Exploring hepatic fibrosis screening via deep learning analysis of tongue images

J Tradit Complement Med. 2024 Mar 6;14(5):544-549. doi: 10.1016/j.jtcme.2024.03.010. eCollection 2024 Sep.

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.

Keywords: Artificial intelligence; Chinese medicine; DenseNet; Hepatic fibrosis; Tongue images.