Objective:In order to achieve a dynamic supervision system throughout the logistics process of high-value consumables of oral and maxillofacial surgery and solve the problem of inaccurate traceability. Methods: It is based on the theory of supply chain management and summarizes the logistics process on high-value consumables of oral and maxillofacial surgery. It is a combination of bar code and information management system. Results: The dynamic supervision system throughout the logistics process of high-value consumables of oral and maxillofacial surgery will replace the original static and extensive mode management. It will achieve the refine management of high-value consumables of oral and maxillofacial surgery. Conclusion:It promotes the integration of material flow, information flow, capital flow. It achieves the dynamic supervision system in every procedure including purchasing, warehousing, consuming, destroying, Checking inventory and ensures the accurate traceability.

Objective To analyze the distribution of types and characteristics of periodontal diseases in 34 677 patients visiting the Department of Periodontology, Peking University School and Hospital of Stomatology.Methods Clinical data of 34 677 patients who had the electronic periodontal examination charts from 2007 to 2012 were collected and analyzed.Results Out of 34 677 patients, 32 517 (93.77％) were diagnosed as chronic periodontitis(CP), 1 642(4.74％) were aggressive periodontitis(AgP) and the rest 518(1.49％) patients were classified into some other types of periodontitis.There were more female patients than male ones.Most of patients were between 25 to 54 years olds.Only 7 306(21.07％) patients had more than two periodontal examination charts which represented regular re-visits to the doctors.The majority of patients had severe periodontitis.Conclusions Most of the patients visiting the Department of Periodontology were older aged and diagnosed chronic periodontitis.They had more severe periodontitis conditions but less re-visits.Therefore it is very important for dentists to enhance the oral health education and make early diagnosis and treatment of periodontal diseases for patients.Dentists also should do more follow-up and maintenance works for patients after the initial treatments.

Objective:Through the construction of public service platform for clinical research of oral diseases, to facilitate more efficient and orderly development of clinical research and related management.Methods:The construction of clinical platform for oral diseases includes data sharing platform, scale system of various disciplines, specialized terminology system, electronic medical record access, clinical research project management system, etc.Results:The established platform can assist physicians in standardizing clinical research in their daily diagnosis and treatment work through a unified terminology scale system; simplify the data integration process through seamless connection of medical data and research data; and realize the whole process management of research through the application of clinical research project management system.Conclusions:The establishment and operation of the platform has significantly increased the inclusion of clinical research samples, promoted the efficient and orderly development of clinical research related work, and significantly improved the management of clinical research.

Objective:To identify tooth number abnormalities on pediatric panoramic radiographs based on deep learning.Methods:Eight hundred panoramic radiographs of children aged 4 to 11 years meeting the inclusion and exclusion criteria were selected and randomly assigned by writing programs in Python (version 3.9) to the training set (480 images), verification set (160 images) and internal test set (160 images), taken in Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology between November 2012 to August 2020. And all panoramic radiographs of children aged 4 to 11 years taken in the First Outpatient Department of Peking University School and Hospital of Stomatology from June 2022 to December 2022 were collected as the external test set (907 images). All of the 1 707 images were obtained by operators to determine the outline and to label the tooth position of each deciduous tooth, permanent tooth, permanent tooth germ and additional tooth. The deep learning model with ResNet-50 as the backbone network was trained on the training set, validated on the verification set, tested on the internal test set and external test set. The images of test sets were divided into two categories according to whether there was abnormality of tooth number, to calculate sensitivity, specificity, positive predictive value and negative predictive value, and then divided into four types of extra teeth and missing permanent teeth both existed, extra teeth existed only, missing permanent teeth existed only, and normal teeth number, to calculate Kappa values. Results:The sensitivity, specificity, positive predictive value and negative predictive value were 98.0%, 98.3%, 99.0% and 96.7% in the internal test set, and 97.1%, 98.4%, 91.9% and 99.5% in the external test set respectively, according to whether there was abnormality of tooth number. While images were divided into four types, the Kappa value obtained in the internal test set was 0.886, and that in the external test set was 0.912. Conclusions:In this study, a deep learning-based model for identifying abnormal tooth number of children was developed, which could identify the position of additional teeth and output the position of missing permanent teeth on the basis of identifying normal deciduous and permanent teeth and permanent tooth germs on panoramic radiographs, so as to assist in diagnosing tooth number abnormalities.