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Related Concept Videos

Assessment of the Mouth01:26

Assessment of the Mouth

348
A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.
348
Tongue01:01

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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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Related Experiment Video

Updated: Aug 26, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical

Tao Jiang1, Zhou Lu1,2, Xiaojuan Hu3

  • 1Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai 201203, China.

Evidence-Based Complementary and Alternative Medicine : Ecam
|October 10, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning accurately classifies tongue images for traditional Chinese medicine (TCM), identifying features linked to age, gender, and metabolic diseases like hypertension.

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Area of Science:

  • Artificial Intelligence
  • Medical Diagnostics
  • Traditional Chinese Medicine

Background:

  • Intelligent tongue diagnosis is crucial for modernizing Traditional Chinese Medicine (TCM).
  • Identifying tongue shape and texture features presents a significant challenge in TCM tongue diagnosis.
  • This study explores deep learning for advanced tongue image analysis.

Purpose of the Study:

  • To apply deep learning techniques for analyzing tongue images.
  • To classify tongue images into seven categories using a deep learning model.
  • To investigate correlations between tongue features, demographics, and metabolic diseases.

Main Methods:

  • Utilized Faster Region-based Convolutional Neural Networks (Faster R-CNN) for tongue image classification.
  • Annotated 8676 tongue images into categories: fissured, tooth-marked, stasis, spotted, greasy coating, peeled coating, and rotten.
  • Evaluated model performance using accuracy, recall, precision, and F1-score on 3601 participants' images.

Main Results:

  • Achieved high performance with average accuracy (90.67%), recall (91.25%), precision (99.28%), and F1-score (95.00%).
  • Identified prevalence of tongue features: fissured (41.49%), tooth-marked (37.16%), and greasy coating (29.66%) were most common.
  • Found significant associations between tongue features (fissured, tooth-marked, spotted, greasy coating) and age/gender, and linked them to metabolic conditions like hypertension and NAFLD.

Conclusions:

  • The Faster R-CNN model demonstrates robust performance in tongue image classification.
  • Preliminary findings reveal significant relationships between tongue features, gender, age, and metabolic diseases.
  • Deep learning offers a promising approach for objective and modernized TCM tongue diagnosis.