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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

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

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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Related Experiment Video

Updated: Jul 23, 2025

Ultrasonic Assessment of Myocardial Microstructure
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Artificial intelligence-assisted interpretation of systolic function by echocardiogram.

Natsumi Yamaguchi1, Yoshitaka Kosaka1, Akihiko Haga2

  • 1Department of Cardiovascular Medicine, Tokushima University Hospital, Tokushima, Japan.

Open Heart
|July 17, 2023
PubMed
Summary

Artificial intelligence (AI) improves left ventricular ejection fraction (LVEF) assessment in echocardiography. AI-LVEF reduces variability and enhances accuracy for less experienced readers, aiding clinical decisions.

Keywords:
Diagnostic ImagingEchocardiographyHeart Failure, Systolic

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Last Updated: Jul 23, 2025

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate echocardiographic assessment of left ventricular ejection fraction (LVEF) is crucial for clinical decision-making.
  • Artificial intelligence (AI) models show promise in accurately estimating LVEF.

Purpose of the Study:

  • To evaluate if an AI model can match expert LVEF reads.
  • To determine if AI-LVEF reduces interinstitutional variability among level 1 readers.

Main Methods:

  • Prospective, multicentre echocardiographic study with five level 1 cardiologists.
  • Readers assessed 48 cases twice: first without AI-LVEF, then with AI-LVEF displayed.
  • LVEF measurements were compared against a reference average from five expert readers.

Main Results:

  • Strong correlation found between AI-LVEF and reference LVEF (r=0.90, p<0.001).
  • AI-LVEF significantly reduced variability (SD reduced from 6.1±2.3 to 2.5±0.9, p<0.001).
  • Accuracy improved, with root-mean squared error decreasing from 7.5±3.1 to 5.6±3.2 (p=0.004).

Conclusions:

  • AI can assist level 1 readers in interpreting echocardiographic systolic function.
  • AI-LVEF integration aids consistent and accurate LVEF assessment across different institutions.