Journal of Shandong University (Health Sciences) ›› 2024, Vol. 62 ›› Issue (1): 38-47.doi: 10.6040/j.issn.1671-7554.0.2023.0435

• Clinical Medicine • Previous Articles    

Construction of a machine learning-based tongue diagnosis model for gastrointestinal diseases

ZHANG Jinghui1, WANG Juan2, ZHAO Yujie3, DUAN Miao1, LIU Yiran4, LIN Minjuan1, QIAO Xu4, LI Zhen1, ZUO Xiuli1   

  1. 1. Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China;
    2. Hospital Development Center of Qingdao Municipal Health Commission, Qingdao 266001, Shandong, China;
    3. Department of Gastroenterology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250011, Shandong, China;
    4. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Published:2024-02-02

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Abstract: Objective To construct a machine learning(ML)-based tongue diagnostic model for the diagnosis of gastrointestinal diseases so as to realize the non-invasive auxiliary diagnosis of common gastrointestinal diseases in a more convenient and faster way. Methods Tongue images of 948 subjects who underwent electronic endoscopy were prospectively collected. After quality screening, 3,140 images that met the application criteria were finally obtained to constitute the tongue image data set, which underwent preprocessing, feature extraction and pattern recognition. On the basis of traditional machine learning methods, a method to realize information fusion in terms of feature fusion and decision fusion was proposed, and a tongue diagnosis model of gastrointestinal diseases was constructed. Results The area under the curve(AUC)of the model was 0.808, which was higher than that of the single handcrafted feature(AUC=0.769)and deep feature(AUC=0.779)models. Sample enhancement using the BSFCM hybrid sampling method improved the models performance for Helicobacter pylori(H. pylori) infection(AUC=0.816), bile reflux(AUC=0.829), reflux esophagitis(AUC=0.800), gastric erosion(AUC=0.833)and duodenal erosion(AUC=0.818). Conclusion The intelligent tongue diagnostic model based on ML constructed in this study shows a high degree of differentiation for a variety of gastrointestinal diseases, and may provide a new and valuable idea and method for the diagnosis and screening of gastrointestinal diseases.

Key words: Artificial intelligence, Tongue image, Gastrointestinal diseases, Machine learning, Tongue diagnosis model

CLC Number: 

  • R241.25
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