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Related Concept Videos

Tongue01:01

Tongue

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The human tongue is a fascinating and complex organ, responsible for various essential functions such as swallowing, speech, and taste. It is also subject to various conditions and diseases. In this article, we delve into the anatomy of the tongue, its roles, and some common conditions that can affect it.
Anatomical Position in the Oral Cavity
The tongue is located within the oral cavity, also known as the mouth. It is attached to the floor of the mouth by a fold of mucous membrane called the...
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The surface of the tongue is covered with various small bumps called papillae, which either distribute what has been ingested (filiform papillae) or contain the sensory taste (or gustatory) receptor cells (fungiform, circumvallate, and foliate papillae). Embedded within each taste-related papilla are the taste buds—clusters of 30 to 100 gustatory receptor cells.
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Related Experiment Video

Updated: Sep 17, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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TonguExpert: A Deep Learning-Based Algorithm Platform for Fine-Grained Extraction and Classification of Tongue

Ting Li1, Ling Zuo2, Pengyu Wang1

  • 1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China.

Phenomics (Cham, Switzerland)
|July 3, 2025
PubMed
Summary

Tongue analysis for disease detection is now automated with TonguExpert. This platform uses deep learning on a large dataset to accurately extract tongue phenotypes, improving diagnosis and making data publicly available.

Keywords:
Automated platformDeep learningTonguExpertTongue imagesTongue phenotype extractionTraditional Chinese medicine

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Biomedical Imaging

Background:

  • Tongue analysis is a promising tool for disease detection and health monitoring, particularly in traditional Chinese medicine.
  • Subjectivity and lack of large datasets hinder automated tongue diagnosis.
  • Existing deep learning methods struggle with subtle tongue details.

Purpose of the Study:

  • To develop an automated deep learning framework for tongue image analysis and phenotype extraction.
  • To create a free platform, TonguExpert, for archiving and analyzing tongue images.
  • To establish the largest publicly available dataset of tongue images and phenotypes.

Main Methods:

  • Developed a deep learning framework for tongue segmentation and phenotype extraction.
  • Utilized a dataset of 5992 tongue images from a Chinese population.
  • Extracted 773 phenotypes, including global and local features, and predicted labels.

Main Results:

  • TonguExpert achieved high accuracy in classifying tongue color (ROC-AUC 0.89-0.99), fissures (0.97), and tooth marks (0.88).
  • The model demonstrated good generalization capabilities on external datasets for predicting new phenotypes like greasy coating.
  • Released the largest publicly available dataset of tongue images and associated phenotypes.

Conclusions:

  • TonguExpert significantly outperforms manual classification, advancing automated tongue diagnosis.
  • The platform and dataset facilitate wider clinical adoption and future research in tongue diagnosis.
  • This work paves the way for expanded applications of automated tongue analysis in healthcare.