ArticleJournal of traditional and complementary medicine2024
Exploring hepatic fibrosis screening via deep learning analysis of tongue images.
Article in Journal of traditional and complementary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Accurate fatty liver disease diagnosis with a multi-source feature fusion model on the segmented tongue image dataset.Journal of advanced research · 2026Article
- Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- An AI-powered tongue image model for home-based monitoring of liver fibrosis.NPJ digital medicine · 2025Article
- Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation.Chinese medicine · 2025Article
- A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025Review
- Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.Frontiers in cellular and infection microbiology · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.