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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

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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.
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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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Related Experiment Video

Updated: Sep 18, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
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Artificial Intelligence for Detection of Prognostically Significant Left Ventricular Dysfunction From

David Playford1, Simon Stewart2, Andrew Watts3

  • 1Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia.

JACC. Advances
|June 24, 2025
PubMed
Summary

An artificial intelligence algorithm accurately identifies left ventricular (LV) dysfunction using echocardiography. This AI tool reliably predicts mortality risk, even with preserved ejection fraction (EF) and missing data.

Keywords:
artificial intelligencediastolic dysfunctionechocardiographyheart failureleft ventricular dysfunctionsystolic dysfunction

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Echocardiography is crucial for assessing left ventricular (LV) function.
  • Identifying LV dysfunction can be challenging, especially with preserved ejection fraction (EF).
  • Current methods may struggle with incomplete or ambiguous echocardiographic data.

Purpose of the Study:

  • To evaluate the performance of an artificial intelligence LV dysfunction (AI-LVD) identification algorithm.
  • To determine the algorithm's ability to identify LV dysfunction from routine echocardiographic measurements.
  • To assess the prognostic value of AI-LVD, particularly in cases with preserved EF.

Main Methods:

  • Trained AI-LVD on large echocardiographic datasets (imputation and training cohorts).
  • Validated AI-LVD in a separate cohort of 81,509 patients with diverse EF categories.
  • Analyzed AI-LVD's correlation with all-cause mortality and its performance with missing data.

Main Results:

  • AI-LVD demonstrated sex-specific outputs correlating with LV dysfunction severity and mortality risk.
  • The algorithm showed significant prognostic capacity in patients with preserved EF.
  • Higher AI-LVD deciles were associated with substantially increased 5-year all-cause mortality in both men and women.

Conclusions:

  • A novel AI-LVD algorithm effectively identifies prognostically significant LV dysfunction using only echocardiographic data.
  • The AI-LVD is reliable even when key echocardiographic parameters are missing or EF is preserved.
  • This AI tool can be integrated into routine echocardiography for real-time reporting and risk stratification.