Developing a deep learning model for the automated monitoring of acupuncture needle insertion: enhancing safety in traditional acupuncture practices.

Journal: BMC complementary medicine and therapies
PMID: 40102810

Abstract

BACKGROUND: Acupuncture is a widely practiced traditional therapy, yet safety concerns, particularly needle breakage and retention, remain critical issues that can lead to complications such as infections, organ injury, or chronic pain. This study aimed to develop a deep learning model to monitor acupuncture needle insertion, detect instances of needle breakage, and prevent needle retention, ultimately improving patient safety and treatment outcomes.

Authors

  • Shun-Ku Lin
    Department of Chinese medicine, Renai Branch, Taipei City Hospital, Taipei, Taiwan.
  • Chien-Kun Su
    Department of Electrical Engineering, Chung Hua University, Hsinchu, Taiwan.
  • Melnard Rome C Mercado
    Department of Electrical Engineering, Chung Hua University, Hsinchu, Taiwan.
  • Syu-Jyun Peng
    Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsin-Chu, Taiwan; Institute of Electronics, National Chiao Tung University, Hsin-Chu, Taiwan. Electronic address: blue.year@msa.hinet.net.

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