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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
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Electrocardiogram01:29

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography
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Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the

Akhil Vaid1, Kipp W Johnson2, Marcus A Badgeley3

  • 1Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

JACC. Cardiovascular Imaging
|October 17, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning models can now quantify left and right ventricular dysfunction using ECGs, offering a new tool for rapid cardiac assessment. These models show promise for early detection and patient prioritization in heart failure cases.

Keywords:
artificial intelligenceechocardiographyelectrocardiogramleft heart failureleft ventricular ejection fractionright heart failure

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep learning (DL) on electrocardiograms (ECGs) offers rapid cardiac function evaluation.
  • Existing DL tools primarily focus on left ventricular (LV) dysfunction, with limited capacity.
  • No DL tools currently exist for estimating right ventricular (RV) function.

Purpose of the Study:

  • Develop DL models for comprehensive quantification of LV and RV dysfunction.
  • Utilize ECG data for assessing ventricular function in a large, diverse patient cohort.
  • Create novel DL tools to address limitations in current cardiac diagnostic workflows.

Main Methods:

  • A multicenter study involving 5 New York City hospitals for internal testing and external validation.
  • Development of novel DL models for LV ejection fraction (LVEF) classification and regression.
  • Utilized natural language processing (NLP) to extract RV function data from echocardiogram reports.

Main Results:

  • Achieved high area under curve (AUC) values for LVEF classification (0.94 internal, 0.94 external).
  • Demonstrated strong performance in LVEF regression with a mean absolute error of 5.84% (internal) and 6.14% (external).
  • Successfully predicted a composite RV outcome with an AUC of 0.84 in both internal and external validation.

Conclusions:

  • DL applied to ECG data can yield cost-effective screening and diagnostic tools for both LV and RV dysfunction.
  • These tools can bridge the gap between ECGs and echocardiography, improving patient management.
  • Enables prioritization of patients with potential biventricular disease for timely interventions.