AIMC Topic: Pacemaker, Artificial

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Future perspectives and challenges of artificial intelligence implementation in remote monitoring of cardiac implantable electronic devices.

Expert review of medical devices Sep 13, 2024
Monitoring, Physiologic Humans Defibrillators, Implantable Artificial Intelligence Pacemaker, Artificial
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Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Journal of medical systems Jul 19, 2024
Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related s...
Deep Learning Sick Sinus Syndrome Electrocardiography Humans Pacemaker, Artificial Artificial Intelligence Male Female Aged Cardiovascular Diseases Middle Aged
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Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.

Journal of medical systems Jul 31, 2023
With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is an increased chance that patients with implanted cardiac devices require MRI examination during their...
Magnetic Resonance Imaging Pacemaker, Artificial Neural Networks, Computer Humans Patient Safety Deep Learning
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Fixed-Time Synchronization of Coupled Oscillator Networks with a Pacemaker.

Sensors (Basel, Switzerland) Dec 3, 2022
This paper investigates the fixed-time synchronization problem of a Kuramoto-oscillator network in the presence of a pacemaker. Based on the framework of the cyber-physical system (CPS), fixed-time synchronization criteria of such network are present...
Neural Networks, Computer Time Factors Pacemaker, Artificial
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Neural Network Detection of Pacemakers for MRI Safety.

Journal of digital imaging Jun 29, 2022
Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-exis...
Humans Radiography Pacemaker, Artificial Neural Networks, Computer Machine Learning Magnetic Resonance Imaging
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Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices.

Scientific reports Jan 7, 2022
To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the...
Pacemaker, Artificial Clinical Decision Rules Models, Statistical Registries Atrial Fibrillation Female Aged Aged, 80 and over Male Cohort Studies ROC Curve Machine Learning Prospective Studies Republic of Korea Humans Artificial Intelligence
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Deep Learning-Based Algorithm for the Detection and Characterization of MRI Safety of Cardiac Implantable Electronic Devices on Chest Radiographs.

Korean journal of radiology Aug 19, 2021
OBJECTIVE: With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to d...
Algorithms Humans Electronics Deep Learning Magnetic Resonance Imaging Defibrillators, Implantable Pacemaker, Artificial
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Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement.

Circulation. Arrhythmia and electrophysiology Mar 9, 2021
Risk Factors Risk Assessment Treatment Outcome Action Potentials Aortic Valve Predictive Value of Tests Decision Support Techniques Prospective Studies Retrospective Studies Pacemaker, Artificial Cardiac Pacing, Artificial Humans Atrioventricular Block Heart Rate Machine Learning Transcatheter Aortic Valve Replacement
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Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE Jan 28, 2021
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...
Aged, 80 and over Male Pacemaker, Artificial Risk Assessment Retrospective Studies Female Aged Humans Arrhythmias, Cardiac Machine Learning Transcatheter Aortic Valve Replacement Forecasting Prosthesis Implantation Postoperative Complications Logistic Models Aortic Valve Stenosis
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A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices.

The American journal of cardiology Dec 29, 2020
Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm...
Radiography, Thoracic Defibrillators, Implantable Pacemaker, Artificial Machine Learning Algorithms Humans Image Interpretation, Computer-Assisted Neural Networks, Computer
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