A computer-implemented method for marking an object on an aligner. The aligner surface is modeled. The method includes calculating a normal for each tile in a tessellated surface and disqualifying a tile from being selected. For tiles not disqualified, a patch is identified that produces a markable area. The method includes selecting an object to be marked, calculating a location of the object in the markable area, and providing the location of the object to a marking device. Disqualifying includes comparing an angle between a normal and an orientation of the beam to an origin of the calculated normal on each tile. Disqualifying includes disqualifying the at least one tile when the angle is outside of a range of −90° to +90°. Identifying the patch includes separating the patch into at least two smaller patches, and one of the two smaller patches of tiles is the markable area.

The present disclosure relates to a method and device for matching oral model data that can provide matching points to a user to match oral model data and computerized tomography (CT) data even for edentulous patients. In this way, convenience is increased and matched data can be effectively generated even for edentulous patients for whom matching has been difficult conventionally.

The present disclosure relates to a method and device for matching oral model data that can provide matching points to a user to match oral model data and computerized tomography (CT) data even for edentulous patients. In this way, convenience is increased and matched data can be effectively generated even for edentulous patients for whom matching has been difficult conventionally.

The present disclosure provides a dental orthodontic appliance and a design method and a manufacturing method thereof. The dental orthodontic appliance includes a shell-like body, and the shell-like body is provided with a plurality of cavities for accommodating maxillary teeth, and the shell-like body is further provided with an upper palatal arch that reshapes a form of a dental arch. Two ends of the upper palatal arch are respectively and partially connected to positions of gingival margins or positions adjacent to the gingival margins on ligual sides in posterior regions of left and right sides of the shell-like body. When the shell-like body interacts with teeth, the upper palatal arch can induce buccolingual lateral amplification of maxillary palatal suture bone deposition by means of deformation, and teeth in the posterior regions move laterally buccolingually under an action of the shell-like body.

The present disclosure provides a dental orthodontic appliance and a design method and a manufacturing method thereof. The dental orthodontic appliance includes a shell-like body, and the shell-like body is provided with a plurality of cavities for accommodating maxillary teeth, and the shell-like body is further provided with an upper palatal arch that reshapes a form of a dental arch. Two ends of the upper palatal arch are respectively and partially connected to positions of gingival margins or positions adjacent to the gingival margins on ligual sides in posterior regions of left and right sides of the shell-like body. When the shell-like body interacts with teeth, the upper palatal arch can induce buccolingual lateral amplification of maxillary palatal suture bone deposition by means of deformation, and teeth in the posterior regions move laterally buccolingually under an action of the shell-like body.

Techniques are described for automating the design of dental restoration appliances using neural networks. An example computing device receives transform information associated with a current dental anatomy of a dental restoration patient, provides the transform information associated with the current dental anatomy of the dental restoration patient as input to a neural network trained with transform information indicating placement of a dental appliance component with respect to one or more teeth of corresponding dental anatomies, the dental appliance being used for dental restoration treatment for the one or more teeth, and executes the neural network using the input to produce placement information for the dental appliance component with respect to the current dental anatomy of the dental restoration patient.

Techniques are described for automating the design of dental restoration appliances using neural networks. An example computing device receives transform information associated with a current dental anatomy of a dental restoration patient, provides the transform information associated with the current dental anatomy of the dental restoration patient as input to a neural network trained with transform information indicating placement of a dental appliance component with respect to one or more teeth of corresponding dental anatomies, the dental appliance being used for dental restoration treatment for the one or more teeth, and executes the neural network using the input to produce placement information for the dental appliance component with respect to the current dental anatomy of the dental restoration patient.

The present disclosure provides method for generating a three-dimensional (3D) model of a dental structure of a subject. The method comprises: capturing image data about the dental structure of the subject using a camera of a mobile device; constructing a first 3D model of the dental structure from the image data; registering the first 3D model with an initial 3D surface model to determine a transformation for at least one element of the dental structure; and updating the initial 3D surface model by (i) applying the transformation to update a position of the at least one element and/or (ii) deforming a surface of a local area of the at least one element using a deformation algorithm.

The present disclosure provides method for generating a three-dimensional (3D) model of a dental structure of a subject. The method comprises: capturing image data about the dental structure of the subject using a camera of a mobile device; constructing a first 3D model of the dental structure from the image data; registering the first 3D model with an initial 3D surface model to determine a transformation for at least one element of the dental structure; and updating the initial 3D surface model by (i) applying the transformation to update a position of the at least one element and/or (ii) deforming a surface of a local area of the at least one element using a deformation algorithm.

Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.