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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

Imaging Studies for Cardiovascular System II:Types of Echocardiography

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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.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...
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Ultrasonography01:17

Ultrasonography

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Related Experiment Video

Updated: Sep 9, 2025

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
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Real-Time Global Longitudinal Strain During Echocardiography: A Deep Learning Platform for Improved Workflow.

Vegard Holmstrøm1, Erik Smistad2, Stian Stølen3

  • 1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Clinic of Cardiology, St. Olavs University Hospital, Trondheim, Norway.

Journal of the American Society of Echocardiography : Official Publication of the American Society of Echocardiography
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning (DL) platform enables real-time, automated measurement of left ventricular global longitudinal strain (GLS). This AI-powered tool significantly reduces analysis time and improves image quality, making GLS more accessible for clinical use.

Keywords:
Artificial intelligenceAutomated measurementsDeep learningGlobal longitudinal strainLeft ventricular systolic function

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Last Updated: Sep 9, 2025

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Left ventricular global longitudinal strain (GLS) offers superior diagnostic and prognostic value compared to ejection fraction.
  • Current semi-automatic GLS analysis methods are hindered by time constraints and operator dependency, limiting widespread clinical adoption.

Purpose of the Study:

  • To evaluate a deep learning (DL) platform for automated, real-time GLS measurements.
  • To assess the feasibility, precision, and time-efficiency of DL-assisted echocardiographic acquisitions.
  • To determine if DL tools can enhance image quality metrics essential for accurate strain analysis.

Main Methods:

  • Development of a DL platform for fully automated, real-time GLS analysis with built-in quality control alerts.
  • Prospective study involving 50 patients with dual image set acquisition (DL platform vs. conventional workflow) by different operators.
  • Comparison of GLS measurements and image quality metrics between the DL platform and conventional methods.

Main Results:

  • The DL platform demonstrated high feasibility (94%) for GLS measurements.
  • Excellent correlation (r = 0.94) was observed between DL-based and manual GLS measurements, with minimal bias.
  • DL-assisted acquisition significantly reduced analysis time by 57% and improved image quality by reducing baseline drift.

Conclusions:

  • The DL platform offers a feasible, precise, and time-efficient solution for automated GLS measurement.
  • Real-time DL feedback during acquisition enhances image quality, supporting standardization and accuracy.
  • Clinical implementation of this DL platform can streamline echocardiographic workflows and improve laboratory efficiency.