive nature and superior soft-tissue contrast. However, lon...

TabulaTime: Novel multimodal deep learning for Acute Coronary Syndrome prediction through environmental and clinical data integration.

Acute Coronary Syndromes (ACS), including ST- and non-ST-segment elevation myocardial infarction (STEMI, NSTEMI), remain a leading cause of global mor...

Accuracy of GPT-5 and GPT-4o in diagnosing STEMI from 12-Lead ECGs: A comparative study with cardiologists and emergency physicians.

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) requires rapid, accurate electrocardiogram (ECG) interpretation. The diagnostic effecti...

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...

Identifying patients at risk for short-term adverse events after hip arthroscopy: a machine learning analysis of a national database.

PURPOSE: To develop and compare machine learning-based risk prediction models to identify patients at risk for short-term adverse outcomes (overnight ...

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...