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Related Concept Videos

Mitral Valve Prolapse II: Assessment and Management01:22

Mitral Valve Prolapse II: Assessment and Management

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 tachycardia.
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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: May 10, 2026

Investigating Aortic Valve Calcification via Isolation and Culture of T Lymphocytes using Feeder Cells from Irradiated Buffy Coat
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Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs

Annie E Bowles1, Julie A Lynch2, Francisca Bermudez3

  • 1VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.

JACC. Advances
|December 17, 2025
PubMed
Summary

A new natural language processing (NLP) system accurately identifies patients with bicuspid aortic valve (BAV) from echocardiographic reports, enabling large-scale retrospective studies. This tool aids in diagnosing this common congenital heart defect.

Keywords:
bicuspid aortic valvecongenital heart diseaseechocardiographyelectronic health recordsnatural language processing

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Area of Science:

  • Cardiovascular Medicine
  • Medical Informatics
  • Computational Biology

Background:

  • Bicuspid aortic valve (BAV) is the most prevalent congenital heart defect, often diagnosed late due to varied symptoms.
  • Lack of a specific BAV diagnosis code before October 2024 hindered retrospective patient identification.

Purpose of the Study:

  • To develop and validate a natural language processing (NLP) system for automated extraction of heart valve morphology from echocardiographic reports.
  • To specifically focus on the accurate detection of bicuspid aortic valve (BAV).

Main Methods:

  • A rule-based NLP system utilizing MedSpaCy was developed to analyze echocardiographic reports.
  • The system was trained on 555 annotated reports and validated on 170 reports, focusing on valve leaflet structure identification.

Main Results:

  • The NLP system demonstrated high performance for BAV detection with precision (0.925), sensitivity (0.939), and F1-score (0.932).
  • Applied to over 14 million echocardiographic documents, the system identified 83,461 patients with BAV (2.40%).
  • High concordance (86.1%) was observed with ICD-10 code Q23.81, with manual review confirming accuracy in discordant cases.

Conclusions:

  • This NLP approach facilitates large-scale retrospective identification of BAV patients from clinical text.
  • It establishes the largest BAV cohort to date, supporting future cardiovascular research.
  • The system aids in clinical decision-making and timely diagnosis of BAV.