Myocardial Infarction

MAF-Net: Multimodal cross-attention-based fusion network for cardiovascular disease classification.

Cardiovascular disease ranks among the leading causes of death globally, posing a severe threat to human health. Consequently, rapid and accurate identification of cardiovascular disease has become a critical research endeavor. Electrocardiograms (ECGs), as a non-invasive detection tool, are widely used in cardiovascular disease detection due to their convenience and effectiveness. However, existi...

AI based ECG data recovery and cardiovascular diseases classification (CEDRC-network).

Cardiovascular diseases (CVDs) are a significant and widespread cause of death in the world, continuing to increase mortality rates. Therefore, timely identification and diagnosis are essential for a patient's optimized recovery and longevity. In this regard, ECG is an effective tool for detecting anomalous heart conditions. However, interfering factors like noise, transient changes, and missing d...

Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.

BACKGROUND: Post-COVID Fatigue (PCF) is one of the most common issues people face after recovering from COVID-19. Due to the heterogeneity of clinical...

EHR-derived cognitive load is associated with guideline-concordant statin initiation in primary care.

INTRODUCTION: Linking electronic health record (EHR) use to care quality may offer insights into potential interventions improving guideline adherence...

Advancing cardiovascular disease diagnosis with an interpretable and responsible AI framework.

Cardiovascular disease (CVD) remains a leading global health threat, responsible for one in five deaths worldwide. Early detection is critical to miti...

Acceptable accuracy for medical AI: a survey of physicians and the general population in Sweden.

OBJECTIVES: To identify the lowest sensitivity and specificity that physicians and the general population consider acceptable for medical artificial i...

Exploring the role of reinforcement learning in vision-language models for cardiovascular disease decision support.

OBJECTIVE: To explore the role of reinforcement learning (RL) in vision-language models (VLMs) for cardiovascular disease (CVD) decision support and a...

Improving transthyretin cardiac amyloidosis detection from electrocardiograms through the Willem artificial intelligence platform.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently underdiagnosed disease in which delay in diagnosis limits the efficacy of t...

A lightweight and explainable cardiac signal framework for screening-oriented cardiometabolic risk assessment.

Early identification of cardiometabolic and autonomic dysfunction using electrocardiogram (ECG) signals is essential for preventive cardiovascular scr...

Automated multi-class ECG arrhythmia detection using VMD and multi-task optimization.

Electrocardiogram (ECG) classification is essential for accurately detecting and tracking heart rhythm disorders. This study proposes a multi-class EC...

Re-evaluating heart rate variability biomarkers for glucose sensing: the impact of age normalisation and subject-independent validation.

BACKGROUND: Heart rate variability (HRV) derived from electrocardiogram (ECG) signals offers a promising non-invasive window into glycemic status; how...

Machine Learning-driven Prioritisation of Lipoprotein(a) Testing: Model Development and Validation.

AIMS: Elevated lipoprotein(a) [Lp(a)] is a common risk factor for cardiovascular disease (CVD) affecting ∼1.4 billion people globally, with novel trea...

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 cardiov...

AI-Enhanced ECG Screenings: An Alternative to Traditional Questionnaires for Ischaemic Heart Disease.

Ischaemic heart disease (IHD) is the leading global cause of death. Traditional screening tools like the Framingham Risk Score (FRS) and Systematic Co...

Machine learning-based prediction of myocardial ischemia-reperfusion injury in patients with ST-segment elevation myocardial infarction: the STEMI-MIRI prediction model.

ECG-Gated Retrospective Coronary CT Angiography With Deep Learning Reconstruction on 8-cm Detector CT Scanners: Pushing Radiation Dose Towards Prospective Acquisition on 16-cm Wide-Detector Scanners.

PURPOSE: Although 16-cm wide-detector CT scanners with prospective ECG-gating enable coronary artery imaging within a single cardiac cycle at a low ra...

Resting-State Electroencephalogram and Heart Rate Variability Jointly Predicts Intentional Episodic Memory Control Using Machine Learning Fusion.

Deficits in intentional control over episodic memory constitute a risk factor for multiple psychiatric disorders. Guided by a body-brain dynamic syste...

Addressing disparities in transthyretin amyloid cardiomyopathy: A systematic review of artificial intelligence in the early identification to improve patient outcomes.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) remains substantially underdiagnosed among Black patient populations. When applied to non-i...

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-...

Comparative evaluation of multimodal large language models for diagnostic accuracy in pediatric electrocardiography: a prospective comparative diagnostic accuracy study.

UNLABELLED: We evaluated three multimodal LLMs, ChatGPT (GPT-5.2), Gemini 3, and Microsoft Copilot, in pediatric ECG interpretation, focusing on clini...