AI Medical Compendium Journal:
JACC. Clinical electrophysiology

Showing 1 to 10 of 22 articles

Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

JACC. Clinical electrophysiology
BACKGROUND: Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong anticoagulation, but optimal patient selection remains challenging.

Serum Potassium Monitoring Using AI-Enabled Smartwatch Electrocardiograms.

JACC. Clinical electrophysiology
BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laborator...

Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.

JACC. Clinical electrophysiology
BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) populatio...

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR.

JACC. Clinical electrophysiology
BACKGROUND: New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR.

QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms.

JACC. Clinical electrophysiology
BACKGROUND: Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

JACC. Clinical electrophysiology
Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led t...

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing.

JACC. Clinical electrophysiology
BACKGROUND: Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer ...

A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes.

JACC. Clinical electrophysiology
BACKGROUND: Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients ...

Use of Wearable Technology and Deep Learning to Improve the Diagnosis of Brugada Syndrome.

JACC. Clinical electrophysiology
BACKGROUND: The diagnosis of Brugada syndrome by 12-lead electrocardiography (ECG) is challenging because the diagnostic type 1 pattern is often transient.

Cardiac Rhythm Device Identification Using Neural Networks.

JACC. Clinical electrophysiology
OBJECTIVES: This paper reports the development, validation, and public availability of a new neural network-based system which attempts to identify the manufacturer and even the model group of a pacemaker or defibrillator from a chest radiograph.