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Related Experiment Video

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Warm Moxibustion and Scraping as a Traditional Chinese Medicine Therapy for Cervical Spondylosis Treatment
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A Convenient Non-harm Cervical Spondylosis Intelligent Identity method based on Machine Learning.

Nana Wang1,2, Xi Huang1, Yi Rao3

  • 1Institute of Computing Technology(ICT), Chinese Academy of Sciences(CAS), Beijing, China.

Scientific Reports
|November 28, 2018
PubMed
Summary

A new non-invasive method, EasiCNCSII, uses surface electromyography (sEMG) and AI to identify cervical spondylosis (CS). This convenient approach achieves high accuracy, offering a promising alternative for diagnosing this common orthopedic condition.

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Area of Science:

  • Biomedical Engineering
  • Orthopedics
  • Artificial Intelligence

Background:

  • Cervical spondylosis (CS) is a prevalent orthopedic condition often diagnosed through clinical assessment and costly imaging.
  • Current diagnostic methods can be invasive and expensive, necessitating more accessible alternatives.

Purpose of the Study:

  • To develop a convenient, non-harmful intelligent method for identifying cervical spondylosis (CS).
  • To leverage surface electromyography (sEMG) and artificial intelligence (AI) for improved CS diagnosis.

Main Methods:

  • A novel data acquisition method based on tendon theory and CS etiology was developed for sEMG collection.
  • A three-tier AI model, EasiAI, was designed, involving feature extraction, dimension reduction with EasiRF, and a gradient boosted regression tree classifier.
  • The EasiCNCSII system integrates sEMG data acquisition and AI-driven CS identification.

Main Results:

  • The EasiCNCSII system demonstrated superior performance compared to four common machine learning models.
  • Achieved a mean accuracy of 91.02%, mean sensitivity of 97.14%, mean specificity of 81.43%, and mean AUC of 0.95.

Conclusions:

  • The proposed EasiCNCSII method provides an effective and convenient non-invasive approach for cervical spondylosis identification.
  • This AI-powered sEMG analysis offers a promising advancement in the diagnosis of CS, potentially reducing healthcare costs and improving patient accessibility.