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Related Concept Videos

Aneurysm II: Clinical Manifestations and Diagnostic Studies01:21

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Thoracic, aortic arch and abdominal aneurysms are significant vascular conditions that can present with various clinical manifestations and lead to serious complications. Understanding these manifestations and the appropriate diagnostic studies is essential for effective management and treatment.Thoracic Aortic AneurysmsThoracic aortic aneurysms often remain asymptomatic until they reach a size that impinges on adjacent structures. They typically cause deep, diffuse chest pain that radiates to...
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A Comparative Study of Machine Learning and Algorithmic Approaches to Automatically Identify the Yield Point in

Timothy K Chung1, Joseph Kim2, Pete H Gueldner1,3

  • 1Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260.

Journal of Biomechanical Engineering
|February 7, 2024
PubMed
Summary

This study developed a machine learning model to automatically identify the yield point in soft tissues, improving objective biomechanical analysis. The model achieved a 5% median error, offering accurate assessment of tissue mechanical properties.

Keywords:
algorithmic identificationbiomechanicsmachine learningmechanical testingproportional limityield point

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Area of Science:

  • Biomechanics
  • Materials Science
  • Biomedical Engineering

Background:

  • Characterizing soft tissue mechanical behavior is crucial for understanding tissue integrity.
  • Identifying the yield point, indicating irreversible microstructural damage, is challenging in soft tissues due to nonlinear behavior and subjective visual inspection methods.
  • Automating yield point identification is needed for objective and efficient biomechanical analysis.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for objective identification of the yield point in biological soft tissues.
  • To overcome the subjectivity and limitations of manual visual inspection for yield point determination.
  • To enhance the automation of biomechanical analysis pipelines for soft tissues.

Main Methods:

  • Collected 279 uniaxial extension curves from various aortic tissue specimens (aneurysmal/nonaneurysmal, longitudinal/circumferential).
  • Utilized expert adjudication to label yield points on the stress-strain curves.
  • Trained a machine learning model on the labeled data to predict the yield point.

Main Results:

  • The trained ML model demonstrated high accuracy in identifying the yield point across different aortic tissue types.
  • The model achieved a median error of 5% in estimating yield stress compared to expert assessments.
  • The study confirms the feasibility of using ML for objective biomechanical property assessment in soft tissues.

Conclusions:

  • Machine learning provides an accurate and objective method for determining the yield point in soft tissues.
  • This automated approach can significantly improve the reliability and efficiency of soft tissue biomechanical analysis.
  • Further validation and refinement of the ML model are planned, incorporating visual damage inspection.