Acute Coronary Syndrome

A Comparison of Prostate Cancer Screening Information Quality on Standard and Advanced Versions of ChatGPT, Google Gemini, and Microsoft Copilot: A Cross-Sectional Study.

PurposeArtificially Intelligent (AI) chatbots have the potential to produce information to support shared prostate cancer (PrCA) decision-making. Therefore, our purpose was to evaluate and compare the accuracy, completeness, readability, and credibility of responses from standard and advanced versions of popular chatbots: ChatGPT-3.5, ChatGPT-4.0, Microsoft Copilot, Microsoft Copilot Pro, Google G...

Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model.

Angio-based microvascular resistance (AMR) as a potential alternative to the index of microcirculatory resistance (IMR) and its relationship with microvascular obstruction (MVO) and other cardiac magnetic resonance (CMR) parameters still lacks comprehensive validation. This study aimed to validate the correlation between AMR and CMR-derived parameters and to construct an interpretable machine lear...

The research progress of biologics in elderly-onset rheumatoid arthritis (EORA).

is a distinct subtype of characterized by heightened treatment challenges due to immune aging and the complexity of comorbidities. This review syste...

Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment.

BACKGROUND: Machine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating ...

Aspirin modulates inflammatory biomarkers in patients with subcortical silent brain infarcts.

INTRODUCTION: This study aimed to identify differences in the levels of inflammation-related biomarkers between patients with subcortical silent brain...

Development and validation of a support vector machine-based nomogram for diagnosis of obstetric antiphospholipid syndrome.

BACKGROUND: Antiphospholipid Syndrome (APS) is a systemic autoimmune disorder characterized by arterial or venous thrombosis and/or pregnancy complica...

Innovative modified-net architecture: enhanced segmentation of deep vein thrombosis.

A new era for diagnosing and treating Deep Vein Thrombosis (DVT) relies on precise segmentation from medical images. Our research introduces a novel a...

The predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma.

INTRODUCTION: Heparin-binding protein is an inflammatory factor with predictive value for sepsis and participates in the inflammatory response through...

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.

This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the ty...

Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.

This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-...

Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score.

BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead ...

Programmable ultrasound-mediated swarms manipulation of bacteria-red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy.

Despite the excellent advantages of biomicrorobots, such as autonomous navigation and targeting actuation, effective penetration and retention to deep...

Mapping Thrombosis Serum Markers by H-NMR Allied with Machine Learning Tools.

Machine learning and artificial intelligence tools were used to investigate the discriminatory potential of blood serum metabolites for thromboembolis...

Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombot...

Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.

The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty...

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is cruc...

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is ...

Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms.

ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the leading causes of morbidity and mortality. Revasculari...

Role of Robotics in Image-Guided Trans-Arterial Interventions.

The integration of robotic systems in image-guided trans-arterial interventions has revolutionized the field of Interventional Radiology (IR), offerin...

Towards real-time myocardial infarction diagnosis: a convergence of machine learning and ion-exchange membrane technologies leveraging miRNA signatures.

Rapid diagnosis of acute myocardial infarction (AMI) is crucial for optimal patient management. Accurate diagnosis and time of onset of an acute event...