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Updated: Jan 19, 2026

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Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a

Federico M Asch1, Nicolas Poilvert2, Theodore Abraham3

  • 1MedStar Health Research Institute, Washington DC (F.M.A.).

Circulation. Cardiovascular Imaging
|September 17, 2019
PubMed
Summary
This summary is machine-generated.

A new machine learning algorithm accurately estimates left ventricular ejection fraction (LVEF) without manual boundary detection. This automated method shows similar accuracy to expert clinicians, improving echocardiographic analysis.

Keywords:
echocardiographyendocardiumleft ventricular functionmachine learningobserver variation

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Echocardiography relies on manual or automated endocardial boundary identification for left ventricular ejection fraction (LVEF) calculation.
  • Current automated methods can be prone to errors, limiting accuracy in some patients.
  • A novel approach is needed to circumvent border detection for more reliable LVEF estimation.

Purpose of the Study:

  • To develop and validate a fully automated machine learning algorithm for LVEF estimation.
  • To assess if the algorithm can estimate ventricular contraction directly, bypassing traditional border detection.
  • To compare the accuracy of the automated method against expert measurements.

Main Methods:

  • A machine learning algorithm (AutoEF) was trained on over 50,000 echocardiographic studies.
  • The algorithm was tested on 99 independent patients, comparing automated LVEF to expert-averaged measurements.
  • Statistical analyses included linear regression, Bland-Altman analysis, and assessment of sensitivity and specificity for detecting low LVEF.

Main Results:

  • Automated LVEF estimation was feasible in all patients with high consistency (mean absolute deviation 2.9%).
  • AutoEF demonstrated excellent agreement with reference values (r=0.95) and high sensitivity (0.90) and specificity (0.92) for detecting LVEF ≤35%.
  • Performance was comparable to that of clinical readers.

Conclusions:

  • A volume-independent machine learning algorithm for LVEF estimation is highly feasible.
  • The automated method achieves accuracy comparable to conventional volume-based measurements.
  • This AI-driven approach offers a promising alternative for accurate LVEF assessment.