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Related Concept Videos

True Stress and True Strain01:28

True Stress and True Strain

Engineering stress is calculated as the load divided by the original, undeformed cross-sectional area. It approximates a material under load. This approximation is especially relevant post-yield in ductile materials. Though engineering stress-strain diagrams are often used for their convenience and accessibility, they can sometimes fall short in accuracy, particularly when dealing with large strain values.
In contrast, true stress offers a more precise portrayal. It is computed by dividing the...
Measurements of Strain01:27

Measurements of Strain

Strain quantifies the deformation of a material under force, typically measured as normal strain, which represents the change in length when compared with the original length. Electrical strain gauges are used for enhanced accuracy. These devices consist of a conductive wire mounted on a paper backing that adheres to the material's surface. These gauges operate on the piezoresistive effect, where the wire's electrical resistance changes in response to mechanical deformation. The strain gauge...
Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...

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Related Experiment Video

Updated: May 22, 2026

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Validation of a fully automated workflow for longitudinal strain using artificial intelligence.

Yuka Nomura1, Yukina Hirata1, Yoshihito Saijo2

  • 1Ultrasound Examination Center, Tokushima University Hospital, Tokushima, Japan.

Journal of Echocardiography
|May 21, 2026
PubMed
Summary

Artificial intelligence (AI) software shows reliable accuracy for left ventricular global longitudinal strain (LVGLS) measurements compared to experts. While AI shows promise for echocardiographic analysis, its correlation for left atrial reservoir strain (LASr) needs further investigation.

Keywords:
Artificial intelligenceDeep learningEchocardiographyStrain measurement

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Manual echocardiographic strain analysis is time-consuming and has inter-observer variability.
  • Artificial intelligence (AI) offers potential to streamline interpretation and reduce variability in echocardiography.
  • Evaluating AI accuracy for strain measurements is crucial for clinical adoption.

Purpose of the Study:

  • To assess the accuracy of AI-derived left ventricular global longitudinal strain (LVGLS) and left atrial reservoir strain (LASr) measurements.
  • To compare AI measurements against those made by expert sonographers.
  • To investigate AI performance across different patient subgroups.

Main Methods:

  • 150 patients' echocardiographic data were analyzed.
  • Fully automated AI software (Us2.ai) and expert sonographers independently measured LVGLS and LASr.
  • Intraclass correlation coefficients (ICC) and subgroup analyses were used to evaluate agreement and accuracy.

Main Results:

  • AI showed a strong correlation with expert measurements for LVGLS (ICC=0.83).
  • AI demonstrated a moderate correlation for LASr (ICC=0.77).
  • Accuracy for both metrics was maintained across various left ventricular ejection fraction (EF) and left atrial volume index (LAVi) categories.

Conclusions:

  • The AI software demonstrated reliable correlation with expert sonographer measurements for LVGLS.
  • The correlation for LASr was not as high, suggesting areas for improvement.
  • AI holds potential as a valuable tool for echocardiographic analysis in clinical practice with further development.