Electrocardiogram
What's New
Last Posted: May 28, 2024
- Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.
Chun-Ka Wong et al. Heart 2024 - Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.
Nicola Fink et al. Diagnostics (Basel) 2024 14(9) - Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.
Neal Yuan et al. NPJ Digit Med 2024 7(1) 96 - Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.
Odeh Adeyi Victor et al. Sensors (Basel) 2024 24(7) - A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms.
Utkarsh Gupta et al. Heliyon 2024 10(5) e26787 - An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.
Liong-Rung Liu et al. Heliyon 2024 10(5) e27200 - Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.
Jingjing Fan et al. Stress Health 2024 e3386 - Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.
Veer Sangha et al. medRxiv 2024 - A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction.
Liam Butler et al. Cardiovasc Digit Health J 2024 4(6) 183-190 - Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.
Samir Awasthi et al. EClinicalMedicine 2023 65102259 - Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach.
Wei-Ting Liu et al. Can J Cardiol 2023 - Single-lead ECG AI model with risk factors detects Atrial Fibrillation during Sinus Rhythm.
Stijn Dupulthys et al. Europace 2023 - A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.
Yu-Cheng Chen et al. Acta Cardiol Sin 2023 39(6) 913-928 - Patients With Hypertrophic Cardiomyopathy and Normal Genetic Investigations Have Few Affected Relatives.
Søren K Nielsen et al. J Am Coll Cardiol 2023 82(18) 1751-1761 - Mobile Health and Preventive Medicine.
Jill Waalen et al. Med Clin North Am 2023 107(6) 1097-1108 - A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?
Binay K Panjiyar et al. Cureus 2023 15(8) e43003 - A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease
JW Hughes et al, NPJ Digital Medicine, September 12, 2023 - Atrial Abnormalities in Brugada Syndrome: Evaluation With ECG Imaging.
Antonio Bisignani et al. JACC Clin Electrophysiol 2023 - Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.
Ibrahim Karabayir et al. Sci Rep 2023 13(1) 12290 - Cardiovascular disease/stroke risk stratification in deep learning framework: a review.
Mrinalini Bhagawati et al. Cardiovasc Diagn Ther 2023 13(3) 557-598
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About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:Jun 21, 2024
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