Acute Coronary Syndrome

On Demographic Group Fairness Guarantees in Deep Learning.

We present a comprehensive theoretical framework analyzing the relationship between data distributio...

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 reson...

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 pr...

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 b...

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 natu...

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 (ST...

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 electrocardi...

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...

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 (...

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 a...

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 i...

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...

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances...

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in car...

Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome Using 12-Lead ECG.

OBJECTIVE: Acute coronary syndrome (ACS) is a life-threatening condition requiring accurate diagnosi...

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 compli...

A fine-grained transformer combined with multimodal data for predicting hospital length of stay in acute coronary syndrome.

The prediction of hospital length of stay (LOS) is of great significance for hospitals to rationally...

Longitudinal Strain by Artificial Intelligence-Driven Automated Strain Analysis for Left Ventricular Function Evaluation and Infarct Region Estimation.

PURPOSE: Accurate evaluation of left ventricular (LV) dysfunction and infarct localization in acute ...

Explainable machine learning framework based on blood biomarkers and routine health information for assistive diagnosis and risk stratification of acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) presents a critical clinical challenge due to its rapi...

[Development of a deep learning model for predicting adverse cardiovascular events in patients with acute coronary syndrome based on retinal fundus images].

Objective: To develop and validate a deep learning model based on retinal fundus images for predicti...