Abstract: Systems and methods including: receiving, from a digital imaging device, one or more 2D images of a patient's face including a mouth region, where the one or more 2D images include a depiction of the patient's teeth in an initial arrangement; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient's teeth based on the one or more received 2D images; generating a realistic composite 2D image depicting the patient's face with the 2D rendering depicting the post-treatment arrangement of the patient's teeth, where the generating includes positioning the 2D rendering within a mouth opening of the patient's face in the realistic composite 2D image; and sending the realistic composite 2D image to an output device for display on a user interface of the output device.

Abstract: Methods and systems including: receiving one or more 2D images of a patient's face including a depiction of the patient's teeth in an initial arrangement; extracting teeth contours of the patient's teeth from within the mouth region of the one or more received 2D images of the patient's face; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient's teeth based on the one or more received 2D images; generating a realistic composite 2D image depicting the patient's face with the 2D rendering depicting the post-treatment arrangement of the patient's teeth; and sending the realistic composite 2D image for display on a user interface of an output device.

Abstract: A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

Abstract: Systems and methods for generating a virtual depiction of an orthodontic treatment of a patient are provided. In some embodiments, a system includes one or more processors, and a memory operably coupled to the one or more processors. The memory can include instructions that, when executed by the one or more processors, cause the system to perform operations including: receiving one or more 2D images of a patient’s face including a mouth region; extracting teeth contours of the patient’s teeth from within the mouth region of the received 2D images; generating, using a neural network, a 2D rendering depicting a post-treatment arrangement of the patient’s teeth; generating a realistic composite 2D image depicting the patient’s face with the 2D rendering depicting the post-treatment arrangement of the patient’s teeth; and sending the realistic composite 2D image to an output device for display on a user interface of the output device.

Abstract: A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.

Abstract: A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

Abstract: Systems and methods for planning a treatment for a patient's teeth are provided. In some embodiments, a method includes receiving a treatment plan including a target tooth arrangement including a change in mass of at least one tooth. The method can also include outputting a graphical user interface including a visualization of the treatment plan. The visualization can include a plurality of digital models, each digital model representing an intermediate tooth arrangement configured to adjust the patient's teeth toward the target tooth arrangement. The visualization can also include a heatmap overlaid onto at least one digital model of the plurality of digital models. The heatmap can show a difference in tooth mass between the target tooth arrangement and the corresponding at least one intermediate tooth arrangement of the at least one digital model.

Abstract: Systems and methods for planning a treatment for a patient's teeth are provided. In some embodiments, a method includes receiving an image of a patient's face and determining a plurality of facial lines using the image of the patient's face. The plurality of facial lines can represent a target smile for the patient. The method can further include outputting a visualization including the plurality of facial lines overlaid onto a composite image. The composite image can include the image of the patient's face combined with a digital model of the patient's teeth. The digital model can represent a tooth arrangement of a treatment plan to adjust the patient's teeth from an initial arrangement toward a target tooth arrangement corresponding to the target smile.

Abstract: Systems and methods for planning a treatment for a patient’s teeth are provided. In some embodiments, a method includes receiving input data representing an initial tooth arrangement of a patient’s teeth. The method can include determining a target tooth arrangement for the patient’s teeth, the target tooth arrangement including a change in mass of at least one tooth. The method can also include generating a plurality of intermediate tooth arrangements configured to adjust the patient’s teeth from the initial tooth arrangement toward the target tooth arrangement. The method can further include generating instructions to output a visualization showing a difference in tooth mass between at least one intermediate tooth arrangement of the plurality of intermediate tooth arrangements and the target tooth arrangement.

Abstract: Provided herein are systems and methods for automatically segmenting a 3D model of a patient's teeth. A patient's dentition may be scanned with a 3D scanning system, such as CT, CBCT, or MRI. The 3D scan data may be automatically segmented with one or more neural networks. The segmented 3D scan can be incorporated into a dental treatment plan.

Abstract: The present disclosure provide for methods of planning restorative procedures. Methods may involve identifying an initial position of a patient's teeth based on a digital representation of the patient's teeth, and determining one or more candidate orthodontic positions for the patient's teeth, wherein each of the one or more candidate orthodontic positions is a target position of the patient's teeth being considered. Methods may further involve virtually modeling a first restorative object at a first evaluated restorative object position on one of the patient's teeth, evaluating a first amount of tooth mass removal corresponding to the first restorative object based on the first evaluated restorative object position and one of the candidate orthodontic positions, and determining a final orthodontic position and a final restorative object position based on the first amount of tooth mass removal.

Abstract: A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

Abstract: A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

Abstract: A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.

Abstract: A computer-implemented method for generating a virtual depiction of an orthodontic treatment of a patient is disclosed herein. The computer-implemented method may involve gathering a three-dimensional (3D) model modeling the patient's dentition at a specific treatment stage of an orthodontic treatment plan. An image of the patient's face and dentition may be gathered. A first set of reference points modeled on the 3D model of the patient's dentition and a second set of reference points represented on the dentition of the image of the patient may be received. The image of the patient's dentition may be projected into a 3D space to create a projected 3D model of the image of the patient's dentition. Based on a comparison of the first reference points and projections of the second set of reference points, a modified image of the patient may be constructed and/or modeled.

Abstract: A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.

Abstract: A computer-implemented method for virtually representing an orthodontic treatment outcome using automated detection of facial and dental reference objects is disclosed herein. The computer-implemented method may involve modeling initial positions of an orthodontic patient's teeth in a three-dimensional (3D) virtual model and determining the effect of application of various force systems applied through an orthodontic treatment plan on the patient's teeth. Images of the patient's face may be used to identify facial reference objects. Dental reference objects of the patient's dentition may be identified from the 3D virtual model. A relationship between the facial reference objects and the dental reference objects may form the basis of modifications to the orthodontic treatment plan and/or modeling final orthodontic positions of the orthodontic treatment plan.

Abstract: A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.

Abstract: A computer-implemented method for virtually representing an orthodontic treatment outcome using automated detection of facial and dental reference objects is disclosed herein. The computer-implemented method may involve modeling initial positions of an orthodontic patient's teeth in a three-dimensional (3D) virtual model and determining the effect of application of various force systems applied through an orthodontic treatment plan on the patient's teeth. Images of the patient's face may be used to identify facial reference objects. Dental reference objects of the patient's dentition may be identified from the 3D virtual model. A relationship between the facial reference objects and the dental reference objects may form the basis of modifications to the orthodontic treatment plan and/or modeling final orthodontic positions of the orthodontic treatment plan.

Abstract: A method may include: receiving an image of a mouth region of a patient's face; extracting teeth contours within the image of the mouth region of the patient's face; locating a mouth opening within the image of the mouth region of the patient's face; extracting the tooth contours from a 3D model of the patient's teeth; and aligning the tooth contours from the 3D model with the tooth contours of the teeth within the image of the mouth region of the patient's face.