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Toward Scalable Electromyography in Oncology: A Narrative Review of Normalization Challenges and Machine Learning

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Summary

Machine learning (ML)-predicted maximal voluntary contractions (MVCs) offer a scalable alternative to traditional EMG normalization in oncology. This approach enhances neuromuscular assessment for cancer patients, improving rehabilitation and survivorship care.

Keywords:
Electromyography (EMG)Machine Learning (ML)Maximal Voluntary Contraction (MVC)Neuromuscular ImpairmentOncology RehabilitationPredictive Modeling

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Area of Science:

  • Neuromuscular physiology
  • Biomedical engineering
  • Oncology rehabilitation

Background:

  • Electromyography (EMG) is vital for monitoring cancer patient recovery.
  • Traditional EMG normalization using maximal voluntary contraction (MVC) is often impractical for patients with cancer due to fatigue, pain, or treatment side effects.
  • This limits the widespread application of EMG in oncology settings.

Purpose of the Study:

  • To review the feasibility and clinical utility of using machine learning (ML) to predict MVCs as an alternative to direct measurement in oncology.
  • To explore how ML-predicted MVCs can overcome limitations of traditional EMG normalization in cancer care.

Main Methods:

  • A narrative review of peer-reviewed articles published between 2015 and 2025.
  • Searches were conducted across major scientific databases (PubMed, IEEE Xplore, etc.) using keywords related to EMG, oncology, MVC, ML, and rehabilitation.
  • Thirty-eight relevant studies were included in the analysis.

Main Results:

  • Traditional MVC normalization is frequently infeasible in cancer patients, posing safety risks and limiting assessment accuracy.
  • ML models, utilizing demographic, anthropometric, and submaximal EMG data, show significant promise for estimating MVCs indirectly.
  • These predictive models can improve accuracy, reduce patient burden, and facilitate broader EMG integration into monitoring and rehabilitation.

Conclusions:

  • ML-predicted MVCs can overcome barriers to EMG standardization in oncology, enhancing functional assessment and rehabilitation strategies.
  • This approach supports more accurate, patient-centered care by reducing reliance on maximal patient efforts.
  • Oncology nurses and rehabilitation specialists can integrate ML-supported EMG into clinical and home-based programs for adaptive, real-time interventions.