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Related Concept Videos

Aortic Regurgitation II: Clinical Features and Diagnostic Tests01:22

Aortic Regurgitation II: Clinical Features and Diagnostic Tests

418
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...
418
Aortic Regurgitation I: Introduction01:15

Aortic Regurgitation I: Introduction

496
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...
496
Mitral Regurgitation I: Introduction01:20

Mitral Regurgitation I: Introduction

423
Mitral regurgitation is characterized by the backward circulation of blood from the left ventricle to the left atrium during systole, a phase of the cardiac cycle when the heart contracts and pumps blood out of the chambers. This abnormal flow occurs primarily due to the dysfunction of the mitral valve or its supporting structures, which include the mitral leaflets, chordae tendineae, annulus, and papillary muscles.Etiology and Mechanisms:Primary Mitral Regurgitation: This type arises from...
423
Mitral Regurgitation II: Clinical Features and Diagnostic Tests01:23

Mitral Regurgitation II: Clinical Features and Diagnostic Tests

381
Mitral regurgitation (MR) is a valvular heart disorder in which the mitral valve fails to close tightly, allowing blood to leak backward into the heart. Understanding the clinical manifestations, assessment, diagnostic findings, and medical management of MR is crucial to effectively managing affected patients.Clinical Manifestations of Mitral RegurgitationMitral regurgitation can be acute or chronic, each presenting differently and requiring different approaches:1. Acute Mitral...
381

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Related Experiment Video

Updated: Jan 17, 2026

In vitro Assessment of Aortic Regurgitation Using Four-Dimensional Flow Magnetic Resonance Imaging
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Deep learning for atrioventricular regurgitation diagnosis: an external validation study.

Ido Cohen1,2, Jeffrey G Malins3, Michal Cohen-Shelly1,4,5

  • 1Leviev Cardiovascular Institute, Sheba Medical Center, Derech Sheba 2, Tel HaShomer, Ramat Gan 52621, Israel.

European Heart Journal. Digital Health
|September 23, 2025
PubMed
Summary

Artificial intelligence (AI) shows high accuracy in diagnosing mitral and tricuspid regurgitation from echocardiograms, potentially improving access to cardiac diagnostics. Further validation is needed for real-world application.

Keywords:
Artificial intelligenceAtrioventricular valve regurgitationEchocardiographyExternal validationMachine learningMitral regurgitationTricuspid regurgitation

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Mitral and tricuspid regurgitation are prevalent in older adults, causing significant morbidity and mortality.
  • Transthoracic echocardiography (TTE) is the standard for diagnosis but has limited accessibility.
  • AI-based analysis of echocardiograms offers a potential solution to bridge this diagnostic gap.

Purpose of the Study:

  • To externally validate a deep learning algorithm for classifying atrioventricular regurgitation severity.
  • To assess the algorithm's performance against cardiologist interpretations using TTE studies.

Main Methods:

  • A deep learning algorithm was validated using TTE studies from the Mayo Clinic Health System.
  • The model analyzed echocardiographic images to classify mitral regurgitation (MR) and tricuspid regurgitation (TR) severity.
  • Performance was evaluated using binary and ordinal classification schemes, with area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Main Results:

  • The AI model achieved high performance for both MR (AUC 0.98, 95% CI: 0.97-0.99) and TR (AUC 0.96, 95% CI: 0.94-0.98).
  • Specific performance metrics included 91% accuracy, 95% sensitivity, and 89% specificity for MR, and 84% accuracy, 91% sensitivity, and 80% specificity for TR.
  • The model generated predictions for 578 out of 1541 eligible TTE studies.

Conclusions:

  • The AI model demonstrated high diagnostic performance in identifying clinically significant atrioventricular regurgitation when predictions were generated.
  • Findings support the feasibility of AI-assisted echocardiography in diverse patient populations.
  • Technical alignment between AI model requirements and local echocardiographic acquisition practices is crucial for real-world applicability.