OBJECTIVE:To provide reference for the clinical pharmacists to apply syndrome differentiation of eight principles theory for TCM consultation services. METHODS:Several examples were given to illustrate the practice of the theory guiding patients'medication consultation in respect of Chinese herbal medicine differential medication,Chinese patent medicine differential medication,dietary taboo,etc. According to the author's many years of work experience,from the three aspects of the counselor, the consultant,and the consulting conditions,the reasons that TCM consultation window couldn't provide the medical staff and patients with rational TCM use suggestions and appropriate diet were analyzed. RESULTS & CONCLUSIONS:The syndrome differentiation of eight principles theory is based on four diagnostic methods to collect and analyze symptoms,signs and other information comprehensively. The information were divided into eight types of syndromes as yin and yang,exterior and interior, cold and heat,deficiency and excess,so as to determine the theory of basic syndromes of disease. The suggestions on rational drug use and appropriate diet were provided for the patients by using syndrome differentiation of eight principles theory. In view of the lack of knowledge and ability of the clinical pharmacists on syndrome differentiation of eight principles theory,the unfamiliar service scope of the clinical pharmacists in TCM consultation service,and the lack of consultation conditions for the use of TCM, it is suggested that the clinical pharmacists should learn more about the syndrome differentiation of eight principles theory;TV, WeChat and other media guide public dialect use of Chinese herbal medicines,Chinese patent medicines and appropriate diet;hardware conditions for TCM consultation by establishing TCM consultation rooms and TCM consultation clinics are establish so as to improve the level of clinical pharmacists'TCM consultation services.

Background: Computer-aided diagnosis based on deep learning technology is a research hotspot in the field of gastroenterology, and computer-aided diagnosis of colorectal polyps has received more and more attention. Aims: To validate a model based on deep learning for the automatic identification of colorectal polyps, and to analyze its auxiliary learning function for helping novice endoscopists. Methods: A total of 1 200 colonoscopy images (600 colorectal polyp images and 600 normal images) in the endoscopy center database of Qingdao Municipal Hospital (East) from January 2019 to January 2020 were retrospectively collected. Deep learning model was used to identify the 1 200 images. The sensitivity, specificity, accuracy and diagnosis time of deep learning model and 5 novice endoscopists for diagnosis of colorectal polyps were compared. Results: The deep learning model showed a sensitivity of 93.2%, specificity of 98.7%, accuracy of 95.9% for detecting colorectal polyps, and the diagnosis time of each image was (0.20±0.03) second. The sensitivity, accuracy, and diagnosis time of the model were superior to 5 novice endoscopists, and the specificity was superior to some novice endoscopists. The accuracies of model for polyps with size ≤5 mm and 6~9 mm were 88.1% and 96.8%, respectively, and were superior to 5 novice endoscopists; the accuracy of model for polyps with size ≥10 mm was 100%, and was similar to 5 novice endoscopists. The accuracy of model for polyps with protrude type was 94.8%, and was superior to some novice endoscopists; the accuracy of model for polyps with flat type was 91.7%, and was superior to 5 novice endoscopists. Missing the polyps with flat type (38.8%), polyps at mucosal folds (32.7%), and mistaking the mucosal folds as polyps (12.2%) were the main causes of false negative or false positive results of the model. Conclusions: The deep learning model has a high accuracy, sensitivity, specificity and shorter diagnosis time for diagnosis of colorectal polyps, and can be used to assist novice endoscopists in diagnosing small polyps and flat polyps.