Latest AI and machine learning research in myocardial infarction for healthcare professionals.
Automated electrocardiogram (ECG) interpretation has advanced, yet most systems remain narrow classifiers that emit fixed labels rather than the narratives or endpoint-specific answers clinicians need. Generative approaches could instead produce rich narratives, but are constrained by the gap between continuous biosignals and discrete language tokens. Here we present DeepECG-Tok, which reframes EC...
The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing CNN+Grad-CAM+multimodal LLM frameworks can generate ECG reports, but their explanations are often only weakly grounded in established diagnostic criteria, reducing...
Background Structural heart disease (SHD) drives heart failure and cardiovascular mortality but remains underdiagnosed, and echocardiography is limite...
Adult and pediatric electrocardiogram (ECG) interpretation relies on age-sensitive criteria, and models pretrained mainly on adult ECGs often transfer...
We conducted a scaling evaluation of unlabeled pretraining for electrocardiogram foundation model performance. One-dimensional vision transformer mask...
Long-tailed label distributions reduce the reliability of deep learning for electrocardiogram (ECG) arrhythmia diagnosis, particularly for clinically ...
Background Recent artificial intelligence (AI) models applied to the electrocardiogram (ECG) for risk stratification typically rely on supervised lear...
Clinical artificial intelligence (AI) models are usually reported as finished artifacts, but each model reflects a limited human search across a much ...
The electrocardiogram (ECG) contains rich nonlinear and non-stationary dynamic information that is only partly captured by conventional ECG interpreta...
Background: Heart failure with reduced ejection fraction (HFrEF) remains a major global health burden. Most electrocardiogram (ECG)-based artificial i...
Adhesive electrocardiography (ECG) electrodes used in neonatal intensive care units (NICUs) may cause skin injury in premature infants. Although photo...
Fetal electrocardiogram (fECG) and Doppler ultrasound provide complementary views of fetal cardiovascular function: fECG captures electrical activity ...
Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus an...
Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper E...
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterog...
Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly...
Electrocardiographic (ECG) interval measurements underpin clinical decision-making and large-scale cardiovascular research, yet existing automated met...
Most published clinical-AI results are single models on a single dataset, difficult to reproduce, and rarely validated outside their training hospital...
Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to b...
Background: Left ventricular diastolic dysfunction (LVDD) is a major determinant of heart failure (HF), yet its assessment relies on multiparametric e...