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Microwave Digital Twin Prototype for Shoulder Injury Detection.

Sahar Borzooei1,2, Pierre-Henri Tournier3, Victorita Dolean4

  • 1Laboratoire d'Electronique Antennes et Télécommunications (LEAT), Université Côte d'Azur, 06000 Nice, France.

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Summary
This summary is machine-generated.

This study introduces a microwave digital twin prototype for detecting rotator cuff tears (RCTs). This machine learning approach offers a more efficient method for diagnosing shoulder injuries compared to traditional imaging techniques.

Keywords:
SVM classificationmachine learningmicrowave sensing systemnumerical modelingtendon injury

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Machine Learning Applications

Background:

  • Rotator cuff tears (RCTs) are common shoulder injuries, with prevalence increasing significantly with age.
  • Existing diagnostic methods can be resource-intensive and time-consuming.

Purpose of the Study:

  • To develop and present a novel microwave digital twin prototype (MDTP) for the detection of rotator cuff tears (RCTs).
  • To leverage machine learning (ML) and advanced numerical modeling for efficient RCT diagnosis.

Main Methods:

  • Generated a generalizable dataset of scattering parameters using flexible numerical modeling.
  • Employed finite element discretization and the domain decomposition method for accelerated computations.
  • Utilized a support vector machine (SVM) for differentiating between healthy and injured shoulder models.

Main Results:

  • Successfully developed a functional microwave digital twin prototype (MDTP).
  • Demonstrated the capability of the ML-based approach to differentiate between healthy and injured shoulder models.
  • The proposed method showed higher efficiency in terms of memory and computing time compared to traditional imaging.

Conclusions:

  • The developed MDTP offers a promising, efficient, and accurate method for rotator cuff tear (RCT) detection.
  • This ML-driven approach addresses challenges associated with real-world data collection for diagnostic models.
  • The study highlights the potential of digital twin technology and machine learning in musculoskeletal injury diagnosis.