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Updated: Jun 13, 2025
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
Published on: April 14, 2023
Exploring hepatic fibrosis screening via deep learning analysis of tongue images
Xiao-Zhou Lu1, Hang-Tong Hu2, Wei Li2
1Department of Traditional Chinese Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Background:
Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate the effectiveness of deep learning-based analysis of tongue images for hepatic fibrosis screening.
Methods:
A total of 1083 tongue images were collected from 741 patients and divided into training, validation, and test sets. DenseNet-201, a convolutional neural network, was employed to train the AI model using these tongue images. The predictive performance of AI was assessed and compared with that of FIB-4, using real-time two-dimensional shear wave elastography as the reference standard.
Results:
The proposed AI model achieved an accuracy of 0.845 (95% CI: 0.79-0.90) and 0.814 (95% CI: 0.76-0.87) in the validation and test sets, respectively, with negative predictive values (NPVs) exceeding 90% in both sets. The AI model outperformed FIB-4 in all aspects, and when combined with FIB-4, the NPV reached 94.4%.
Conclusion:
Tongue inspection, with the assistance of AI, could serve as a first-line screening method for hepatic fibrosis.
