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Related Concept Videos

Aortic Regurgitation I: Introduction01:15

Aortic Regurgitation I: Introduction

1.3K
IntroductionAortic regurgitation is characterized by the backward flow of blood from the aorta into the left ventricle during diastole and arises from the improper closure of the aortic valve. This condition results in left ventricular volume overload and can stem from both acute and chronic etiologies, each contributing uniquely to the disease's progression and symptomatology.Acute and Chronic CausesAcute aortic regurgitation often results from events that suddenly impair the integrity of the...
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Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

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Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
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Related Experiment Video

Updated: Apr 11, 2026

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart
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Automated Aortic Regurgitation Detection and Quantification: A Deep Learning Approach Using Multi-View

Christina Binder, Yuki Sahashi, Hirotaka Ieki

    Medrxiv : the Preprint Server for Health Sciences
    |April 1, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning model, EchoNet-AR, accurately assesses aortic regurgitation (AR) severity using echocardiography videos. This AI tool shows promise for clinical decision support in managing AR disease.

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    Area of Science:

    • Cardiovascular Imaging
    • Artificial Intelligence in Medicine
    • Medical Diagnostics

    Background:

    • Accurate assessment of aortic regurgitation (AR) severity is crucial for timely intervention and chronic disease management.
    • Doppler echocardiography is the standard for AR assessment, but its accuracy can be limited by image quality and the need for multi-view integration.
    • Existing methods require expert interpretation and can be subjective, highlighting the need for automated, objective assessment tools.

    Purpose of the Study:

    • To develop and validate a deep learning model for automated assessment of aortic regurgitation severity.
    • To evaluate the model's performance using multi-view color Doppler echocardiography videos.
    • To assess the generalizability of the model in an external validation cohort.

    Main Methods:

    • A convolutional neural network (R2+1D) was developed to classify AR severity from five standard echocardiographic views.
    • The model was trained on a large dataset of 47,638 videos from 32,396 studies at Cedars-Sinai Medical Center.
    • External validation was performed on 3,369 videos from 1,504 studies at Stanford Healthcare Center.

    Main Results:

    • The EchoNet-AR model demonstrated high accuracy in identifying at least moderate AR (AUC 0.95) and severe AR (AUC 0.97) in the training cohort.
    • Consistent performance was observed in the external validation cohort, with AUCs of 0.92 for at least moderate AR and 0.94 for severe AR.
    • The model showed robust performance across varying image quality, valve morphologies, and patient demographics, focusing on hemodynamically significant regions.

    Conclusions:

    • The EchoNet-AR model accurately classifies AR severity by synthesizing information from multiple echocardiographic views.
    • The model exhibits robust generalizability and potential as an automated clinical decision support tool for AR assessment.
    • Clinical interpretation remains essential, especially for complex cases involving multiple valve pathologies or altered hemodynamics.