WO2020011864A1 - Procede de simulation d'une situation dentaire - Google Patents
Procede de simulation d'une situation dentaire Download PDFInfo
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- WO2020011864A1 WO2020011864A1 PCT/EP2019/068558 EP2019068558W WO2020011864A1 WO 2020011864 A1 WO2020011864 A1 WO 2020011864A1 EP 2019068558 W EP2019068558 W EP 2019068558W WO 2020011864 A1 WO2020011864 A1 WO 2020011864A1
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- image
- orthodontic appliance
- dental
- neural network
- historical
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
- A61C13/00—Dental prostheses; Making same
- A61C13/0003—Making bridge-work, inlays, implants or the like
- A61C13/0004—Computer-assisted sizing or machining of dental prostheses
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
- A61C7/00—Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
- A61C7/002—Orthodontic computer assisted systems
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/24—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for the mouth, i.e. stomatoscopes, e.g. with tongue depressors; Instruments for opening or keeping open the mouth
Definitions
- the present invention relates to a simulation method for generating a hyperrealistic dental view simulating the wearing of an orthodontic appliance.
- Patient adherence to orthodontic treatment is important for the success of this treatment.
- wearing an orthodontic appliance changes the patient's appearance, which can discourage him from carrying out the treatment.
- An object of the invention is to respond, at least partially, to this need.
- the invention provides a simulation process comprising the following steps:
- such a neural network is capable of transforming the original image in a surprisingly realistic manner.
- a method according to the invention thus makes it possible to integrate into the original image a representation of an orthodontic appliance, or to modify an orthodontic appliance represented on the original image, or to delete an orthodontic appliance represented on the 'original image. The patient can thus benefit from a simulation that allows him to properly measure the visual impact of wearing the orthodontic appliance or changing the orthodontic appliance.
- a method according to the invention is remarkable in that the neural network is trained to create a modified image from the original image which is supplied to it.
- This process is therefore quite different from a process in which, for example, we add to an image an element, for example a representation of an existing orthodontic appliance.
- the neural network creates this representation.
- This representation is therefore not the reproduction of a real orthodontic appliance or a three-dimensional model of a real orthodontic appliance, but is generated by the neural network in an artificial way, at the same time as the rest of the image. .
- the representation of the orthodontic appliance is very realistic and allows a good simulation for the patient, as shown in Figure 3B.
- the training of the neural network teaches him to represent the orthodontic appliance in the context of the original image, with the corresponding contrast, sharpness, shadows and reflections.
- the simulation is therefore much more realistic than the simple addition, in an image representing the dental arch, of a pre-existing representation of an orthodontic appliance.
- the modification of the original image by the neural network can lead to modifications of other zones of the original image than the representation zone of the orthodontic appliance.
- a careful comparison of Figures 3A and 3B shows that the profiles of the lower teeth on the original image (Lig. 3A) and on the modified image (Lig. 3B) are slightly different. These differences, which could be harmful if the modified image were used to intervene on the teeth, for example to guide a dentist during a milling operation, are not when the modified image is intended to be presented to the patient. .
- the performance of neural networks can even make it significantly impossible to detect a difference outside the area in which the orthodontic appliance has been represented, as shown by a comparison of FIGS. 6A and 6B.
- - Represent a dental organ, and in particular an orthodontic appliance on an image of a bare dental arch, that is to say not carrying the dental organ; - Suppress the representation of a dental organ, and in particular an orthodontic appliance on an image of an equipped dental arch, that is to say carrying the dental organ; or
- the dental organ is chosen from an orthodontic appliance, a crown, an implant, a bridge, and a veneer;
- the original image is a photo or a view of a three-dimensional digital model of said arch
- said view is rendered hyperreal by means of a “transformation” neural network
- said deformation consists of:
- the modification neural network is trained with a historical learning base made up of a set of historical records, each historical record comprising:
- a historical image chosen from a photo of a dental scene representing a dental arch not wearing an orthodontic appliance, a view of a dental scene representing a dental arch not wearing an orthodontic appliance, a photo of a dental scene representing a dental arch with an orthodontic appliance, and a view of a model representing a dental arch with an orthodontic appliance, and a historical description specifying whether or not the historical image represents an orthodontic appliance
- the type of orthodontic appliance includes active multi-attachment appliances or orthodontic aligners or compression appliances, that is to say, for example, that all active multi-attachment appliances are considered to be of the same type ;
- the modified image is presented on the screen of a patient's telephone or on a mirror, preferably in augmented reality.
- the invention also provides a method for increasing a patient's adherence to orthodontic treatment, the method comprising the following steps:
- step 2 simulation by means of a simulation method according to the invention, the original image acquired in step a) representing a naked dental arch of the patient and the neural network of modification being the neural network chosen at step 1);
- step c) after step c), based on the patient's opinion, determination of orthodontic treatment with an orthodontic appliance of the type chosen in step 1), or repeated in step 1) with another type orthodontic appliance.
- step 3 the patient gives an opinion on the modified image which is presented to him in step c). If satisfied, orthodontic treatment is determined with the orthodontic appliance of the chosen type. The orthodontic appliance having been accepted by the patient, his adherence to the treatment is high.
- Steps b) and c) of a simulation method according to the invention are implemented by a computer, after loading the original image into the computer.
- Step 1) and preferably step 3) are also implemented by a computer.
- the invention therefore also relates to:
- a computer program comprising program code instructions for the execution of these steps b), c), 1) and preferably 3), when said program is executed by a computer, - a computer medium on which such a program is recorded, for example a memory or a CD-ROM.
- a "patient” is a person for whom a method according to the invention is implemented, regardless of whether this person is undergoing orthodontic treatment or not.
- Dental professional means any person qualified to provide dental care, which includes in particular an orthodontist and a dentist.
- a "dental situation” defines a set of characteristics relating to an arch of a patient at an instant, for example the position of the teeth, their shape, the position of an orthodontic appliance, etc. at this moment.
- model is meant a three-dimensional digital model. It consists of a set of voxels.
- An "arch model” is a model representing at least part of a dental arch, preferably at least 2, preferably at least 3, preferably at least 4 teeth (Fig. 4 for example).
- An observation of a model, under determined observation conditions, in particular at a determined angle and distance, is called a "view”.
- An "image” is a two-dimensional, pixel-shaped representation of a scene.
- An extraoral image is an image taken from an observation point outside the mouth, for example taken in front of the patient, preferably with a retractor.
- a “photo” is a particular image, conventionally in color, taken with a camera.
- “Camera” means any device for taking a photo, which includes a camera, mobile phone, tablet or computer. Another example is a view.
- a tooth attribute is an attribute whose value is specific to teeth.
- a value of a tooth attribute is assigned to each tooth zone of the image considered or to each tooth model of a dental arch model considered.
- a tooth attribute does not concern the image or the model as a whole. It takes its value due to the characteristics of the tooth to which it refers.
- a “scene” is made up of a set of elements that can be observed simultaneously.
- a “dental scene” is a scene comprising at least part of a dental arch. It preferably represents at least 2, preferably at least 3, preferably at least 4 teeth.
- photo of an arcade means a photo, a view, a representation, a scan or a model, etc. of all or part of said dental arch.
- a “learning base” is a database of computer records suitable for training a neural network. Each record conventionally comprises an object, for example an image, and information on this object, or "description".
- a description includes values for attributes of the object. For example, an attribute of a dental scene image can be used to identify a type of orthodontic appliance shown. The attribute is then "Type of orthodontic appliance” and the value of this attribute is, for example "active multi-attachment appliance” or "compression appliance”.
- a "local" modification of an original image is a modification which mainly concerns only part of this image, the rest of the image not being substantially modified. In reality, the entire original image can be changed because the image is regenerated. But for an observer who does not pay particular attention, only part of the original image appears modified.
- the modification could consist in adding the representation of an orthodontic appliance on the original image. Outside the representation of the orthodontic appliance, the original image does not appear modified for an observer who does not examine the details.
- Dental organ means any device intended to be worn by the dental arch, and in particular an orthodontic appliance, a crown, an implant, a bridge, or a veneer.
- FIGS. 2A and 2B represent examples of historical photos used to train the modification neural network
- FIG. 3A and 3B respectively represent an original photo and a photo modified by a method according to the invention
- FIG. 4 shows an example of a model of a dental arch
- FIG. 5 shows a view of a model of a dental arch
- FIG. 6A and 6B respectively represent an original photo and a photo modified by a method according to the invention.
- the original image is preferably an extraoral image, for example taken in front of the patient, preferably with a dental retractor.
- the original image can be an "original” photo ( Figure 3).
- the original photo is acquired with a camera, preferably chosen from a mobile phone, a “connected” camera, a so-called “smart” watch, or a “smartwatch”, a tablet or a personal computer, fixed or portable. , including a photo acquisition system.
- a camera preferably chosen from a mobile phone, a “connected” camera, a so-called “smart” watch, or a “smartwatch”, a tablet or a personal computer, fixed or portable.
- the camera is a mobile phone.
- the camera is moved away from the dental arch by more than 5 cm, more than 8 cm, or even more than 10 cm, which avoids condensation of water vapor on the camera lens and makes focusing easier.
- the camera in particular the mobile telephone, is not provided with any specific optics for the acquisition of the original photos, which is in particular possible because of the spacing of the dental arch during acquisition.
- an original photo is in color, preferably in real color.
- the acquisition of the original photo is carried out by the patient, preferably without the use of a immobilizer for the camera, and in particular without a tripod.
- the original image can alternatively be a view, called “original view” (FIG. 5), of a three-dimensional model of the arch, called “original model”.
- Original view (FIG. 5)
- original model can be prepared from measurements made on the patient's teeth or on a molding of his teeth, for example a plaster molding. It is preferably created with a 3D scanner. The original model is then advantageously very precise.
- the original model is theoretical, that is to say does not correspond to a real situation.
- the original model can be created by assembling a set of tooth models chosen from a digital library. The arrangement of the tooth models is determined so that the original model is realistic, that is to say, corresponds to a situation that could have been encountered in a patient.
- the tooth models are arranged in an arc, depending on their nature, and realistically oriented. The use of a model of theoretical origin advantageously makes it possible to simulate dental arches without having to take precise measurements on the patient.
- the view of the original model used as the original image can be acquired after distorting the original model.
- the deformation thus makes it possible to simulate hypothetical dental situations.
- the original model is cut into elementary models, each elementary model representing in 3D an element of the scene that the original model models.
- elementary models for each tooth and / or the tongue, and / or the mouth, and / or the lips, and / or the jaws, and / or the gum.
- the original model thus cut out can then be deformed, for example by moving the tooth models, to simulate the effect of orthodontic treatment or the course of a recurrence, or an aesthetic treatment.
- the distortion of the original model can notably consist of
- a displacement of a tooth model for example to simulate a spacing between two teeth or a bringing together of two teeth
- the original view is made hyperrealistic before step b), preferably by proceeding according to the following steps: i) creation of a learning base “of transformation” made up of more than 1000 records “of transformation ”, each transformation record comprising:
- the transformation neural network can in particular be chosen from the list below.
- Image transformation techniques are further described in the article by Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks.”
- hyperrealistic provides substantially the same information as photos, without having to take photos. It is indeed very difficult to notice that the view of hyperrealistic origin is not a photo. A hyperrealistic image can therefore also be described as “photorealistic”.
- step b) the original image resulting from step a) is subjected to a neural network trained to represent the orthodontic appliance on images, or "modification neural network".
- neural network or “artificial neural network” is a set of algorithms well known to those skilled in the art.
- the neural network can in particular be chosen from: networks specialized in the classification of images, called “CNN” (“Convolutional neural network”), for example
- VGG CNN S V GG CNN M, V GG CNN M 2048, V GG CNN M 10 24, V GG_CNN_M_ 128, V GG CNN F, VGG ILSVRC-2014 l6-layer, VGG ILSVRC-2014 l9-layer , Network-in-Network (Imagenet & CIFAR-10)
- the Object Detection Network for example:
- a neural network To be operational, a neural network must be trained by a learning process called "deep learning”. Such a process is well known.
- the modification neural network can in particular be trained from a historical learning base made up of a set of historical records, each historical record comprising:
- a historical image chosen from a photo of a dental scene representing a dental arch not wearing an orthodontic appliance (Fig. 2A for example), a view of a dental scene representing a dental arch not wearing an orthodontic appliance , a photo of a dental scene representing a dental arch with an orthodontic appliance (Fig. 2B for example), and a view of a model representing a dental arch with an orthodontic appliance, and
- - a historical description specifying whether or not the historical image represents an orthodontic appliance and / or identifying, in this image, the representation of the orthodontic appliance.
- a historical image consisting of a photo or view can be acquired as described above for the acquisition of original photos and original views.
- the descriptions are generated by an operator who observes the historical image and completes the description accordingly, by means of a computer. This operation is called "labeling".
- the latter By presenting the historical recordings at the input of the neural network, the latter gradually learns the difference between an image which represents an orthodontic appliance and an image which does not represent an orthodontic appliance. He thus becomes able, depending on the original image presented to him, to generate a modified image to represent an orthodontic appliance.
- the neural network therefore does not position a view of a 3D model of orthodontic appliance or a pre-existing image of orthodontic appliance on the original image.
- the network regenerates a complete image from the only original image by integrating an orthodontic appliance (created from the original image).
- Training the neural network of modification also allows him to learn to represent an orthodontic appliance in its context, and in particular in lighting and / or sharpness conditions which are those of the original image.
- the integration of the orthodontic appliance is therefore particularly realistic ( Figure 3B).
- the quality of training of the modification neural network directly depends on the number of historical records in the learning base.
- the historical learning base preferably comprises more than 10,000 records.
- the historical learning base preferably comprises more than 5,000, preferably more than 10,000, preferably more than 30,000, preferably more than 50,000, preferably more than 100,000 historical records.
- the quality of the training in the modifying neural network can also be improved if, when the original image is a photo or a view, the historical learning base only contains photos or only views, respectively.
- the quality of training of the modification neural network can be improved if the historical learning base is specialized for a type of orthodontic appliance.
- the neural network is trained with a historical learning base in which the historical images which represent an orthodontic appliance represent only orthodontic appliances of a predetermined type. The modification neural network will thus be efficient in generating a modified image to represent an orthodontic appliance of this type.
- an operator chooses a type of orthodontic appliance to be represented and a computer specializes the learning base accordingly, for example by retaining only the historical records whose historical images do not represent an orthodontic appliance or represent an orthodontic appliance of the selected type.
- step c) the modified image is presented to the patient, preferably on a computer screen or in augmented reality, for example on a screen of a telephone or on a mirror in which the patient looks at himself.
- the patient can thus observe the appearance he will have when he wears the orthodontic appliance, and therefore more easily accept the corresponding treatment.
- the patient takes the original photo, for example with his mobile phone, and a computer, integrated into the mobile phone or with which the mobile phone can communicate, implements the method.
- the edited image is displayed on the mobile phone screen.
- the patient can very easily request a simulation of his dental situation, without even having to move, from one or preferably several photos of his teeth.
- a method according to the invention can also be implemented to generate a modified image representing a simulated dental situation from a digital three-dimensional model of a dental arch.
- the dental situation can be simulated at a time of past or future simulation, as part of a therapeutic treatment or not.
- step a we acquire a view of hyperrealistic origin obtained according to the following steps:
- the modified image thus appears as a photo which would have been taken at the time of simulation and which carries the orthodontic appliance. It can be presented to the patient in order to show him, for example, his future or past dental situation, and thus motivate him to observe orthodontic treatment.
- step a2) the original model is deformed to simulate the effect of time in the event of poor compliance, that is to say if the patient does not comply with medical prescriptions.
- the patient is not limited to a human being.
- a method according to the invention can be used for another animal.
- a learning base does not necessarily consist of recordings of "pairs".
- the article by Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent aciversarial networks" describes other possible learning bases.
- the method is not limited to a method for adding the representation of an orthodontic appliance to an image, but can also be used for any modification of the representation of a dental arch, and in particular for removing the representation. of an orthodontic appliance on an image of a dental arch with an orthodontic appliance or to replace the representation of an orthodontic appliance on an image of a dental arch equipped with an orthodontic appliance by the representation of another orthodontic appliance .
- the training is adapted accordingly. This adaptation poses no particular difficulty.
- the method is not limited to a method for adding or deleting or modifying a representation of an orthodontic appliance on an original image. It extends to the representation of any other dental organ.
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Abstract
Description
Claims
Priority Applications (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US17/259,516 US12303346B2 (en) | 2018-07-13 | 2019-07-10 | Method for simulating a dental situation |
| EP19736741.0A EP3821394A1 (fr) | 2018-07-13 | 2019-07-10 | Procede de simulation d'une situation dentaire |
| BR112021000501-3A BR112021000501A2 (pt) | 2018-07-13 | 2019-07-10 | Método de simulação |
| CN201980046736.3A CN112424820A (zh) | 2018-07-13 | 2019-07-10 | 用于模拟牙齿状况的方法 |
| US19/183,985 US20250248790A1 (en) | 2018-07-13 | 2025-04-21 | Method for simulating a dental situation |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FR1856498 | 2018-07-13 | ||
| FR1856498A FR3083898B1 (fr) | 2018-07-13 | 2018-07-13 | Procede de simulation d'une situation dentaire |
Related Child Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| US17/259,516 A-371-Of-International US12303346B2 (en) | 2018-07-13 | 2019-07-10 | Method for simulating a dental situation |
| US19/183,985 Division US20250248790A1 (en) | 2018-07-13 | 2025-04-21 | Method for simulating a dental situation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2020011864A1 true WO2020011864A1 (fr) | 2020-01-16 |
Family
ID=63684121
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/EP2019/068558 Ceased WO2020011864A1 (fr) | 2018-07-13 | 2019-07-10 | Procede de simulation d'une situation dentaire |
Country Status (6)
| Country | Link |
|---|---|
| US (2) | US12303346B2 (fr) |
| EP (1) | EP3821394A1 (fr) |
| CN (1) | CN112424820A (fr) |
| BR (1) | BR112021000501A2 (fr) |
| FR (1) | FR3083898B1 (fr) |
| WO (1) | WO2020011864A1 (fr) |
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| CN111265317A (zh) * | 2020-02-10 | 2020-06-12 | 上海牙典医疗器械有限公司 | 一种牙齿正畸过程预测方法 |
| USD962437S1 (en) | 2020-05-14 | 2022-08-30 | Get-Grin Inc. | Dental scope |
| US11638636B2 (en) | 2020-02-26 | 2023-05-02 | Get Grin Inc. | Systems and methods for remote dental monitoring |
| US12349871B2 (en) | 2022-04-22 | 2025-07-08 | Get-Grin Inc. | Systems and methods for intraoral imaging |
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| CN111180075B (zh) * | 2020-03-02 | 2023-10-20 | 浙江大学 | 基于心脏杂音建立的动力学模型和计算机模拟仿真的方法 |
| US20220378549A1 (en) * | 2021-06-01 | 2022-12-01 | Align Technology, Inc. | Automated management of clinical modifications to treatment plans using three-dimensional controls |
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| WO2018069736A1 (fr) * | 2016-10-14 | 2018-04-19 | Axial Medical Printing Limited | Procédé de génération d'un modèle physique 3d d'une caractéristique anatomique spécifique d'un patient à partir d'images médicales 2d |
| US20180110590A1 (en) * | 2016-10-20 | 2018-04-26 | Baliram Maraj | Systems and methods for dental treatment utilizing mixed reality and deep learning |
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| US20200405447A1 (en) * | 2013-09-19 | 2020-12-31 | Dental Monitoring | Method for monitoring the position of teeth |
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2019
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- 2019-07-10 BR BR112021000501-3A patent/BR112021000501A2/pt unknown
- 2019-07-10 CN CN201980046736.3A patent/CN112424820A/zh active Pending
- 2019-07-10 US US17/259,516 patent/US12303346B2/en active Active
- 2019-07-10 WO PCT/EP2019/068558 patent/WO2020011864A1/fr not_active Ceased
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2025
- 2025-04-21 US US19/183,985 patent/US20250248790A1/en active Pending
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111265317A (zh) * | 2020-02-10 | 2020-06-12 | 上海牙典医疗器械有限公司 | 一种牙齿正畸过程预测方法 |
| US11638636B2 (en) | 2020-02-26 | 2023-05-02 | Get Grin Inc. | Systems and methods for remote dental monitoring |
| US11957528B2 (en) | 2020-02-26 | 2024-04-16 | Get-Grin Inc. | Systems and methods for remote dental monitoring |
| USD962437S1 (en) | 2020-05-14 | 2022-08-30 | Get-Grin Inc. | Dental scope |
| USD988514S1 (en) | 2020-05-14 | 2023-06-06 | Get-Grin Inc. | Dental scope |
| US12349871B2 (en) | 2022-04-22 | 2025-07-08 | Get-Grin Inc. | Systems and methods for intraoral imaging |
Also Published As
| Publication number | Publication date |
|---|---|
| EP3821394A1 (fr) | 2021-05-19 |
| US20250248790A1 (en) | 2025-08-07 |
| US12303346B2 (en) | 2025-05-20 |
| BR112021000501A2 (pt) | 2021-04-06 |
| FR3083898B1 (fr) | 2020-10-09 |
| FR3083898A1 (fr) | 2020-01-17 |
| US20210267716A1 (en) | 2021-09-02 |
| CN112424820A (zh) | 2021-02-26 |
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