Arrhythmias

MEETI: A Multimodal ECG Dataset from MIMIC-IV-ECG with Signals, Images, Features and Interpretations.

Electrocardiograms (ECGs) are essential for diagnosing arrhythmias, myocardial ischemia, and conduction disorders. While machine learning has achieved expert-level performance in ECG interpretation, the development of clinically deployable multimodal artificial intelligence (AI) systems is limited by the lack of public datasets that integrate raw signals, diagnostic images, and interpretation text...

Endoscopic Ultrasound for the Management of Pancreatic Neuroendocrine Tumors: Diagnosis, Treatment, and Future Perspectives.

Pancreatic neuroendocrine tumors (PanNETs) are increasingly diagnosed, reflecting greater clinical awareness, improved imaging, and revised classification. This review summarizes evidence on epidemiology, diagnostic workup, and endoscopic ultrasound (EUS)-guided management of PanNETs, encompassing diagnostic evaluation, tissue acquisition, and therapeutic interventions. EUS provides the highest di...

Present state and future of screening for atrial fibrillation: a state-of-the-art review.

Atrial fibrillation (AF) is the most common arrhythmia and is a leading cause of stroke and heart failure yet often remains undiagnosed. Screening has...

A joint CNN-Bi-LSTM-transformer architecture with SHAP explanations for multi-label arrhythmia detection from 12-lead ECGs.

Interpretable, automated Artificial Intelligence (AI) solutions are essential for accurate 12-lead electrocardiogram (ECG) arrhythmia classification b...

Ryanodine receptor 2 mutations in catecholaminergic polymorphic ventricular tachycardia: From molecular mechanisms to precision medicine.

Catecholaminergic polymorphic ventricular tachycardia is a classic example of the successful transfer of genetic cardiology from gene discovery to imp...

Four-electrode ECG reconstruction using anatomically grounded synthetic leads: a physiological measurement framework with hybrid CNN-transformer mapping.

The standard 12-lead electrocardiogram (ECG) remains essential for cardiac diagnosis but requires ten physical electrodes, limiting long-term and...

The Harvard-Emory ECG Database.

The Harvard-Emory ECG Database (HEEDB) is currently the largest open-access collection of 12-lead electrocardiogram (ECG) recordings, developed throug...

AI-ECG classification for Brugada syndrome: A study of machine learning techniques to optimise for limited datasets.

Deep neural networks can classify ECGs with high accuracy when training data is abundant. Rare conditions like Brugada syndrome, an inherited arrhythm...

Taiwan Academy of Tumor Ablation (TATA) consensus on hepatocellular carcinoma ablation.

Hepatocellular carcinoma (HCC) ranks sixth in incidence and third in mortality worldwide, underscoring its public health burden. Ablation therapy is o...

Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury.

The autonomous nervous system (ANS) response in neurological disorders is a direct modifiable risk factor for cardiovascular health, however, difficul...

Machine learning models enhance detection of arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiac disorder associated with sudden cardiac death, yet its diagnosis remains...

Reliable detection of focal onset impaired awareness seizures in patients with epilepsy using wearable ECG: Development and validation study.

BACKGROUND: Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and...

Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure.

BACKGROUND: ECG-based artificial intelligence may enable efficient prediction of incident heart failure (HF) risk to facilitate preventive efforts. Pr...

AI-Based STroke Risk fActor Classification and Treatment (ABSTRACT) study.

BACKGROUND: Stroke is a leading cause of death and disability worldwide, costing the UK approximately £26 billion annually. While lifestyle modificati...

Multi-resolution adaptive channel fusion transformer encoder LSTM for accurate streamflow prediction.

Accurate streamflow prediction plays a vital role in water management and flood mitigation. However, conventional deep learning models often fail to s...

Inadequate Preventive Dental Care Is Associated With Higher Cardiovascular Risk Identified by ECG-Based Artificial Intelligence Algorithms.

BACKGROUND: Inadequate preventive dental care may contribute to inflammatory conditions such as periodontitis, increasing cardiovascular disease risk....

Higher artificial intelligence-ECG atrial fibrillation prediction model output and estimated physiologic aging predict higher risk of adverse vascular events in patients with migraine.

OBJECTIVE: To investigate the ability of artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output a...

Spatial and single-cell transcriptomic atlas of human suprachiasmatic nucleus.

The suprachiasmatic nucleus (SCN) is considered the master pacemaker of the circadian clock in mammals, but our current knowledge of the SCN is mostly...

Investigating explainable arrhythmia classification using class-specific ensemble model from segmented ECG signals.

Accurate arrhythmia classification from short clinical ECGs is hard to achieve and explain. Prior studies are often single-lead, use uniform model fus...

Predicting cardiopulmonary exercise testing outcomes in congenital heart disease through multimodal data integration and geometric learning.

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables including ...