AIMC Topic: Defibrillators, Implantable

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Active learning and margin strategies for arrhythmia classification in implantable devices.

Computers in biology and medicine Feb 13, 2025
BACKGROUND AND OBJECTIVES: The massive storage of cardiac arrhythmic episodes from Implantable Cardioverter Defibrillators (ICD) and the advent of new artificial intelligence algorithms are opening up new opportunities for electrophysiological knowle...
Supervised Machine Learning Arrhythmias, Cardiac Defibrillators, Implantable Humans Machine Learning Algorithms
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Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.

Journal of the American College of Cardiology Nov 20, 2024
BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional stati...
Female Hospitalization Aged Humans Machine Learning Heart Failure Defibrillators, Implantable Male Middle Aged Risk Assessment
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Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.

Current cardiology reports Oct 18, 2024
PURPOSE OF REVIEW: To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias.
Humans Wearable Electronic Devices Artificial Intelligence Arrhythmias, Cardiac Algorithms Defibrillators, Implantable
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Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Circulation journal : official journal of the Japanese Circulation Society Oct 1, 2024
BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however...
Arrhythmias, Cardiac Tachycardia, Ventricular Humans Defibrillators, Implantable Machine Learning Middle Aged Retrospective Studies Electrocardiography Female Cluster Analysis Aged Male
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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
Humans Artificial Intelligence Monitoring, Physiologic Defibrillators, Implantable Pacemaker, Artificial
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Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

Heart rhythm May 24, 2024
BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable.
Electrophysiologic Techniques, Cardiac Tachycardia, Ventricular Defibrillators, Implantable Ventricular Fibrillation Predictive Value of Tests Registries Machine Learning Retrospective Studies Female Aged Male Middle Aged Humans
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Ethical use of artificial intelligence to prevent sudden cardiac death: an interview study of patient perspectives.

BMC medical ethics Apr 4, 2024
BACKGROUND: The emergence of artificial intelligence (AI) in medicine has prompted the development of numerous ethical guidelines, while the involvement of patients in the creation of these documents lags behind. As part of the European PROFID projec...
Humans Artificial Intelligence Defibrillators, Implantable Death, Sudden, Cardiac Delivery of Health Care Qualitative Research
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Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Journal of applied clinical medical physics Feb 18, 2024
BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT).
Image Processing, Computer-Assisted Humans Radiotherapy, Image-Guided Magnetic Resonance Imaging Deep Learning Defibrillators, Implantable Artifacts
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Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models.

Heart rhythm Feb 12, 2024
BACKGROUND: Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE).
Risk Assessment Device Removal Defibrillators, Implantable Neural Networks, Computer Vena Cava, Superior Aged Machine Learning Male Biomarkers Middle Aged Retrospective Studies Humans Female
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Correlation analysis of deep learning methods in S-ICD screening.

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc Mar 15, 2023
BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of EC...
Female Humans Middle Aged Heart Aged Deep Learning Retrospective Studies Defibrillators, Implantable Male Electrocardiography Death, Sudden, Cardiac
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