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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,...
312

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Related Experiment Video

Updated: Jun 21, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
06:34

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Published on: October 28, 2020

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2-Dimensional Echocardiographic Global Longitudinal Strain With Artificial Intelligence Using Open Data From a

Catherine C Stowell1, James P Howard1, Tiffany Ng1

  • 1National Heart & Lung Institute, Imperial College, London, United Kingdom.

JACC. Cardiovascular Imaging
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

An open-source machine learning method for global longitudinal strain (GLS) analysis shows strong agreement with expert consensus, matching the performance of proprietary software and individual expert measurements.

Keywords:
artificial intelligenceechocardiographyglobal longitudinal strain

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Echocardiography

Background:

  • Global longitudinal strain (GLS) offers superior reproducibility and prognostic value compared to ejection fraction.
  • Automated and transparent methodologies are crucial for enhancing trust and adoption of GLS in clinical practice.

Purpose of the Study:

  • To develop and validate an open machine-learning-based global longitudinal strain (GLS) methodology.
  • To compare the performance of the developed method against multiexpert consensus and proprietary software.

Main Methods:

  • A multi-image neural network (Unity-GLS) was trained on 6,819 echocardiographic images.
  • External validation involved 100 echocardiograms with tracings and points labeled by 11 experts.
  • Performance was assessed using median absolute error and rank correlation against expert consensus and visual ranking.

Main Results:

  • The developed Unity-GLS method demonstrated a median absolute error of 1.3 GLS units, outperforming proprietary methods (2.5 and 2.2).
  • Unity-GLS achieved a correlation of 0.91 with expert consensus, surpassing individual expert measurements (0.85) and proprietary software (0.73, 0.79).
  • Rank correlation with expert visual ranking was 0.77 for Unity-GLS, comparable to individual experts (0.72) and proprietary software (0.70, 0.74).

Conclusions:

  • The open-source Unity-GLS approach provides GLS calculations that are as accurate and reliable as expert consensus and proprietary solutions.
  • The availability of training data, code, and trained networks promotes transparency and facilitates wider implementation.