Gingival margin curve design method for personalized implant tooth

Abstract

In order to overcome the defects of an existing gingival margin curve extraction mode, the present invention discloses a gingival margin curve design method for a personalized implant tooth. The gingival margin curve design method for the personalized implant tooth comprises the following steps of: a step 1 of respectively reconstructing gingival margin profiles of a residual tooth of a patient suffering from tooth missing and a three-dimensional dental model of a sample, extracting feature regions of gingival margin curves according to an obtained maximum principal curvature value, extracting gingival margin feature lines of the residual tooth of the patient suffering from tooth missing and the sample by utilizing a granular computing and cellular automaton combining method, then respectively fitting the gingival margin feature curves of the residual tooth of the patient suffering from tooth missing and the dental model of the sample, and finally, constructing a single gingival margin curve of the residual tooth of the patient suffering from tooth missing and the sample; a step 2 of constructing a gingival margin biological multivariate statistical analysis model of the sample; and a step 3 of designing a personalized gingival margin curve of a missed tooth of the patient. The method disclosed by the present invention aims to improve accuracy and efficiency of designing the implant tooth gingival margin curve, improve the repaired gingival margin profile form of the implant tooth, and improve a repair success rate of the personalized implant tooth.

Description

technical field [0001] The invention relates to a method for designing a gingival margin curve of a personalized dental implant, which belongs to the computer-aided design of dental implants and relates to the technical field of biomedical engineering. Background technique [0002] The contour shape of the gingival margin has an important impact on the implantation of the implant abutment and the restoration of the denture. The abutments implanted with different contour shapes of the gingival margin are subjected to different forces, and at the same time affect the health and service life of the implant restoration. Traditional denture restoration methods usually use hand-carved wax or plaster models to simulate the contour of the gingival margin based on expert experience. Due to the complex anatomical structure of the human oral cavity and the large differences in the contours of the gingival margins of different human bodies, there are problems such as poor accuracy and l...

AI Technical Summary

Problems solved by technology

In the paper "Tooth Segmentation of Dental Study Models Using Range Images" published in the academic journal "IEEE Transactions on Medical Imaging" (2004, 23(3): 350-362), Kondo et al. proposed an automatic extraction method for gingival margins based on depth images, which improved the efficiency and accuracy of gingival margin contour extraction. This method uses the detected tooth feature points in the plane depth image of the triangular mesh model to fit the dental arch line, and expands the dental arch line to calculate the panoramic depth image, and then detects the interdental space and gingival margin in the plan view and the panoramic view respectively. Finally, the segmentation boundary of each tooth is determined according to the results of the two images. Since the depth image cannot accurately reflect the three-dimensional information of the teeth, this method will have a large segmentation deviation for severe deformities and interdental interference models.

Yuan Tianran and others published the paper "Tooth Shape Modeling Method for Three-dimensional Dental Model" in the academic journal "Journal of Computer-Aided Design and Graphics" (2010, 22(4): 703-710), using morphology and hole restoration The technology extracts the characteristic line of the gingival margin and restores the first-order continuous anatomical shape of each tooth, but this method requires a lot of manual intervention in the implementation process, the degree of automation is not high, and more manual experience is required, which affects the design. Efficiency and Modeling Accuracy

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