Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD

Shixuan Dai; Xiaojing Guo; Shi Liu; Liping Tu; Xiaojuan Hu; Ji Cui; QunSheng Ruan; Xin Tan; Hao Lu; Tao Jiang; Jiatuo Xu

doi:10.1016/j.heliyon.2024.e29269

Application of intelligent tongue image analysis in Conjunction with microbiomes in the diagnosis of MAFLD

Heliyon. 2024 Apr 4;10(7):e29269. doi: 10.1016/j.heliyon.2024.e29269. eCollection 2024 Apr 15.

Authors

Shixuan Dai¹, Xiaojing Guo², Shi Liu¹, Liping Tu¹, Xiaojuan Hu¹, Ji Cui¹, QunSheng Ruan³, Xin Tan⁴, Hao Lu⁵, Tao Jiang¹, Jiatuo Xu¹

Affiliations

¹ Department of College of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Road, Shanghai, 201203, China.
² Department of Anesthesiology, Naval Medical University, No. 800, Xiangyin Road, Shanghai,200433, China.
³ Department of Software, Xiamen University, No. 422, Siming South Road, Siming District, Xiamen City, Fujian Province, 361005, China.
⁴ Department of Computer Science and Technology, East China Normal University, No. 3663, Zhongshan North Road, Shanghai, 200062, China.
⁵ Department of Endocrinology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 528, Zhangheng Road, Shanghai,200021, China.

Abstract

Background: Metabolic associated fatty liver disease (MAFLD) is a widespread liver disease that can lead to liver fibrosis and cirrhosis. Therefore, it is essential to develop early diagnosic and screening methods.

Methods: We performed a cross-sectional observational study. In this study, based on data from 92 patients with MAFLD and 74 healthy individuals, we observed the characteristics of tongue images, tongue coating and intestinal flora. A generative adversarial network was used to extract tongue image features, and 16S rRNA sequencing was performed using the tongue coating and intestinal flora. We then applied tongue image analysis technology combined with microbiome technology to obtain an MAFLD early screening model with higher accuracy. In addition, we compared different modelling methods, including Extreme Gradient Boosting (XGBoost), random forest, neural networks(MLP), stochastic gradient descent(SGD), and support vector machine(SVM).

Results: The results show that tongue-coating Streptococcus and Rothia, intestinal Blautia, and Streptococcus are potential biomarkers for MAFLD. The diagnostic model jointly incorporating tongue image features, basic information (gender, age, BMI), and tongue coating marker flora (Streptococcus, Rothia), can have an accuracy of 96.39%, higher than the accuracy value except for bacteria.

Conclusion: Combining computer-intelligent tongue diagnosis with microbiome technology enhances MAFLD diagnostic accuracy and provides a convenient early screening reference.

Keywords: Generative adversarial networks; MAFLD; Oral-intestinal microbiome; Tongue diagnosis.