Abstract: Method of enrichment of a reference model to be enriched representing a dental arch. Acquisition, under first real conditions, of a current image of the arch displaying one region. Exploration of the reference model in such a manner as to determine a first view of the reference model, in a first direction of observation the reference image exhibiting a maximum match with the current image. Determination, by comparison of the images, of a first orphan point represented on the current image and not represented on the reference image when the current image is in a first register position in which it is superposed, in the space of the reference model, with the reference image. Addition, in the reference model, of a point on a first straight line parallel to the first direction of observation and passing through the first orphan point in the first register position.

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: 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 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: 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.

Abstract: A method for predicting a future dental situation for a patient. Acquiring historical data including at least: previous time point and context parameter values. Acquiring at a time point all the data related to the current dental situation, including at least: the current time point, context parameter values at the current time point, statistical analysis of the historical data and the current data, so as to predict, at a future time point, at least one future dental situation for the current patient. Depending on the future dental situation, (re)evaluation of the benefit of an orthodontic treatment. Steps may include creating a three-dimensional digital reference model, acquiring at least one two-dimensional image, analysing each updated image and creation, searching, for each updated image, and collecting data relative to the updated reference model and relative to the orthodontic appliance.

Abstract: A method for generating a three-dimensional digital model of a dental arch of a patient. The method includes projection of at least one light beam onto the arch, so as to draw at least one light mark on the arch, and simultaneously, displacement of the arch across the beam and acquisition, during the displacement, of a series of updated images of the arch each showing an updated projection defined by the projected mark. The method includes identification of the updated projection on each updated image, then search for a three-dimensional digital model, called “updated model”, exhibiting a best fit with all the updated projections.

Abstract: A method for predicting a future dental situation for a patient. Acquiring historical data including at least: previous time point and context parameter values. Acquiring at a time point all the data related to the current dental situation, including at least: the current time point, context parameter values at the current time point, statistical analysis of the historical data and the current data, so as to predict, at a future time point, at least one future dental situation for the current patient. Depending on the future dental situation, (re)evaluation of the benefit of an orthodontic treatment. Steps may include creating a three-dimensional digital reference model, acquiring at least one two-dimensional image, analysing each updated image and creation, searching, for each updated image, and collecting data relative to the updated reference model and relative to the orthodontic appliance.

Abstract: Method of analysis of a diagnostic dental representation showing a dental arch of a current patient in several dimensions. The method includes creation of a learning base including more than 1,000 historical dental structures. Each historical dental structure includes a historical dental representation showing an arch of a historical patient in several dimensions and a historical specification containing a value for at least a first attribute relating to a dental object associated with the historical dental representation. The method includes training of at least one deep learning device by use of the learning base. The method includes submission of the diagnostic dental representation to the deep learning device in such a manner that it determines, for the diagnostic dental representation, at least one value for the first attribute.

Abstract: Method for evaluating a dental situation of a patient. The method having the following successive steps: 1) generating an initial model of at least one dental arch of the patient, preferably by means of a scanner; 2) splitting the initial model in order to define a tooth model for at least some of the teeth represented on the initial model and thereby to obtain a split model; 3) determining an initial support curve of the tooth models in the split model; 4) fixing each tooth model virtually on the initial support curve, preferably by computer; 5) modifying the split model by deformation of the initial support curve according to a deformed support curve, so as to obtain a first deformed model, in which the tooth models are aligned according to the deformed support curve; 6) presenting the first deformed model.

Abstract: An imaging device including a support, a mouth retractor fastened to the support and defining a retractor opening, a colorimetric calibration chart and/or a translucence calibration chart and a light source that is oriented so as to illuminate both the teeth of the patient through the retractor opening and the colorimetric calibration chart and/or the translucence calibration chart. Structure 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 both the retractor opening and of the colorimetric calibration chart and/or of the translucence calibration chart.

Abstract: A method for monitoring the positioning of the teeth including production of a three-dimensional digital initial reference model of the arches of the patient and, for each tooth, definition, from the initial reference model, of a three-dimensional digital reference tooth model; acquisition of updated image of at least one two-dimensional image of the arches in actual acquisition conditions; analysis of each updated image and production, for each updated image, of an updated map; optionally, determination, for each updated image, of rough virtual acquisition conditions approximating the actual acquisition conditions; searching, for each updated image, for a final reference model corresponding to the positioning of the teeth during the acquisition of the updated image, for each tooth model, comparison of the positionings of the tooth model in the initial reference model and in the reference model obtained at the end of the preceding steps to determine the movement of the teeth.