This paper summarized the clinical experience of Prof. LI Diangui in treating gastroesophageal reflux disease （GERD） based on the theory of turbidity-toxin in liver. It is believed that internal accumulation of turbidity-toxin and liver depression with stomach counterflow are the main pathogenesis of GERD， and thus the therapeutic methods of resolving turbidity and resolving toxins， regulating the liver and harmonizing the stomach are proposed. In clinical practice， GERD is divided into the early stage， middle stage and late stage. For the early stage， the modified Huazhuo Shugan Hewei Formula （化浊疏肝和胃方） is used to regulate qi and remove turbidity， soothe the liver and harmonize the stomach； for the middle stage， the modified Huazhuo Qingre Zhisuan Formula （化浊清热制酸方） is applied to clear heat， direct the turbid downward， and resolve toxins； for the late stage， the modified Yiwei Decoction （益胃汤） is adopted to replenish qi， nourish yin and simultaneously resolve turbidity-toxin. Throughout the treatment process， attention should be paid to protecting the spleen and stomach， and the medication could be modified according to changes of individual condition.

Objective To train an automatic recognition and classification model of ulcerative colitis colonoscopy image based on ResNet,and to test its accuracy,in order to help doctors improve the clinical detection rate and classification accuracy of ulcerative colitis.Methods A total of 4000 colonoscopy images were retrospectively collected from the Colonoscopy Center of Hebei Hospital of Traditional Chinese Medicine from January 2018 to October 2023,and were divided into normal group,mild group,moderate group and severe group according to Mayo endoscopic scoring criteria,with 1000 images for each group.After pre-processing such as brightness adjustment and Angle rotation,the number of images was expanded to 20,000,and the data set was randomly divided into training set,verification set and test set according to the ratio of 7∶2∶1.The training set and verification set are input into the ResNet model to learn and test its stability.After all training is completed,the accuracy of the model is recorded through the test set,and the accurate regression curve is made to evaluate the classification effect of the model.Results In the test set,the accuracy of classification of ulcerative colitis was 99.8％in normal group,98.8％in mild group,95.6％in moderate group and 97.8％in severe group.Conclusion ResNet has good performance in image recognition and classification of ulcerative colitis,can improve the clinical accuracy of ulcerative colitis,and can assist doctors to identify and classify the disease,which has a more reliable clinical application value.

Objective To train an automatic recognition and classification model of ulcerative colitis colonoscopy image based on ResNet,and to test its accuracy,in order to help doctors improve the clinical detection rate and classification accuracy of ulcerative colitis.Methods A total of 4000 colonoscopy images were retrospectively collected from the Colonoscopy Center of Hebei Hospital of Traditional Chinese Medicine from January 2018 to October 2023,and were divided into normal group,mild group,moderate group and severe group according to Mayo endoscopic scoring criteria,with 1000 images for each group.After pre-processing such as brightness adjustment and Angle rotation,the number of images was expanded to 20,000,and the data set was randomly divided into training set,verification set and test set according to the ratio of 7∶2∶1.The training set and verification set are input into the ResNet model to learn and test its stability.After all training is completed,the accuracy of the model is recorded through the test set,and the accurate regression curve is made to evaluate the classification effect of the model.Results In the test set,the accuracy of classification of ulcerative colitis was 99.8％in normal group,98.8％in mild group,95.6％in moderate group and 97.8％in severe group.Conclusion ResNet has good performance in image recognition and classification of ulcerative colitis,can improve the clinical accuracy of ulcerative colitis,and can assist doctors to identify and classify the disease,which has a more reliable clinical application value.