Application of computer tongue image analysis technology in the diagnosis of NAFLD.

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

Tongue color clustering and visual application based on 2D information.

PURPOSE: Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way. METHODS: A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI). RESULTS: The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively. CONCLUSIONS: The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.

Clinical epidemiology of gastric cancer in Hehuang valley of China: a 10-year epidemiological study of gastric cancer.

AIM: To investigate the clinical epidemiological characteristics of gastric cancer in the Hehuang valley, China, to provide a reference for treatment and prevention of regional gastric cancer. METHODS: Between February 2003 and February 2013, the records of 2419 patients with gastric cancer were included in this study. The patient's characteristics, histological and pathological features, as well as the dietary habits of the patients, were investigated. RESULTS: The clinical data showed that adenocarcinoma was the leading histological type of gastric cancer in this area. Characteristics of gastric cancer in different ethnic groups and age showed that the 60.55-65.50 years group showed the high incidence of gastric cancer in all ethnic groups. There were more male gastric cancer patients than female. Intestinal was the most common type of gastric cancer in the Hehuang valley. There was no significant difference in the proportion of sex in terms of Helicobacter pylori infection. The impact of dietary habits on gastric cancer showed that regular consumption of fried or grilled food, consumption of high-salt, high-fat and spicy food and drinking strong Boiled brick-tea were three important factors associated with gastric cancer in males and females. CONCLUSION: Differences existed in race, sex, and age of patients according to the epidemiology of gastric cancer in the Hehuang valley. Moreover, dietary habits was also an important factor contributing to gastric cancer.