Information processing system for orthodontic appliances

Abstract

This concerns a system that automatically identifies the parts included in orthodontic appliances. [Solution] The information processing system of the present invention comprises: an image acquisition means for acquiring data relating to orthodontic appliances; an annotation means for assigning information to the features of parts included in the data acquired by the image acquisition means; a learning means for constructing a model for identifying parts by learning the features of parts based on the annotation data, using the data and information from the annotation means as annotation data; and an inference means for identifying parts included in new data relating to orthodontic appliances using the model constructed by the learning means, and outputting the part identification results in association with information.

Claims

1. An information processing system for identifying a part included in an orthodontic appliance composed of at least one part, An image acquisition means for acquiring data related to the aforementioned orthodontic appliance, An annotation means for assigning information to the features of the parts included in the data acquired by the image acquisition means, A learning means that uses the data and information obtained by the annotation means as annotation data, and learns the characteristics of the parts based on the annotation data to construct a model for identifying the parts, An information processing system characterized by comprising: an inference means that uses the model constructed by the learning means to identify the parts included in new data relating to the orthodontic appliance, and outputs the identification result of the parts in association with the information.

2. The information processing system according to claim 1, further comprising a slip output means that outputs a slip relating to the parts included in the orthodontic appliance based on the output from the inference means.

3. The information processing system according to claim 1, characterized in that the annotation means indicates the parts included in the data acquired by the image acquisition means with bounding boxes and assigns the information.

4. The information processing system according to claim 1, characterized in that the annotation means assigns information by representing the parts included in the data acquired by the image acquisition means as polygons.

5. The information processing system according to claim 1, characterized in that the annotation means indicates the parts included in the data acquired by the image acquisition means with free curves and assigns the information.

6. The image acquisition means acquires data from at least two viewpoints, a photograph from a first angle and a photograph from a second angle. The annotation means performs annotation on each of the photographs from the first and second angles. The information processing system according to claim 1, characterized in that the learning means learns the features of the part from a first angle and learns the features of the part from a second angle, and integrates the features obtained from at least two viewpoints to construct a model.

7. The image acquisition means acquires 3D data of the orthodontic appliance using a 3D scanner. The information processing system according to claim 1, characterized in that the annotation means performs annotation on a plurality of 2D images from multiple viewpoints generated from the 3D data acquired by the image acquisition means.

8. The image acquisition means acquires 3D data of the orthodontic appliance using a 3D scanner. The information processing system according to claim 1, characterized in that the annotation means performs annotation on the 3D data acquired by the image acquisition means using voxel data, point cloud data, or mesh data.

9. The information processing system according to claim 1, characterized in that the annotation means automatically represents the parts included in the orthodontic appliance using bounding boxes, polygons, or free curves, by utilizing differences in color or texture of the parts.

10. The information processing system according to claim 9, characterized in that the annotation means modifies parameters for determining differences in color or texture of the parts included in the orthodontic appliance when the annotation is modified by an operator.

11. The information processing system according to claim 10, characterized in that the modification of the aforementioned parameters is performed by modification using a feedback loop.

12. The information processing system according to claim 10, characterized in that the modification of the aforementioned parameters is performed by a deep learning model.

13. The annotation means labels the parts of the orthodontic appliance by dividing them into major and minor categories. The information processing system according to claim 1, characterized in that the learning means identifies the parts based on the hierarchy of the major classification and the minor classification.

14. The annotation means assigns the information to small image data, The learning means constructs a pre-trained model based on the dataset using the small image data annotated by the annotation means. The annotation means automatically labels the information for the parts included in the orthodontic appliance using the pre-trained model. The information processing system according to claim 1, characterized in that, when the annotation means is modified by an operator, it causes the modified dataset to be used to reconstruct the pre-trained model.

15. The learning means further constructs an interrelationship model that learns the correlations between the parts of the orthodontic appliance, The information processing system according to claim 1, characterized in that the inference means outputs an inference result that takes into account the interrelationships between the parts by applying the interrelationship model to the inference result of the model.

16. An information processing system for generating a learning model for identifying the parts included in an orthodontic appliance composed of at least one part, An image acquisition means for acquiring data related to the aforementioned orthodontic appliance, An annotation means for assigning information to the features of the parts included in the data acquired by the image acquisition means, An information processing system characterized by comprising: a learning means that uses the data and information obtained by the annotation means as annotation data, and uses the annotation data to learn the characteristics of the parts and construct a model for identifying the parts.

17. An information processing system for creating annotation data for a learning model for identifying parts included in an orthodontic appliance consisting of at least one part, An image acquisition means for acquiring data related to the aforementioned orthodontic appliance, An information processing system characterized by comprising annotation means for assigning information to the characteristics of the parts included in the data acquired by the image acquisition means.

18. An information processing system for identifying a part included in an orthodontic appliance composed of at least one part, An information processing system comprising: an inference means that learns the characteristics of the aforementioned parts and uses the model, which has learned information about the aforementioned parts, to identify the aforementioned parts included in new data relating to the orthodontic appliance, and outputs the identification result of the aforementioned parts in association with the aforementioned information.

19. Furthermore, the information processing system according to claim 18 is characterized by comprising a slip output means that outputs a slip relating to the parts included in the orthodontic appliance based on the output from the inference means.

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