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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

297
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,...
297

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Related Experiment Video

Updated: Jun 7, 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

3.9K

A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to

Magnus Rogstadkjernet1, Sigurd Z Zha2, Lars G Klæboe3

  • 1Institute for Clinical Medicine, University of Oslo, Oslo, Norway. Magnus.Rogstad@gmail.com.

BMC Medical Imaging
|November 11, 2024
PubMed
Summary

Deep learning (DL) automates strain calculations in echocardiography, achieving results comparable to cardiologists. This approach enhances the clinical adoption and reproducibility of speckle tracking echocardiography (STE).

Keywords:
Artificial intelligenceAutomationDeep learningSpeckle-tracking echocardiographyStrain rate imaging

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Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Speckle tracking echocardiography (STE) quantifies left ventricular (LV) deformation, crucial for assessing LV function.
  • Manual region of interest (ROI) outlining in STE is time-consuming and can affect strain value accuracy.
  • Standardizing STE is vital for its increasing clinical application.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for automating strain calculations in echocardiography.
  • To integrate the DL model with existing echocardiographic software for seamless clinical workflow.
  • To assess if DL-automated strain calculations achieve fidelity comparable to trained cardiologists.

Main Methods:

  • A DL model (EfficientNetB1) was trained on 672 echocardiographic exams with cardiologist-defined ROIs.
  • Various techniques, including dataset size, quality, augmentations, and transfer learning, were evaluated.
  • DL-predicted ROIs were used in commercial software for automated strain calculation.

Main Results:

  • DL-automated strain calculations showed an average absolute difference of 0.75 for GLS and 1.16 for LS compared to human operators.
  • Bland-Altman analysis indicated no significant bias, with fewer outliers in lower longitudinal strain (LS) ranges.
  • No significant performance variations were observed based on tested data properties or techniques.

Conclusions:

  • Deep learning-assisted automated strain measurements are feasible and demonstrate results within interobserver variability.
  • Automating STE with DL can simplify and standardize strain analysis in clinical practice and research.
  • This technology has the potential to improve the adoption and reproducibility of STE parameters.