AIMC Topic: Electrocardiography

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Deep learning and the electrocardiogram: review of the current state-of-the-art.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for ...

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

Mayo Clinic proceedings
OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).

More than meets the eye: Using AI to identify reduced heart function by electrocardiograms.

Med (New York, N.Y.)
Electrocardiographic (ECG) assessment of patients with suspected heart disease is a bedrock of cardiology for diagnosing conduction system disease, arrhythmias, and heart attack. Now, using AI-assisted interpretation of ECGs, the signals within these...

Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

European heart journal. Acute cardiovascular care
AIMS: An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care un...

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.

Cardiovascular research
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances ...

Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.

Proceedings of the National Academy of Sciences of the United States of America
Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically...

Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram.

JAMA cardiology
IMPORTANCE: Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is the hallmark feature of LQTS, approximately 40% of...

Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Electrocardiographic imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse p...