Objective To observe the clinical efficacy of the combined therapy of super-laser radiation and plum acupuncture on alopecia areata.Methods Altogether 178 alopecia areata patients were randomly divided into 3 groups:65 patients in the treatment group being given super-laser radiation and plum acupuncture,55 in the control group A solely with super-laser radiation and 58 in the control group B solely with plum acupuncture.The therapeutic effects of the treatment group and the control groups were compared by rank sum test.Results At the end of the 1st treating period,the cured cases,markedly effective cases,ordinary effective cases and invalid cases in control group A were 1,4,10 and 40;those numbers of control group B were 1,3,11 and 43;those numbers of the treatment group were 3,12,14 and 36.At the end of the 2nd treating period,those numbers of control group A were 8,8,16 and 23;those numbers of control group B were 7,8,18 and 25;those numbers of the treatment group were 11,20,19 and 15.At the end of the 3rd treating period,those numbers of A control group were 21,11,12 and 11;those numbers of B control group were 20,14,11 and 13;those numbers of the treatment group were 33,17,10 and 5.Comparing the performance of each groups at the end of the 3 periods,we found that effects were always better in the treatment group than the other two control groups.The difference between the treatment group and the other two control groups was statistically significant (P＜0.05).Conclusions It is effective and safe to use super laser radiation combined plum acupuncture on alopecia areata.The clinical efficacy of this joint method is better than either of the two separate ways.

OBJECTIVEThis project aims to establish an interactive oral and maxillofacial radiological image annotation database and to analyze its feasibility for implementation into curricula in order to develop a highly effective software for image browsing.

METHODSWe established the interactive image annotation database primarily on the basis of the local network and Foxit Reader. A pilot survey was then conducted to determine the performance of the interactive database. Seventy-six students were asked to complete a structured and open questionnaire related to their perceptions of using the database. Simple numeric quantitative and qualitative analyses were then applied.

RESULTSA total of 542 portable document format (PDF) digital teaching images and corresponding annotated files were collected. The survey revealed that most of the students found the database useful. Approximately 87.50% of the 64 subjects who compelete questionnaire believed that the database was superior to an older system (joint photographic experts group, JPEG) of image browsing.

CONCLUSIONThe integration and sharing of teaching resources and the establishment of an internet-based learning platform is the key to realizing a digital medical teaching system. The established database has high potential in a wide range of practical applications.

In recent years, artificial intelligence technology has developed rapidly and has been gradually applied to the fields of clinical image data processing, auxiliary diagnosis and prognosis evaluation. Research has shown that it can simplify doctors’ clinical tasks, quickly provide analysis and processing results, and has high accuracy. In terms of orthodontic diagnosis and treatment, artificial intelligence can assist in the rapid fixation of two-dimensional and three-dimensional cephalometric measurements. In addition, it is also widely used in the efficient processing and analysis of three-dimensional dental molds data, and shows considerable advantages in determining deciding whether orthodontic treatment needs tooth extraction, thus assisting in judging the stage of growth and development, orthodontic prognosis and aesthetic evaluation. Although the application of artificial intelligence technology is limited by the quantity and quality of training data, combining it with orthodontic clinical diagnosis and treatment can provide faster and more effective analysis and diagnosis and support more accurate diagnosis and treatment decisions. This paper reviews the current application of artificial intelligence technology in orthodontic diagnosis and treatment in the hope that orthodontists can rationally treat and use artificial intelligence technology in the clinic, and make artificial intelligence better serve orthodontic clinical diagnosis and treatment, so as to promote the further development of intelligent orthodontic diagnosis and treatment processes.

Root position plays an important role in healthy, stable, and aesthetic orthodontic treatment. In the past, two-dimensional radiographic images were used to assess the accuracy and precision of tooth root positions. In recent years, the use of cone beam CT (CBCT) and its reconstructed images to measure the three-dimensional spatial position and angle of root position has become mainstream. The root position is mainly described by measuring the relationship between the root and adjacent structures in the buccolingual, vertical, and mesiodistal directions as well as the root angle. The thickness of the alveolar bone on the buccolingual side of the root represents the buccolingual position, the vertical height in the alveolar bone and the relationship between apex and maxillary sinus represents the vertical position, the interroot alveolar bone thickness represents the mesiodistal position of the root, and the root angle is mostly based on incisal mandibular plane angle, angulation, torque, and other angles in the traditional two-dimensional measurement. Fitting CBCT and digital model data can be used to monitor the relationship between root and alveolar bone during orthodontic treatment, but a more comprehensive, standardized three-dimensional tooth root position measurement method is required to make full use of the root data provided by CBCT to study the relative optimal position of the tooth root at different anatomical levels, which combines with computer technology to optimize the digital design of orthodontic diagnosis and treatment.

Three-dimensional tooth segmentation is the segmentation of single-tooth models from a digital dental model. It is an important foundation for diagnosis, planning, treatment and customized appliance manufacturing in digital orthodontics. With the deep integration of artificial intelligence technology and big data from stomatology, the use of deep learning algorithms to assist 3D tooth segmentation has gradually become mainstream. This review summarizes the current situation of deep learning algorithms that assist 3D tooth segmentation from the aspects of dataset establishment, algorithm architecture, algorithm performance, innovation and advantages, deficiencies of current research and prospects. The results of the literature review showed that deep learning tooth segmentation methods could obtain an accuracy of more than 95% and had good robustness. However, the segmentation of complex dental models, operation time and richness of the training database still need to be improved. Research and development of the "consumption reduction and strong core" algorithm, establishment of an authoritative data sample base with multiple centers, and expansion of data application depth and breadth will lead to further development in this field.