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Identification of auricular acupoints using a convolutional neural network.

Junsuk Kim1, Youngseok Kim1, Da-Eun Yoon2

  • 1School of Information Convergence, Kwangwoon University, Seoul, Republic of Korea.

Integrative Medicine Research
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) accurately identifies auricular acupoints, showing higher consistency than traditional methods. This AI model offers a reliable tool for enhancing precision in acupuncture practice.

Keywords:
Artificial intelligenceAuricular acupointConvolutional neural networkDeep learningLocationPrediction

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Area of Science:

  • Medical Acupuncture
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Accurate acupoint identification is crucial for effective acupuncture therapy.
  • Artificial intelligence (AI) offers potential for automated and precise acupoint detection.
  • This study evaluates an AI model for auricular acupoint prediction against traditional methods.

Purpose of the Study:

  • To assess the accuracy and consistency of an AI model in identifying specific auricular acupoints (shenmen, lung, mouth).
  • To compare the AI model's performance with acupoint placements by a traditional Korean medicine practitioner.
  • To explore the potential of AI in improving acupoint localization in clinical practice.

Main Methods:

  • Utilized Mask R-CNN for ear region isolation and a CNN model for landmark detection on ear images from 39 individuals.
  • Trained the CNN model on resized images to predict three auricular acupoints, treating each as a separate coordinate for reliability.
  • Employed kernel density estimation to analyze acupoint distribution and compare model consistency.

Main Results:

  • AI-predicted acupoint centroids deviated by less than 3 pixels from practitioner placements.
  • Kernel density estimation revealed narrower acupoint distributions with AI predictions, indicating greater consistency.
  • The AI model demonstrated higher consistency in acupoint predictions across different images compared to manual placement.

Conclusions:

  • AI-driven auricular acupoint identification shows significant potential for improved accuracy and consistency.
  • Findings support the integration of AI as a reliable tool to enhance clinical precision in acupuncture.
  • This technology can advance the practice of acupuncture through enhanced localization accuracy.