Abstract: A method of determining an optimal switchover moment for a hybrid orthodontic treatment of teeth. Creation of a digital three-dimensional model of at least part of a dental arch. Deformation of the initial reference model until the tooth models are in a target position. Acquisition of at least one two-dimensional image of the teeth. Deformation of the initial reference model until the at least one updated image corresponds to the initial reference model. Determination, from the updated reference model and from said target reference model, of a plurality of remaining-treatments for moving the teeth from their position represented in the updated reference model into their position represented in the target reference model. Determination of a profile including at least one value for an evaluation parameter. Evaluation of each profile by an evaluation rule. Determination the profile of which is optimal for replacing an orthodontic appliance with an aligner.

Abstract: A method includes the following steps of (i) production with a 3D scanner, of a three-dimensional digital reference model of at least part of a dental arch of the patient, or “initial reference model”; (ii) acquisition, in actual acquisition conditions, by the patient or one of his or her close relatives, with a mobile phone, of at least three two-dimensional images of the dental arches of the patient corresponding to a front view, a right-side view and a left side view of the teeth of the patient, respectively, called “updated images”; and (iii) searching, through modification of the initial reference model, for a final reference model corresponding to the positioning and/or the shape of the teeth on the updated images.

Abstract: A method for analyzing an image, called “analysis image”, of a dental arch of a patient. The method in which the analysis image is submitted to a neural network, in order to determine at least one value of an image attribute relating to the analysis image. The analysis image is a photograph or an image taken from a film. The image attribute relates to a position or an orientation or a calibration of an acquisition apparatus used to acquire the analysis image or a combination thereof, or a quality of the analysis image, and in particular relating to the brightness, to the contrast or to the sharpness of the analysis image, or a combination thereof.

Abstract: A method for evaluating, by computer, at an “updated” time, the conformity of an orthodontic aligner with a dental arch of a patient, referred to as the “support arch.” The method includes the following successive steps: A) producing a digital three-dimensional model of the support arch in its configuration at the updated time, or an “updated reference model”, and determining from the updated reference model, a support surface for the aligner; B) deforming the support surface on the basis of the geometry of the aligner in order to obtain a deformed support surface; C) comparing an external surface of the aligner with the deformed support surface, then determining, on the basis of the comparison, at least one score for the conformity of the aligner with the support arch.

Abstract: An acquisition kit, including: a mouthpiece intended for being placed into the mouth of a patient and including a reference mark; and an image-acquisition apparatus including a screen for viewing an image that can be acquired, computer memory containing information on target acquisition conditions, and a computer program comprising program code instructions for displaying on said screen a reference in a position such that, when the reference mark matches the reference on the screen, the acquisition apparatus complies with the target acquisition conditions.

Abstract: A method for transforming an “original” view of an “original” digital three-dimensional model into a hyper-realistic view. The method includes creating a “transformation” learning base of more than 1000 “transformation” records. Each transformation record includes a “transformation” photo representing a scene, and a view of a “transformation” digital three-dimensional model modeling the scene, or “transformation view”, the transformation view representing the scene in the same way as the transformation photo. The method includes training at least one “transformation” neural network, by way of the transformation learning base. The method includes submitting the original view to the at least one transformation neural network, such that it transforms it into a hyper-realistic view.

Abstract: A method for analyzing an image, called analysis image, of a dental arch of a patient. The analysis image is submitted to a deep learning device in order to determine at least one value of a tooth attribute relating to a tooth represented on the analysis image, and/or at least one value of an image attribute relating to the analysis image.

Abstract: A method for monitoring the shape of teeth including: producing a three-dimensional digital model of at least one portion of an arch of the patient or “initial reference model”; acquiring at least one two-dimensional image of the arches, referred to as “updated image”, under actual acquisition conditions; analysing each updated image and producing an updated map relating to a piece of discriminating information; optionally determining rough virtual acquisition conditions that approximate the actual acquisition conditions; searching each updated image for a final reference model corresponding to the shape of the teeth, and optionally to the positioning of the teeth, during the acquisition of the updated image; and comparing the shapes of the initial reference model and of the reference model obtained at the end of the preceding steps, referred to as “final reference model”, in order to determine the deformation and/or the movement of teeth between steps a) and b).

Abstract: A method for enriching a historical learning base. Training of a first neural network to transform first photos of first dental scenes into first hyper-realistic images to simulate the effect of a dental event on the first dental scenes. Submission, to the trained first neural network, of a source photo representing a source dental scene in a dental context, so as to obtain a final image representing, hyper-realistically, the source dental scene after simulation of the dental event. Creation of a descriptor of the final image, or “final descriptor.” Creation of a historical record consisting of the final image and the final descriptor, and addition of the historical record in the historical learning base.

Abstract: A method for evaluating an orthodontic appliance. Acquisition of a two-dimensional “appliance image” of teeth at least partially representing the orthodontic appliance in a service position. Acquisition of a two-dimensional “dentition image” of the teeth. If a determination of comparable dental and appliance attributes is not possible from the appliance image and from the dentition image, respectively, conversion of the appliance image and/or of the dentition image so that the appliance image be compatible with the dentition image. Determination of dental and appliance attributes relative to the shape of the tooth represented on the dentition image and relative to the shape of the orthodontic appliance on the appliance image. Comparison of the dental and appliance attributes, followed by determination of at least one score. At least one image is acquired with a mobile phone.

Abstract: A method for modeling a dental arch of a patient. Creation of a historical library including more than 1000 historical tooth models, and assignment, to each historical tooth model, of a value for at least one tooth attribute value. Analysis of at least one analysis image of the dental arch by means of a deep learning device so as to determine at least one analysis tooth zone and at least one tooth attribute value. For each analysis tooth zone determined in the preceding step, search, in the historical library, for a historical tooth model exhibiting a maximal proximity with the analysis image. Arrangement of all of the optimal tooth models so as to create an assembled model which exhibits a maximal proximity Some optionally steps are possible.

Abstract: A method for assessing the shape of an orthodontic aligner, said method comprising the following steps: a?) acquisition of at least one image at least partially representing the aligner in a service position in which it is worn by a patient, called “analysis image”; b?) analysis of the analysis image by means of a deep learning device, preferably a neural network, trained by means of a learning base, so as to determine a value for at least one tooth attribute of an “analysis tooth zone” representing, at least partially, a tooth on said analysis image, the tooth attribute relating to a separation between the tooth represented by the analysis tooth zone, and the aligner represented on the analysis image, and/or for an image attribute of the analysis image, the image attribute relating to a separation between at least one tooth represented on the analysis image, and the aligner represented on said analysis image.

Abstract: A method for evaluating the shape of an orthodontic aligner worn by a patient. Acquisition of at least one two-dimensional image of teeth of the patient as “updated image.” At least one updated image as “aligner image,” at least partially representing the aligner in a service position. At least one updated image as “dentition image,” representing the teeth. If the dentition image is different from the aligner image, conversion of the dentition image so that it represents the teeth as seen under the acquisition conditions used during the acquisition of the aligner image. Determination, for each of a plurality of teeth represented on the dentition and aligner images, of interior and exterior tooth outlines representing the outline of the free end of the tooth on the dentition and aligner images. Comparison of the interior and exterior tooth outlines, followed by determination of at least one score according to the comparison.

Abstract: A simulating method. The method includes acquiring an image of a dental arch of a patient, called the “original image.” The method includes subjecting the original image to a neural network, called the “modification neural network”, trained to locally modify the original image in order to obtain a “modified image.” The method includes presenting the modified image to the patient.

Abstract: Method for cutting a three-dimensional model of a dental scene, or “scene model.” The method includes acquiring a view of the scene model, called the “analysis view.” The method includes analyzing the analysis view by a neural network in order to identify, in the analysis view, at least one elementary zone representing an element of the dental scene, and assigning a value to at least one attribute of the elementary zone. The method includes identifying a region of the scene model represented by the elementary zone on the analysis view, and assigning, in the region, a value to an attribute of the scene model in accordance with the value of the attribute of the elementary zone.

Abstract: A method for modeling a dental arch of a patient. Creation of a historical library including more than 1000 historical tooth models, and assignment, to each historical tooth model, of a value for at least one tooth attribute value. Analysis of at least one analysis image of the dental arch by means of a deep learning device so as to determine at least one analysis tooth zone and at least one tooth attribute value. For each analysis tooth zone determined in the preceding step, search, in the historical library, for a historical tooth model exhibiting a maximal proximity with the analysis image. Arrangement of all of the optimal tooth models so as to create an assembled model which exhibits a maximal proximity. Some optionally steps are possible.

Abstract: Method for preparing a composed response from a value for at least one dental attribute relating to the dental situation of a patient. The method includes: (d) selection by a composition computer of at least one relevant standard response from a base or standard responses and as a function of the value of the at least one dental attribute; (e) composition of a composed response by the composition computer on the basis of the at least one relevant standard response; (f) optionally, validation and/or modification of the composed response by a controller; (g) presentation of the optionally validated and/or modified composed response. The selection in (d) and the composition in (e) are determined according to composition rules of a composition protocol. Before (e), the composition protocol is selected in a protocol models base and/or is created by selecting a protocol model in a protocol models base and then modifying.

Abstract: A method for assessing the shape of an orthodontic aligner. Acquisition of at least one analysis image at least partially representing the aligner in a service position in which it is worn by a patient. Analysis of the analysis image by means of a deep learning device, trained by means of a learning base, so as to determine a value. For at least one tooth attribute of an analysis tooth zone representing, at least partially, a tooth on the analysis image, the tooth attribute relating to a separation between the tooth represented by the analysis tooth zone, and the aligner represented on the analysis image. For an image attribute of the analysis image, the image attribute relating to a separation between at least one tooth represented on the analysis image, and the aligner represented on the analysis image.

Abstract: A method for analyzing a real dental situation of a patient. The method includes steps, as follows, in succession. At an updated instant, acquisition of an updated image representing a real dental scene as observed by an operator. Simulation, for a simulation instant, of the real dental scene represented on the updated image, then determination of a virtual dental scene as a function of the simulation. Presentation of the virtual dental scene in transparent mode and overlaid on the real dental scene, or display of the updated image on a screen and presentation of the virtual dental scene overlaid with the representation of the real dental scene on the updated image displayed on the screen, in transparent mode or not on the representation.

Abstract: An imaging device including: a support; a mouth retractor fastened to the support and defining a retractor opening; and means for fastening an image acquisition apparatus to the support in a position in which the acquisition apparatus is oriented so as to receive an image of the retractor opening.