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Related Concept Videos

Mitral Valve Prolapse I: Introduction01:27

Mitral Valve Prolapse I: Introduction

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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...
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Mitral Valve Prolapse II: Assessment and Management01:22

Mitral Valve Prolapse II: Assessment and Management

425
IntroductionA range of clinical features characterizes Mitral Valve Prolapse (MVP), but it is important to note that many individuals with MVP are asymptomatic and may remain so throughout their lives. For those who do exhibit symptoms, the following are the key clinical features:Palpitations: This is a common symptom where individuals feel an irregular or rapid heartbeat. Palpitations in MVP are often due to arrhythmias such as premature ventricular contractions or supraventricular...
425
Mitral Stenosis II: Clinical features and Diagnostic Tests01:23

Mitral Stenosis II: Clinical features and Diagnostic Tests

220
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...
220
Mitral Valve Prolapse III: Nursing Management01:19

Mitral Valve Prolapse III: Nursing Management

291
The nursing management of Mitral Valve Prolapse, or MVP, centers around patient education, symptom monitoring, and lifestyle modifications.Patient Education on MVP Diagnosis and Heredity: Nurses should provide comprehensive education about MVP, a condition where the mitral valve does not close appropriately during heartbeats. This education often includes the condition's pathophysiology, symptoms, and potential complications, like arrhythmias or mitral regurgitation. Though not fully...
291
Mitral Regurgitation II: Clinical Features and Diagnostic Tests01:23

Mitral Regurgitation II: Clinical Features and Diagnostic Tests

371
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...
371
Mitral Regurgitation I: Introduction01:20

Mitral Regurgitation I: Introduction

421
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...
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Related Experiment Video

Updated: Jan 16, 2026

An Image Guided Transapical Mitral Valve Leaflet Puncture Model of Controlled Volume Overload from Mitral Regurgitation in the Rat
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A Deep Learning Model to Identify Mitral Valve Prolapse From the Echocardiogram.

Mostafa A Al-Alusi1, Emily S Lau2, Aeron M Small3

  • 1Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts, USA; Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA.

JACC. Cardiovascular Imaging
|October 1, 2025
PubMed
Summary

A new deep learning model, DROID-MVP, accurately detects mitral valve prolapse (MVP) from echocardiogram videos. Its predictions correlate with mitral regurgitation severity and future valve surgery, potentially automating diagnosis.

Keywords:
artificial intelligencedeep learningechocardiographymitral regurgitationmitral valve prolapse

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Mitral valve prolapse (MVP) affects 2-3% of the population and increases the risk of heart failure and sudden death.
  • Traditional diagnosis via transthoracic echocardiography is time-consuming and requires specialized expertise.

Purpose of the Study:

  • To develop and validate a deep learning model, DROID-MVP, for automated MVP classification from digital echocardiogram videos.
  • To assess the association between DROID-MVP predictions and clinical outcomes such as mitral regurgitation and mitral valve repair/replacement.

Main Methods:

  • Trained and validated DROID-MVP on over 1 million echocardiogram videos from 16,902 cardiology patients.
  • Externally validated the model on primary care patient cohorts from two major hospitals (MGH and BWH).
  • Assessed correlations between DROID-MVP scores, mitral regurgitation severity, and subsequent mitral valve surgery.

Main Results:

  • DROID-MVP demonstrated high accuracy in identifying MVP across internal and external validation sets (AUROC ranging from 0.947 to 0.968).
  • Higher DROID-MVP scores were significantly associated with moderate-to-severe mitral regurgitation (OR: 2.0) and future mitral valve repair or replacement (HR: 3.7).

Conclusions:

  • Deep learning model DROID-MVP effectively identifies MVP from echocardiogram videos.
  • Model predictions serve as digital markers associated with clinically significant MVP, potentially aiding in diagnosis and risk stratification.
  • This technology can automate MVP diagnosis and improve patient management.