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New Studies: AI Captures Electrocardiogram Patterns That Could Signal a Future Sudden Cardiac Arrest

DAIC

In a study published in Communications Medicine , David Ouyang, MD, assistant professor of Cardiology and Medicine at Cedars-Sinai, along with Chugh and fellow investigators trained a deep learning algorithm to study patterns in electrocardiograms, also known as ECGs, which are recordings of the heart’s electrical activity.

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The Electrocardiogram at 100 Years: History and Future

Circulation

Circulation, Volume 149, Issue 6 , Page 411-413, February 6, 2024.

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Abstract 4142589: GLP-1 receptor agonists slow the progression of subclinical cardiovascular risk as detected from 12-lead electrocardiograms

Circulation

Circulation, Volume 150, Issue Suppl_1 , Page A4142589-A4142589, November 12, 2024. However, it is unclear whether early subclinical therapeutic changes can be detected from routine 12-lead electrocardiograms (ECGs).Methods:We

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Abstract 4136784: Diagnostic significance and prognosis prediction of electrocardiogram plane QRS-T angle in patients with non-ST segment elevation acute coronary syndrome

Circulation

Circulation, Volume 150, Issue Suppl_1 , Page A4136784-A4136784, November 12, 2024. Objective:For patients with non-ST segment elevation acute coronary syndrome (NSTE-ACS), the diagnostic efficacy of traditional ECG is not sensitive and specific.

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6 Cardiology Board Review Questions That Will Help You Pass the Boards

BoardVitals - Cardiovascular

Question banks are a favorite exam preparation resource for Cardiologists that want to practice in the format of the exam.

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Understanding an Enlarged Heart (Cardiomegaly): Causes, Symptoms, and Treatment

MIBHS

Electrocardiogram (ECG/EKG) An ECG records the electrical activity of the heart and can help detect abnormalities in the heart’s rhythm that might contribute to enlargement. Regular physical activity can strengthen the heart and improve circulation. Avoid smoking and limit alcohol intake.

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Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning

Circulation: Arrhythmia and Electrophysiology

Circulation: Arrhythmia and Electrophysiology, Ahead of Print. BACKGROUND:It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. We compared 6 machine learning models to predict acute and long-term end points after ablation and used Shapley explainability analysis to contrast phenotypes.