Myocardial Infarction

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 or PMS2, which confer an elevated lifetime risk of colorectal cancer (CRC). Since the early 2000s, colonoscopic surveillance has been recommended to reduce CRC incidence via polypectomy and mortality via early detection, with intervals now being tai...

Evolutionary-Based Deep Learning Network Model using Adaptive Mixing Differential Evolution and Application in Acute Pulmonary Embolism.

INTRODUCTION: Acute pulmonary embolism (APE) is characterized by high incidence and mortality, along with non-specific clinical manifestations. Its common symptoms such as dyspnea, chest pain, cough, and hemoptysis can also appear in other diseases, frequently resulting in the oversight of APE patients and raising the risk of misdiagnosis and mortality. Current clinical risk stratification for pul...

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...

Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification.

AIMS: Attribution-based explainability methods are widely used in electrocardiogram (ECG) analysis to interpret predictions from 'black-box' deep neur...

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-makin...

Clinical Impact of Postrecanalization Hemorrhagic Transformation and Its Prediction Using Baseline Noncontrast CT.

BACKGROUND: Hemorrhagic transformation (HT) after recanalization therapy remains a critical concern in acute ischemic stroke management. While severe ...

Prediction of Heart Failure With Reduced Ejection Fraction With Artificial Intelligence Electrocardiography in Patients With Atrial Fibrillation.

OBJECTIVE: To determine whether a low ejection fraction artificial intelligence electrocardiogram (AI-ECG) algorithm predicts incident heart failure w...

Precise oxygen therapy to emphysema patients by fuzzy-based gain tuning control of set-point regulated MRAC.

Emphysema, a primary component of chronic obstructive pulmonary disease (COPD), causes progressive dyspnea through the destruction of alveolar membran...

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-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Deep Learning-Based ROSC Prediction and ECG Phenotyping in Out-of-Hospital Cardiac Arrest.

INTRODUCTION: Electrocardiogram (ECG) signals during cardiac arrest contain detailed information on cardiac rhythm characteristics and have been assoc...

NeuroCardioSense (NCS): A Time-Aware Fuzzy Decision Framework for Multi-Lead ECG Classification and Arrhythmia Detection.

Accurate classification of electrocardiogram (ECG) signals is essential for automated arrhythmia detection and clinical decision support. Existing dee...

3D ECG: a new simplified view.

BACKGROUND: The standard 12‑lead electrocardiogram (ECG) represents cardiac electrical activity through two-dimensional projections, requiring clinici...

EFFNet: Efficient feature fusion network for left ventricular hypertrophy identification based on 12-lead electrocardiogram signals.

BACKGROUND: Left ventricular hypertrophy (LVH) is a common cardiovascular disorder, yet its detection from electrocardiogram (ECG) signals remains cha...

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 in pharmacotherapy. Emerging evidence reveals a p...

An artificial intelligence prediction model for optimizing patient selection for cardiac imaging for the investigation of suspected coronary artery disease.

AIMS: Nearly, 40% of patients undergoing elective invasive coronary angiography (ICA) are diagnosed with non-obstructive coronary artery disease (CAD)...

Artificial Intelligence-Enabled Electrocardiographic Detection of Severe Aortic Stenosis Leading to Transcatheter Aortic Valve Replacement.

BACKGROUND: EchoNext is an artificial intelligence (artificial intelligence)-enabled electrocardiographic (ECG) model validated to detect unrecognized...

Deep learning-enabled ECG system for detecting left ventricular hypertrophy and predicting cardiovascular prognoses.

Left ventricular hypertrophy (LVH) is a common condition with a prevalence of 15%-20% in general population. Prior studies have suggested that deep le...

Validation of AI-enhanced ECG image analysis for identifying extreme cardiac magnetic resonance metrics in a cross-ethnic UK biobank study.

Artificial intelligence enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, but validation against cardiac mag...

Early electrocardiographic repolarization changes are associated with subclinical cancer therapy-related cardiac dysfunction in lymphoma patients: A machine learning-assisted longitudinal study.

UNLABELLED: Anthracycline-induced cardiotoxicity remains a significant clinical challenge. We evaluated longitudinal electrocardiographic (ECG) repola...