CN114099016B - Digital twin model-based fixed correction and invisible correction hybrid treatment system - Google Patents
Digital twin model-based fixed correction and invisible correction hybrid treatment system Download PDFInfo
- Publication number
- CN114099016B CN114099016B CN202111353026.0A CN202111353026A CN114099016B CN 114099016 B CN114099016 B CN 114099016B CN 202111353026 A CN202111353026 A CN 202111353026A CN 114099016 B CN114099016 B CN 114099016B
- Authority
- CN
- China
- Prior art keywords
- data
- correction
- module
- processing
- data processing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Dentistry (AREA)
- Epidemiology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Dental Tools And Instruments Or Auxiliary Dental Instruments (AREA)
Abstract
The invention belongs to the field of oral cavity digitization, and particularly discloses a digital twin model-based fixed correction and invisible correction hybrid treatment system, which comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring dental jaw face neck soft and hard tissue image data and the like of a patient; the data processing module is used for processing the acquired images and data; the digital twin module is used for integrating the data acquisition information, the processing information and the data scanning information uniformly, and constructing a digital twin model and carrying out operation processing; the machine learning module is used for collecting data processing and scheme design of mixed treatment of fixed correction and invisible correction; the scheme selection module is used for selecting fixed correction and/or invisible correction schemes; the correction effect prediction module is used for outputting the change condition of the soft and hard tissues after the teeth are corrected; and the scheme revising module revises the preferred treatment scheme according to the predicted correction effect. The system of the invention fully combines the advantages of different correction appliances and can greatly shorten the treatment time.
Description
Technical Field
The invention relates to the field of oral cavity digitization, in particular to a digital twin model-based fixed correction and invisible correction hybrid treatment system.
Background
With the continuous improvement of economic development and living standard of people, the medical and health problems become the focus of social attention. In recent years, digital twinning, internet of things, big data and other information technologies are gradually applied to the medical industry, and traditional medical treatment is being transformed into digital medical treatment and intelligent medical treatment. Digital twinning (Digital Twin) is to digitally build a multi-dimensional, multi-time-space-scale, multi-disciplinary, multi-physical-quantity dynamic virtual model of a physical entity to simulate and characterize properties, behaviors, rules, etc. of the physical entity in a real environment. The concept of digital twinning was originally proposed in 2003 by grives professor U.S. michigan university product lifecycle management curriculum, and was primarily applied in the military and aerospace fields early on. Such as the united states air force research laboratory, the national aviation administration (NASA) developed aircraft health management applications based on digital twinning. Because digital twinning has the characteristics of virtual-real fusion and real-time interaction, iterative operation and optimization, full factor/full flow/full service data driving and the like, the digital twinning is applied to various stages of a product life cycle, including product design, manufacturing, service, operation and maintenance and the like. Digital twinning is widely applied in manufacturing industry, is applied to aerospace at the earliest, is widely applied in different research directions along with deep research, and has reports and practical cases in the fields of electric power, communication, automobile manufacturing and the like.
In recent years, the digital twin research field is gradually expanded to the medical industry, tao Fei et al propose the concept of digital twin medical treatment, apply the digital twin five-dimensional model to a medical health system, realize the functions of prognosis of human diseases, implementation of remote treatment and the like, perform virtual human training and training of medical staff, and effectively change the current state of medical health; methods such as artificial intelligence, machine learning, big data, etc. have been widely used in the medical field and even in the orthodontic field.
Orthodontic generally requires two and a half years to three years, and treatment time is long, so that three years of adolescence is very valuable for patients in adolescence treatment. How to shorten the treatment course while ensuring the correction quality and controlling the treatment risk is an urgent problem to be solved in the current orthodontic treatment. Current appliances are classified as fixed appliances and invisible braces. The fixing correction has better performance than the invisible correction in the aspects of maintaining the stability of the occlusal plane and the support of the occlusal relation of the posterior teeth, protecting the anchorage and the like, but the stainless steel wire can enter the groove of the fixing corrector only by repeated long-time alignment and leveling at the early stage, and the period of time is long. The fixed appliance requires the teeth to be aligned first, starting with a soft wire and gradually changing to a hard wire to be able to move the teeth, which takes about 6-10 months. Invisible correction is a digital product, treatment targets are designed firstly, treatment is guided from beginning to end, the invisible corrector has the excellent performance of early starting and closing gap movement teeth, but later adjustment needs repeated restarting treatment plan, and the treatment period is prolonged. Thus, the invisible appliance does not need to align the teeth as a fixed appliance, has a soft wire, gradually changes to a hard wire to move the teeth, and basically starts to move after putting on the dental mouthpiece, so that the time of about 6-10 months is saved in the initial stage, but the difference between the movement of the teeth and the layout of the appliance design is larger and larger as the dental mouthpiece is gradually changed. Therefore, the correction is half that the invisible correction scheme has to be restarted, and a new dental mouthpiece is manufactured again, and the course of treatment is generally longer after three times of restarting from beginning to end. In addition, for medical records of tooth extraction correction, after closing the tooth extraction gap, the occlusal plane can be folded, the long axis of the teeth can be abnormal, and after restarting a new treatment scheme and a new corrector, the average treatment time of the invisible correction is about 4 years or even longer. Orthodontic treatment is to improve efficiency, and shortening treatment course is very important to periodontal health, tooth root health and life quality of patients.
Disclosure of Invention
The invention aims to provide a digital twin model-based fixed correction and invisible correction hybrid treatment system so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: the digital twin model-based fixed correction and invisible correction hybrid treatment system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring dental maxillofacial neck soft and hard tissue image data, tooth scanning data, facial image data, breathing data, pronunciation data, saliva data, blood data, gene data and whole body disease data of a patient, and the facial image comprises a two-dimensional facial photograph, a three-dimensional, static and dynamic image; the data processing module is used for processing the acquired images and data, and the processing process comprises image segmentation, face extraction, respiratory data processing, pronunciation analysis processing, saliva processing and systemic disease data processing; the digital twin module is used for integrating the data acquisition information, the processing information and the data scanning information uniformly, and constructing a digital twin model and carrying out operation processing; the machine learning module is used for collecting data processing and scheme design of mixed treatment of fixed correction and invisible correction; the scheme selection module is used for selecting a fixed correction and/or invisible correction scheme, and the treatment time and the order of loading the fixed correction device or the invisible correction device for selecting the fixed correction and/or the invisible correction scheme; the correction effect prediction module is used for outputting the change condition of the soft and hard tissues after the teeth are corrected, predicting the development change, and prompting the treatment risk and the treatment point needing improvement; and the scheme revising module revises the preferred comprehensive treatment scheme with the shortest treatment course according to the predicted correction effect.
Preferably, the collecting process of the data collecting module comprises the following steps: acquiring data of jawbone, face and neck of a patient based on CT, and scanning intraoral tooth data by an intraoral scanner and facial soft tissue contours by a facial scanner; taking two-dimensional and three-dimensional facial photos and videos based on cameras, wherein the two-dimensional and three-dimensional facial photos comprise static and dynamic facial contours and expression changes; collecting pronunciation characteristics, frequency and depth of breath based on a microphone sensor; collecting pronunciation characteristics of a section of sentences of a patient, including a seesaw sound, a flat tongue sound, a vowel sound and a nasal sound, based on a microphone, wherein the section of sentences include but are not limited to mandarin, dialect and foreign language; detecting the secretion amount, viscosity and secreted protein molecular components and content of saliva based on a saliva detector; collecting protein molecular components and contents in blood based on a blood collection analyzer; analyzing and screening root absorption and alveolar bone wall genes based on an oral detection process; screening whether the patients have epilepsy, rheumatism, hepatitis, nephritis, tuberculosis, diabetes, heart disease, hemophilia and rickets based on the whole body disease collecting process.
Preferably, the data processing module comprises a maxillofacial neck data processing unit, a facial image processing unit, a breathing data processing unit, a pronunciation data processing unit, a saliva data processing unit, a blood data processing unit and a gene data processing unit, and the image segmentation in the processing process of the data processing module comprises tooth separation, periodontal ligament, jawbone, tooth position and crowding degree, upper and lower jaw dentition relation, tooth midline position and airway width; the processing process of the facial image processing unit comprises extraction of facial contours and expression features, nose-lip distance and facial midline position; the processing process of the breathing data processing unit comprises the steps of analyzing the pronunciation characteristics, frequency and depth of breathing, and judging whether an apnea syndrome exists or not; the processing procedure of the pronunciation data processing unit comprises extracting and analyzing the characteristic points of the seesaw sound, the flat tongue sound, the vowel and the nasal sound, the characteristic points of mandarin, dialect and foreign language voice, and the characteristic points of cleft lip and palate voice, and carrying out pronunciation data analysis and processing; the saliva data processing unit processes include analysis of saliva secretion, viscosity, protein molecule composition and content.
The treatment method of the digital twin model-based fixed correction and stealth correction hybrid treatment system comprises the following steps:
s1: collecting dental maxillofacial neck soft and hard tissue image data, tooth scanning data, facial image data, respiratory data, pronunciation data, saliva data, blood data, gene data and the like of a patient;
s2: processing the acquired image and data;
s3: uniformly integrating data acquisition information, processing information, 3D printing information, mechanical analysis information and data scanning information, and constructing a digital twin model and operation processing;
s4: the method comprises the steps of collecting data processing and scheme design of mixed treatment of fixed correction and invisible correction through a machine learning module, and determining the comprehensive treatment scheme with the shortest treatment course according to the treatment time selected by the fixed correction and/or invisible correction scheme and the sequence of loading the fixed correction device or the invisible correction device;
s5: the scheme selection module performs fixed correction and/or invisible correction scheme selection according to a specific patient;
s6: outputting the change condition of the soft and hard tissues after the tooth correction by the correction effect prediction module, and predicting the development change, the treatment risk prompt and the treatment point needing improvement;
s7: based on the predicted correction effect output by the correction effect prediction module, the preferred treatment scheme is intelligently revised.
Preferably, in the fixing correction stage in S5, the system can automatically identify the crown center point of each tooth through crown center point identification, and automatically transfer the position of the crown center point into an indirectly connected guide plate which is connected to the fixing corrector; and S5, a stealth correction stage to close the tooth extraction gap.
Compared with the prior art, the invention has the beneficial effects that:
the system is based on a digital twin model, optimizes the invisible appliance and the fixed appliance during the correction treatment, fully combines the advantages of different appliances, can greatly shorten the treatment time and improve the treatment effect, closes the tooth extraction gap by the fixed appliance after the tooth extraction correction, simultaneously corrects the fixed appliance, and aligns the teeth by a digital product.
Drawings
FIG. 1 is a block diagram of a system module of the present invention;
FIG. 2 is a schematic diagram of the selection of the fixed and invisible appliances by the scheme selection module according to the embodiment of the present invention.
In the figure: 1. a data acquisition module; 2. a data processing module; 3. a digital twinning module; 4. a machine learning module; 5. a scheme selection module; 6. the correction effect prediction module; 7. and a scheme revision module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides a technical solution: the digital twin model-based fixed correction and invisible correction hybrid treatment system comprises a data acquisition module 1, wherein the data acquisition module is used for acquiring dental facial neck soft and hard tissue image data, tooth scanning data, facial image data, respiratory data, pronunciation data, saliva data, blood data, gene data and whole body disease number of a patient, and the facial image comprises a two-dimensional facial photograph, a three-dimensional, static and dynamic image;
the data processing module 2 is used for processing the acquired images and data, and the processing process comprises image segmentation, face extraction, respiratory data processing, pronunciation analysis processing, saliva processing and systemic disease data processing;
the digital twin module 3 is used for integrating the data acquisition information, the processing information and the data scanning information uniformly, and constructing a digital twin model and carrying out operation processing;
the machine learning module 4 is used for collecting data processing and scheme design of the mixed treatment of the fixed correction and the invisible correction;
the scheme selection module 5 is used for selecting the fixed correction and/or invisible correction scheme, selecting the treatment time of the fixed correction and/or invisible correction scheme and the sequence of loading the fixed correction device or the invisible correction device, and determining the comprehensive treatment scheme with the shortest treatment course;
the correction effect prediction module 6 is used for outputting the change condition of the soft and hard tissues after the teeth are corrected, predicting the development change, and prompting the treatment risk and the treatment point needing improvement;
the scheme revision module 7 revises the preferred treatment scheme according to the predicted correction effect.
In this embodiment, the acquisition process of the data acquisition module 1 includes: acquiring data of jawbone, face and neck of a patient based on CT, and scanning intraoral tooth data by an intraoral scanner and facial soft tissue contours by a facial scanner; taking two-dimensional and three-dimensional facial photos and videos based on cameras, wherein the two-dimensional and three-dimensional facial photos comprise static and dynamic facial contours and expression changes; collecting pronunciation characteristics, frequency and depth of breath based on a microphone sensor; collecting pronunciation characteristics of a section of sentences of a patient, including a seesaw sound, a flat tongue sound, a vowel sound and a nasal sound, based on a microphone, wherein the section of sentences include but are not limited to mandarin, dialect and foreign language; detecting the secretion amount, viscosity and secreted protein molecular components and content of saliva based on a saliva detector; collecting protein molecular components and contents in blood based on a blood collection analyzer; analyzing and screening root absorption and alveolar bone wall genes based on an oral detection process; screening whether the patients have epilepsy, rheumatism, hepatitis, nephritis, tuberculosis, diabetes, heart disease, hemophilia and rickets based on the whole body disease collecting process.
In this embodiment, the data processing module 2 includes a maxillofacial neck data processing unit, a facial image processing unit, a respiratory data processing unit, a pronunciation data processing unit, a saliva data processing unit, a blood data processing unit, and a gene data processing unit, and the image segmentation in the processing of the data processing module 2 includes separating teeth, periodontal ligament, jawbone, tooth position and crowding degree, upper and lower jaw dentition relationship, tooth midline position, and width of air flue; the processing process of the facial image processing unit comprises extraction of facial contours and expression features, nose-lip distance and facial midline position; the processing process of the breathing data processing unit comprises the steps of analyzing the pronunciation characteristics, frequency and depth of breathing, and judging whether an apnea syndrome exists or not; the processing procedure of the pronunciation data processing unit comprises extracting and analyzing the characteristic points of the seesaw sound, the flat tongue sound, the vowel and the nasal sound, the characteristic points of mandarin, dialect and foreign language voice, and the characteristic points of cleft lip and palate voice, and carrying out pronunciation data analysis and processing; the saliva data processing unit processes include analysis of saliva secretion, viscosity, protein molecule composition and content.
In this embodiment, during the processing of the data processing module 2, DGCNN is used to separate clinical dental crown data from oral scan data, and then manually marked CT samples are used to perform supervised training on UNet neural network feature functions, usingAs a loss function, dividing CT image data, establishing a model file comprising separated teeth, periodontal ligament and jaw bone, and carrying out tooth position, crowding degree, upper and lower jaw dentition relation and tooth center line position on the basis of the model file; performing feature point positioning and regression analysis on medical images such as X-ray side position plates by using a deep convolutional neural network CNN, calculating quantitative analysis indexes of the air passage, and determining the width of the air passage; the facial image processing unit comprises extracting facial contour and expression features by using Sift feature, and calculating nose lip distance and facial midline based on the featureA location; the processing process of the breathing data processing unit comprises the steps of analyzing the pronunciation characteristics, frequency and depth of breathing, and judging whether an apnea syndrome exists or not; the processing procedure of the pronunciation data processing unit comprises extracting and analyzing the characteristic points of the seesaw sound, the flat tongue sound, the vowel and the nasal sound, the characteristic points of mandarin, dialect and foreign language voice, and the characteristic points of cleft lip voice, extracting the characteristics of voice signals by using an LSTM (least squares) circulating neural network, and analyzing and processing pronunciation data.
In this embodiment, the digital twin module 3 is configured to integrate the collected information uniformly based on artificial intelligence and/or virtual reality and/or big data, and output the integrated information to the data processing software to construct a digital twin model, and perform operation processing.
In this embodiment, the method for treating the digital twin model-based hybrid correction and stealth correction treatment system includes the steps of:
s1: collecting dental maxillofacial neck soft and hard tissue image data, tooth scanning data, facial image data, respiratory data, pronunciation data, saliva data, blood data, gene data and the like of a patient;
s2: processing the acquired image and data;
s3: uniformly integrating data acquisition information, processing information, 3D printing information, mechanical analysis information and data scanning information, and constructing a digital twin model and operation processing;
s4: the machine learning module 4 is used for collecting the data processing and scheme design of the mixed treatment of the fixed correction and the invisible correction, the treatment time selected by the fixed correction and/or the invisible correction scheme and the sequence of loading the fixed correction device or the invisible correction device, and determining the treatment scheme with the shortest treatment course;
s5: the scheme selection module 5 performs fixed correction and/or invisible correction scheme selection according to a specific patient (a fixed correction stage, through crown center point identification, a system can automatically identify the crown center point of each tooth, and the position of the crown center point is automatically transferred into a guide plate which is indirectly connected and is connected to a fixed corrector;
s6: outputting the change condition of the soft and hard tissues after the tooth correction by the correction effect prediction module 6, and predicting the development change, the treatment risk prompt and the treatment point needing improvement;
s7: based on the predicted corrective effect output by the corrective effect prediction module 6, the preferred treatment scheme is intelligently revised.
In the present embodiment, the machine learning module 4 performs machine algorithm learning based on the case data input.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A digital twin model-based hybrid stationary correction and stealth correction therapy system, comprising:
the data acquisition module (1) is used for acquiring dental maxillofacial neck soft and hard tissue image data, tooth scanning data, facial image data, breathing data, pronunciation data, saliva data, blood data, gene data and whole body disease data of a patient, and the facial image comprises a two-dimensional facial photo, a three-dimensional, static and dynamic image;
the data processing module (2) is used for processing the acquired images and data, and the processing process comprises image segmentation, face extraction, respiratory data processing, pronunciation analysis processing, saliva data processing and systemic disease data processing;
the digital twin module (3) is used for integrating the data acquisition information, the processing information and the data scanning information uniformly, and constructing a digital twin model and carrying out operation processing;
the machine learning module (4) is used for collecting data processing and scheme design of mixed treatment of fixed correction and invisible correction, and the treatment time and the sequence of loading the fixed correction device or the invisible correction device selected by the fixed correction and/or the invisible correction scheme;
the scheme selection module (5) is used for selecting a fixed correction and/or invisible correction scheme, and selecting treatment time and loading order of the fixed correction device or the invisible correction device by the fixed correction and/or the invisible correction scheme;
the correction effect prediction module (6) is used for outputting the change condition of the soft and hard tissues after the teeth are corrected, predicting the development change, and prompting the treatment risk and the treatment point needing improvement;
and a scheme revising module (7) revises the preferred treatment scheme according to the predicted correction effect.
2. The digital twin model based hybrid stationary and stealth corrective therapy system according to claim 1, wherein the acquisition process of the data acquisition module (1) comprises: acquiring data of jawbone, face and neck of a patient based on CT, and scanning intraoral tooth data by an intraoral scanner and facial soft tissue contours by a facial scanner; taking two-dimensional and three-dimensional facial photos and videos based on cameras, wherein the two-dimensional and three-dimensional facial photos comprise static and dynamic facial contours and expression changes; collecting pronunciation characteristics, frequency and depth of breath based on a microphone sensor; collecting pronunciation characteristics of a section of sentences of a patient, including a seesaw sound, a flat tongue sound, a vowel and a nasal sound, based on a microphone, wherein the section of sentences include mandarin, dialect and foreign language; detecting the secretion amount, viscosity and secreted protein molecular components and content of saliva based on a saliva detector; collecting protein molecular components and contents in blood based on a blood collection analyzer; screening whether the patients have epilepsy, rheumatism, hepatitis, nephritis, tuberculosis, diabetes, heart disease, hemophilia and rickets based on the whole body disease collecting process.
3. The digital twin model-based hybrid stationary correction and stealth correction treatment system according to claim 1, wherein the data processing module (2) comprises a maxillofacial neck data processing unit, a facial image processing unit, a respiratory data processing unit, a pronunciation data processing unit, a saliva data processing unit, a blood data processing unit and a genetic data processing unit; the processing process of the facial image processing unit comprises extraction of facial contours and expression features, and calculation of nose lip distance and facial midline position; the processing process of the breathing data processing unit comprises the steps of analyzing the pronunciation characteristics, frequency and depth of breathing, and judging whether an apnea syndrome exists or not; the processing procedure of the pronunciation data processing unit comprises extracting and analyzing the characteristic points of the seesaw sound, the flat tongue sound, the vowel and the nasal sound, the characteristic points of mandarin, dialect and foreign language voice, and the characteristic points of cleft lip and palate voice, and carrying out pronunciation data analysis and processing; the saliva data processing unit processes include analysis of saliva secretion, viscosity, protein molecule composition and content.
4. The digital twin model-based fixed correction and stealth correction hybrid therapy system according to claim 1, wherein the digital twin module (3) is configured to unify and integrate collected data acquisition information, processing information, data scanning information based on artificial intelligence and/or virtual reality and/or big data, and output the integrated data acquisition information, processing information, data scanning information to data processing software to construct a digital twin model, and perform operation processing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111353026.0A CN114099016B (en) | 2021-11-16 | 2021-11-16 | Digital twin model-based fixed correction and invisible correction hybrid treatment system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111353026.0A CN114099016B (en) | 2021-11-16 | 2021-11-16 | Digital twin model-based fixed correction and invisible correction hybrid treatment system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114099016A CN114099016A (en) | 2022-03-01 |
| CN114099016B true CN114099016B (en) | 2023-08-01 |
Family
ID=80395866
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111353026.0A Active CN114099016B (en) | 2021-11-16 | 2021-11-16 | Digital twin model-based fixed correction and invisible correction hybrid treatment system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114099016B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117726659A (en) * | 2022-06-20 | 2024-03-19 | 杭州朝厚信息科技有限公司 | Method for generating simulated dental orthodontic post-treatment side shots |
| CN115620871A (en) * | 2022-11-21 | 2023-01-17 | 优铸科技(北京)有限公司 | Corrector information generation method and device for invisible orthodontic platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107811715A (en) * | 2016-09-14 | 2018-03-20 | 亚力士电脑机械股份有限公司 | Digital teeth antidote assembly |
| WO2018101785A1 (en) * | 2016-12-01 | 2018-06-07 | 조건제 | Hybrid orthodontic appliance and method for manufacturing same |
| CN110403719A (en) * | 2019-07-02 | 2019-11-05 | 浙江工业大学 | A kind of sublevel segmentation aesthetics orthodontic therapy method and the double round tube lingual brackets of standard |
| CN113180858A (en) * | 2021-05-28 | 2021-07-30 | 天津正丽科技有限公司 | Combined controllable appliance system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10589087B2 (en) * | 2003-11-26 | 2020-03-17 | Wicab, Inc. | Systems and methods for altering brain and body functions and for treating conditions and diseases of the same |
| CN116602778A (en) * | 2017-10-31 | 2023-08-18 | 阿莱恩技术有限公司 | Dental appliance with selective bite loading and controlled tip staggering |
| EP3996584A4 (en) * | 2019-08-13 | 2023-08-09 | Twin Health, Inc. | Improving metabolic health using a precision treatment platform enabled by whole body digital twin technology |
| US20210241909A1 (en) * | 2020-02-03 | 2021-08-05 | Koninklijke Philips N.V. | Method and a system for evaluating treatment strategies on a virtual model of a patient |
-
2021
- 2021-11-16 CN CN202111353026.0A patent/CN114099016B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107811715A (en) * | 2016-09-14 | 2018-03-20 | 亚力士电脑机械股份有限公司 | Digital teeth antidote assembly |
| WO2018101785A1 (en) * | 2016-12-01 | 2018-06-07 | 조건제 | Hybrid orthodontic appliance and method for manufacturing same |
| CN110403719A (en) * | 2019-07-02 | 2019-11-05 | 浙江工业大学 | A kind of sublevel segmentation aesthetics orthodontic therapy method and the double round tube lingual brackets of standard |
| CN113180858A (en) * | 2021-05-28 | 2021-07-30 | 天津正丽科技有限公司 | Combined controllable appliance system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114099016A (en) | 2022-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114099016B (en) | Digital twin model-based fixed correction and invisible correction hybrid treatment system | |
| CN108735292B (en) | Removable partial denture scheme decision method and system based on artificial intelligence | |
| KR20190020756A (en) | Method for estimating at least one of shape, position and orientation of a dental restoration | |
| Tian et al. | DCPR-GAN: dental crown prosthesis restoration using two-stage generative adversarial networks | |
| US20200100724A1 (en) | Applying non-real time and non-user attended algorithms to stored non-imaging data and existing imaging data for obtaining a dental diagnosis | |
| JP6777917B1 (en) | Estimator, estimation system, estimation method, and estimation program | |
| Chen et al. | Missing teeth and restoration detection using dental panoramic radiography based on transfer learning with CNNs | |
| Kurup et al. | Dentistry and artificial intelligence | |
| CN114224529B (en) | Digital twin model mapping system for oral cavity, dental jaw face and neck and establishing method | |
| CN114224528B (en) | Oral cavity digital twin model system based on virtual reality interaction and establishment method | |
| US20240029901A1 (en) | Systems and Methods to generate a personalized medical summary (PMS) from a practitioner-patient conversation. | |
| CN111275808B (en) | Method and device for establishing tooth orthodontic model | |
| Runte et al. | Symmetry and aesthetics in dentistry | |
| CN116052890B (en) | Tooth implant three-dimensional scanning modeling system and method based on Internet of things | |
| Budală et al. | A Contemporary Review of Clinical Factors Involved in Speech-Perspectives from a Prosthodontist Point of View | |
| EP3968277A1 (en) | Segmentation device | |
| JP6771687B1 (en) | Estimator, estimation system, estimation method, and estimation program | |
| Sikri et al. | Artificial intelligence in prosthodontics and oral implantology–A narrative review | |
| Banerjee | Artificial intelligence in dentistry: A ray of hope | |
| Larkin et al. | Accuracy of artificial intelligence‐assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2‐year growth interval | |
| JP6777916B1 (en) | Estimator, estimation system, estimation method, and estimation program | |
| JP7496995B2 (en) | Estimation device, estimation method, and estimation program | |
| CN117238509B (en) | Difficult airway assessment system and assessment method based on common camera data | |
| Sahu et al. | Technological Empowerment: Applications of Machine Learning in Oral Healthcare | |
| Wu et al. | Application study on the recognition of oral obstructed tooth images using semantic segmentation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |