Method for analyzing an image of a dental arch

1. A method for assessing the shape of an orthodontic aligner, said method comprising the following steps:

a″) more than 1 week after the start of the treatment with the aligner, 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”, the analysis image being a photograph, or an image extracted from a film;

b″) 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 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,

in the step a″), a cellphone is used to acquire the analysis image.

2. The method as claimed in claim 1 , comprising, after the step b″), step c″) as follows:

c″) assessment of the suitability of the aligner as a function of the value of said tooth attribute.

3. The method as claimed in claim 2 , in which, in the step b″), all said analysis tooth zones are identified, and the tooth attribute value is determined for each analysis tooth zone, and, in the step c″), the suitability of the aligner is determined as a function of said tooth attribute values.

4. The method as claimed in claim 2 , comprising, after the step c″), a step d″) as follows:

d″) sending of an information message as a function of said assessment.

5. The method as claimed in claim 2 , in which, as a function of the result of said assessment, a new aligner is manufactured.

6. The method as claimed in claim 1 , in which said tooth attribute is chosen from a tooth number, a tooth type, a tooth shape parameter, a tooth appearance parameter, a parameter relating to the state of the tooth, an age for the patient, or a combination of these attributes.

7. The method as claimed in claim 1 , in which the step b″) comprises the following steps:

1) creation of a learning base comprising more than 1000 images of dental arches, or “historical images”, each historical image representing an aligner worn by a “historical” patient and comprising one or more zones each representing a tooth, or “historical tooth zones”, to each of which, for at least one tooth attribute relating to a separation between the tooth represented by the historical tooth zone considered, and the aligner represented, a tooth attribute value is assigned;

2) training of at least one deep learning device, by means of the learning base;

3) submission of the analysis image to the deep learning device for it to determine at least one probability relating to:

the presence, in a location of said analysis image, of an analysis tooth zone, and

the attribute value of the tooth represented on said analysis tooth zone;

4) determination, as a function of said probability, of the presence of a separation of the aligner and of the tooth represented by said analysis tooth zone.

8. The method as claimed in claim 1 , in which the step b″) comprises the following steps:

1′) creation of a learning base comprising more than 1000 images of dental arches, or “historical images”, each historical image comprising an attribute value for at least one image attribute, or “image attribute value”, the image attribute relating to a separation between the tooth or teeth and the aligner represented on the historical image;

2′) training of at least one deep learning device, by means of the learning base;

3′) submission of the analysis image to the deep learning device for it to determine, for said analysis image, at least one probability relating to said image attribute value.

9. The method as claimed in claim 1 , in which the learning base is constructed according to the following steps:

A) at an “updated” instant, production of a model of a dental arch of a patient, or “updated reference model”, and segmentation of the updated reference model so as to produce, for each tooth, a “tooth model”, and, for at least one tooth attribute, assignment of a tooth attribute value to each tooth model;

B) acquisition of at least one image of said arch, or “updated image”, in real acquisition conditions;

C) for each updated image, search for virtual acquisition conditions suitable for an acquisition of an image of the updated reference model, called “reference image”, exhibiting a maximum match with the updated image in said virtual acquisition conditions, and acquisition of said reference image;

D) identification, in the reference image, of at least one zone representing a tooth model, or “reference tooth zone” and, by comparison of the updated image and of the reference image, determination, in the updated image, of a zone representing said tooth model, or “updated tooth zone”;

E) assignment, to said updated tooth zone, of the tooth attribute value of said tooth model;

F) addition of the updated image enriched with a description of said updated tooth zone and its tooth attribute value, or “historical image”, in the learning base.

10. The method as claimed in claim 1 , in which the learning base is constructed according to the following steps:

A′) at an initial instant, production of a model of a dental arch of a patient, or “initial reference model”, and segmentation of the initial reference model so as to produce, for each tooth, a “tooth model”, and, for at least one tooth attribute, assignment of a tooth attribute value to each tooth model;

B′) at an updated instant, acquisition of at least one image of said arch, or “updated image”, in real acquisition conditions;

C′) search, by deformation of the initial reference model, for an updated reference model and virtual acquisition conditions suitable for an acquisition of an image of the updated reference model, called “reference image”, exhibiting a maximum match with the updated image in said virtual acquisition conditions;

D) identification, in the reference image, of at least one zone representing a tooth model, or “reference tooth zone” and, by comparison of the updated image and the reference image, determination, in the updated image, of a zone representing said tooth model, or “updated tooth zone”;

E) assignment, to said updated tooth zone, of the tooth attribute value of said tooth model;

F) addition of the updated image enriched with a description of said updated tooth zone and its tooth attribute value, or “historical image”, in the learning base.

11. The method as claimed in claim 1 , in which, to create a historical image of the learning base, an operator,

identifies one or more “historical” tooth zones on an image, called then assigns, to each identified historical tooth zone, a value for at least one tooth attribute, and/or

assigns to an image a value for at least one tooth attribute.

12. The method as claimed in claim 1 , in which, in the step a″), a cellphone is used to acquire the analysis image.

13. The method as claimed in claim 1 , in which in step b″), the deep learning device is a neural network.

14. The method as claimed in claim 1 , in which the step b″) comprises the following steps:

1) creation of a learning base comprising more than 1000 images of dental arches, or “historical images”, each historical image representing an aligner worn by a “historical” patient and comprising one or more zones each representing a tooth, or “historical tooth zones”, to each of which, for at least one tooth attribute relating to a separation between the tooth represented by the historical tooth zone considered, and the aligner represented, a tooth attribute value is assigned;

2) training of at least one deep learning device, by means of the learning base;

3) submission of the analysis image to the deep learning device for it to determine at least one probability relating to:

the presence, in a location of said analysis image, of an analysis tooth zone; and

the attribute value of the tooth represented on said analysis tooth zone;

4) determination, as a function of said probability, of an amplitude of said separation.

15. A method for assessing the shape of an orthodontic aligner, said method comprising the following steps:

a″) more than 1 week after the start of the treatment with the aligner, 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” the analysis image being a photograph, or an image extracted from a film;

b″) 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 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.

16. The method as claimed in claim 15 , comprising, after the step b″), step c″) as follows:

c″) assessment of the suitability of the aligner as a function of the value of said image attribute.