IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v10y2025i2p703-714.html
   My bibliography  Save this article

Evaluation of AI-Assisted Ultrasound-Guided Galvanic Therapy (AAUGGT) for the Treatment of Inflammatory-Induced Pain vs other Modalities

Author

Listed:
  • Kaneez Abbas

    (Athreya Med Tech)

  • Majd Oteibi

    (Validus Institute Inc)

  • Danesh Khazaei

    (Portland State University)

  • Bala Balaguru

    (Athreya Med Tech)

  • Faryar Etesami

    (Portland State University)

  • Hadi Khazaei

    (Athreya Med Tech)

Abstract

Inflammatory-induced pain and discomfort remain significant contributors to the global burden of chronic diseases, affecting quality of life and productivity. (1) Current therapeutic options often involve pharmacological interventions with limited efficacy and potential side effects. Galvanic therapy, which uses low-level electrical stimulation, has shown promise in modulating inflammation and pain pathways. (2) When combined with precision imaging techniques like ultrasound and cutting-edge artificial intelligence (AI), this approach can provide personalized, non-invasive, and effective treatments.(3) This project proposes the development of an AI-Assisted Ultrasound-Guided Galvanic Therapy (AAUGGT) system that leverages real-time AI algorithms for inflammation detection and treatment optimization. Our research will validate the efficacy of this technology in reducing inflammatory responses and pain while improving treatment precision. The proposed study has the potential to revolutionize non-invasive pain management by providing an affordable and scalable alternative to existing therapeutic strategies.

Suggested Citation

  • Kaneez Abbas & Majd Oteibi & Danesh Khazaei & Bala Balaguru & Faryar Etesami & Hadi Khazaei, 2025. "Evaluation of AI-Assisted Ultrasound-Guided Galvanic Therapy (AAUGGT) for the Treatment of Inflammatory-Induced Pain vs other Modalities," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(2), pages 703-714, February.
  • Handle: RePEc:bjf:journl:v:10:y:2025:i:2:p:703-714
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrias/digital-library/volume-10-issue-2/703-714.pdf
    Download Restriction: no

    File URL: https://rsisinternational.org/journals/ijrias/articles/evaluation-of-ai-assisted-ultrasound-guided-galvanic-therapy-aauggt-for-the-treatment-of-inflammatory-induced-pain-vs-other-modalities/
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:journl:v:10:y:2025:i:2:p:703-714. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrias/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.