Method for designing a dental component

1. A method for designing a dental component, comprising the steps of:

measuring a dental situation;

generating a 3D model of the dental situation;

applying an artificial neural network for machine learning (convolutional neural network; CNN) to the 3D model of the dental situation and/or an initial 3D model of the dental component, and

automatically generating a fully designed 3D model of the dental component;

wherein the neural network is trained on the basis of a training dataset;

wherein new data is added to the training data set to further train the neural network on the basis of the expanded training data set.

2. The method according to claim 1 , wherein the neural network is trained on the basis of a training data set, wherein the training data set includes initial 3D models of initial dental components and manual changes of these initial 3D models during the design of the 3D model of at least one user.

3. The method according to claim 1 , wherein the neural network is trained on the basis of a training data set, wherein the training data set includes a plurality of 3D models of a plurality of dental situations and the corresponding 3D models of fully designed initial dental components of at least one user.

4. The method according to claim 2 , wherein the training data set only contains the data of one user or of a group of experienced users.

5. The method according to claim 2 , wherein the neural network remains unchanged after training on the basis of the training data set.

6. The method according to claim 1 , wherein the dental component is the restoration, wherein the restoration is an inlay, a crown, a crown framework, a bridge, a bridge framework, an abutment, a pontic or a veneer.

7. The method according to claim 6 , wherein the 3D model of the dental situation has at least one tooth for inserting the dental component, at least one preparation, one residual tooth, at least one neighboring tooth, an abutment for inserting the dental component to be produced, color information of the dental situation and/or a color gradient of the dental situation.

8. The method according to claim 7 , wherein the neural network uses color information and a color gradient of the 3D model of the dental situation, specifically the residual tooth and/or at least one neighboring tooth, to automatically specify a color and/or a color gradient for the restoration to be inserted.

9. The method according to claim 6 , wherein the neural network automatically specifies a material, a production method, an insertion method, a tapping position and a contact tightness with respect to the neighboring teeth for the dental component to be produced.

10. The method according to claim 6 , wherein the training data set additionally includes a color, a color gradient, a material, a production method, an insertion method, a tapping position and/or a contact tightness with respect to the neighboring teeth of the fully designed dental component.

11. The method according to claim 1 , wherein the dental component is a bite guard or an impression tray, wherein the 3D model of the dental situation comprises the teeth for placing the bite guard or the impression tray.

12. The method according to claim 11 , wherein the neural network automatically specifies a material and/or a production method for the bite guard or the impression tray to be produced.

13. A device comprising a processor configured to:

measure a dental situation;

generate a 3D model of the dental situation;

apply an artificial neural network for machine learning (convolutional neural network; CNN) to the 3D model of the dental situation and/or an initial 3D model of the dental component, and

automatically generate a fully designed 3D model of the dental component;

wherein the neural network is trained on the basis of a training dataset;

wherein new data is added to the training data set to further train the neural network on the basis of the expanded training data set.

14. A non-transitory computer-readable storage medium comprising commands which, when executed by a computer, cause the computer to:

measure a dental situation;

generate a 3D model of the dental situation;

apply an artificial neural network for machine learning (convolutional neural network; CNN) to the 3D model of the dental situation and/or an initial 3D model of the dental component, and

automatically generate a fully designed 3D model of the dental component;

wherein the neural network is trained on the basis of a training dataset;

wherein new data is added to the training data set to further train the neural network on the basis of the expanded training data set.