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Related Concept Videos

Mitral Stenosis II: Clinical features and Diagnostic Tests01:23

Mitral Stenosis II: Clinical features and Diagnostic Tests

Mitral stenosis is a heart condition in which the mitral valve, which allows blood to flow from the left atrium to the left ventricle, becomes narrowed or stenotic. This narrowing hinders blood flow and leads to clinical symptoms requiring specific medical evaluations and management strategies. The following overview outlines the clinical symptoms, assessments, diagnostic findings, prevention methods, and treatments for mitral stenosis.Clinical ManifestationsDyspnea (shortness of breath): This...
Mitral Regurgitation II: Clinical Features and Diagnostic Tests01:23

Mitral Regurgitation II: Clinical Features and Diagnostic Tests

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...
Mitral Valve Prolapse I: Introduction01:27

Mitral Valve Prolapse I: Introduction

IntroductionThe mitral valve, one of the heart's four valves, regulates blood flow. These valves have flaps that open and close to direct blood properly through the heart and body. During each heartbeat, the flaps open for blood to pass through and seal shut to prevent backflow. Specifically, the mitral valve opens to allow blood flow from the heart's upper left chamber to the lower left chamber. It then closes securely as the lower left chamber contracts to pump blood to the body, preventing...
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion, evaluates...
Mitral Regurgitation I: Introduction01:20

Mitral Regurgitation I: Introduction

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...
Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for diagnosing...

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Related Experiment Video

Updated: Jun 17, 2026

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice
12:12

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice

Published on: February 14, 2017

Deep Learning-Based Multiclass Classification of Mitral Valve Etiologies Using Limited B-Mode and Color Doppler

Dawun Jeong1, Moon-Seung Soh2, Jaeik Jeon3

  • 1Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea.

Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography
|June 15, 2026
PubMed
Summary

A deep learning framework accurately classifies mitral valve (MV) etiologies from routine echocardiograms. This AI tool aids consistent MV evaluation, complementing expert interpretation and quantitative analysis.

Keywords:
Deep LearningEchocardiographyEtiology ClassificationMitral Valve DiseaseMulticlass Classification

Related Experiment Videos

Last Updated: Jun 17, 2026

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice
12:12

Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice

Published on: February 14, 2017

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate mitral valve (MV) etiologic classification is crucial for patient management but relies on expert interpretation.
  • Automated morphologic analysis using artificial intelligence (AI) is limited in routine echocardiography.

Purpose of the Study:

  • To develop and validate a deep learning (DL) framework for classifying major MV etiologies using limited transthoracic echocardiography (TTE) views.
  • To assess the DL model's performance across different mitral regurgitation (MR) severities and image quality (IQ) strata.

Main Methods:

  • A multi-view DL model was trained on 4,344 TTE examinations to classify five MV etiologies.
  • Validation was performed on an internal test set and an independent external test set (2,262 TTE examinations).
  • Subgroup analyses included MR severity and automated IQ.

Main Results:

  • The DL model demonstrated robust performance in classifying MV etiologies on both internal (AUROC 0.968-0.997) and external (AUROC 0.931-0.992) datasets.
  • Performance remained stable across different image quality levels and was comparable to expert assessment.
  • The model correctly identified at least one expert-assigned etiology in 85.7% of cases with multiple MV etiologies.

Conclusions:

  • Deep learning analysis of routine TTE views enables reliable multiclass classification of MV etiologies.
  • This AI-driven approach can enhance the consistency and scalability of MV evaluation in clinical practice.
  • The DL framework serves as a valuable adjunct to quantitative automation and expert visual assessment.