Exploring hepatic fibrosis screening via deep learning analysis of tongue images
Journal of Traditional and Complementary Medicine(2024)
摘要
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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关键词
Chinese medicine,Tongue images,Artificial intelligence,DenseNet,Hepatic fibrosis
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