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Related Experiment Video

Updated: Jul 1, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Tongue feature dataset construction and real-time detection.

Wen-Hsien Chang1, Chih-Chieh Chen2, Han-Kuei Wu3,4

  • 1Graduate Institute of Chinese Medicine, School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan, Republic of China.

Plos One
|March 7, 2024
PubMed
Summary
This summary is machine-generated.

This study developed an automated system using deep learning for tongue diagnosis in Traditional Chinese Medicine (TCM). The AI model accurately detects tongue features in real-time, improving diagnostic efficiency and consistency.

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Area of Science:

  • Integrative Medicine
  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis

Background:

  • Traditional Chinese Medicine (TCM) tongue diagnosis offers objective clinical insights.
  • Current manual interpretation is time-consuming and subjective.
  • Automated systems can enhance efficiency and consistency in TCM diagnosis.

Purpose of the Study:

  • To develop an automated deep learning system for tongue feature detection.
  • To expedite and standardize TCM tongue diagnosis.
  • To create an interpretable and intuitive AI model for clinical use.

Main Methods:

  • Collected and manually annotated tongue images with features like fissures and coatings.
  • Trained a deep learning object detection model using annotated data.
  • Validated the model's real-time detection capabilities.

Main Results:

  • Created a high-quality annotated tongue feature dataset.
  • Achieved average precision of 47.67% (fissures), 58.94% (tooth marks), and 71.25% (thick, yellow coatings).
  • Demonstrated real-time tongue feature detection at over 40 FPS.

Conclusions:

  • A deep learning model for real-time tongue feature localization was successfully developed.
  • The model offers interpretability and intuitiveness, addressing limitations of typical neural networks.
  • The system shows significant feasibility for clinical application in TCM.