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A Novel Application of Musculoskeletal Ultrasound Imaging
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Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound.

Xiao Yang1, Beilei Zhang1, Ying Liu1

  • 1Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.

Ultrasonic Imaging
|June 17, 2024
PubMed
Summary

This study introduces an automated method using ultrasound and deep learning to assess skeletal muscle strength, improving accuracy for athletes in rehabilitation and training. The novel approach achieves high classification accuracy for biceps brachii muscle strength.

Keywords:
ResNetdeep learningmusclestrength assessmentultrasound

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Skeletal muscle strength assessment is crucial for athletic rehabilitation and training.
  • Traditional methods for muscle strength evaluation are operator-dependent, potentially impacting accuracy.
  • There is a need for objective and reliable methods to assess muscle strength.

Purpose of the Study:

  • To develop and validate an automated method for evaluating skeletal muscle strength.
  • To leverage ultrasound imaging and deep learning for objective muscle strength assessment.
  • To improve the accuracy and reliability of muscle strength classification.

Main Methods:

  • Collected B-mode ultrasound data of the biceps brachii from athletes at various strength levels.
  • Trained a deep learning model using the collected ultrasound data.
  • Evaluated the method's effectiveness by testing biceps brachii contraction under different force levels.

Main Results:

  • The automated method achieved high classification accuracy for specific muscle strength grades (98% for grade 4, 96% for grade 6).
  • The overall average accuracy of the deep learning model was 93% and 87% for different assessments.
  • Experimental results demonstrated the method's ability to accurately classify muscle strength.

Conclusions:

  • The proposed automated method using ultrasound and deep learning accurately and effectively evaluates skeletal muscle strength.
  • This innovative approach offers a more objective and reliable alternative to traditional muscle strength assessment techniques.
  • The findings support the application of AI and medical imaging in sports science and rehabilitation.