Résumé
@#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.
Résumé
@#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.
Résumé
Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar's structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels' variance and mean values, which improves the algorithm's robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms' results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.