CN114496254A - Gingivitis evaluation system construction method, gingivitis evaluation system and gingivitis evaluation method - Google Patents
Gingivitis evaluation system construction method, gingivitis evaluation system and gingivitis evaluation method Download PDFInfo
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Abstract
The embodiment of the invention provides a construction method of a gingivitis evaluation system, a gingivitis evaluation system and an evaluation method, and solves the problems that the existing gingivitis evaluation system is low in accuracy and cannot evaluate a single tooth specifically. The construction method of the gingivitis evaluation system comprises the following steps: obtaining a plurality of dentition and periodontal soft tissue morphologies; segmenting the dentition and periodontal soft tissue morphology into single tooth data; labeling teeth with gingivitis based on the single tooth data, and establishing an expert knowledge base; training the expert knowledge base, and establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base.
Description
Technical Field
The invention relates to the technical field of medical instruments, in particular to a construction method of a gingivitis evaluation system, a gingivitis evaluation system and a gingivitis evaluation method.
Background
Periodontal disease is a multi-factor chronic inflammation mediated by plaque biofilm, leading to alterations in the morphology of periodontal soft and hard tissues. The loss of teeth caused by periodontitis is the first reason for the loss of teeth of adults in China. The fourth national oral epidemiological survey shows that the proportion of peridental health of adults in our country is less than 10%. The color, shape and quality of periodontal soft tissue caused by periodontal disease are important indexes for evaluating periodontal disease. The gingival color change can visually reflect the severity of the inflammation, and the quantification of the gingival color change can be used for evaluating the improvement of the gingival inflammation condition before and after treatment and the treatment effect. However, most of the existing training models are digital photos, which are affected by operators, camera brands, shooting environments, shooting angles and the like, and the digital photos are two-dimensional images and only contain color information but not form information of periodontal soft tissue, so that the accuracy of evaluation results is affected, and evaluation cannot be performed on a single tooth.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method for constructing a gingivitis evaluation system, and a gingivitis evaluation method, which solve the problems that the existing gingivitis evaluation system has low accuracy and cannot evaluate a single tooth specifically.
The construction method of the gingivitis evaluation system, the tooth evaluation system and the evaluation method provided by the embodiment of the invention comprise the following steps:
obtaining a plurality of dentition and periodontal soft tissue morphologies;
segmenting the dentition and periodontal soft tissue morphology into single tooth data;
labeling teeth with gingivitis based on the single tooth data, and establishing an expert knowledge base;
training the expert knowledge base, and establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base.
In one embodiment, the step of labeling the teeth with gingivitis based on the single tooth data and establishing an expert knowledge base comprises:
performing gingivitis grade scoring on teeth based on the single-tooth data to form a single-tooth data set, wherein the single-tooth data is colorful three-dimensional data;
performing data amplification on the data set;
and establishing an expert knowledge base based on the data set after data amplification.
In one embodiment, the method of data amplification of the data set comprises: and performing data amplification on the data set by adopting at least one of scaling, local cropping and rotating modes.
In one embodiment, the augmented data set includes training data, validation data, and test data.
In one embodiment, the training of the expert knowledge base, and the establishing of the gingivitis evaluation system based on the feature knowledge graph and the trained expert knowledge base comprises: training the expert knowledge base based on a deep learning network model, learning a characteristic knowledge graph in the periodontal field from an expert system, and establishing the gingivitis evaluation system based on an artificial intelligence reasoning algorithm.
In one embodiment, after the step of training the expert knowledge base based on the deep learning network model, the method further includes: the accuracy of the output results after learning is evaluated based on the clinician's indicia of gingivitis.
A gingivitis evaluation system comprising:
the acquisition module is used for acquiring the forms of a plurality of dentitions and periodontal soft tissues;
the processing module is used for segmenting the dentition and the periodontal soft tissue form into single-tooth data;
the analysis module is used for marking teeth with gingivitis based on the single-tooth data and establishing an expert knowledge base;
the training module is used for training the expert knowledge base;
and the construction module is used for establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base.
A method for evaluating gingivitis using the above gingivitis evaluation system, comprising:
collecting dentition and periodontal soft tissue morphology of a patient;
inputting the dentition and periodontal soft tissue morphology into the gingivitis evaluation system;
the gingivitis evaluation system outputs a gingivitis score of the patient.
An electronic device comprising a memory and a processor, the memory for storing one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement a method of constructing a gingivitis evaluation system as described above.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method of constructing a gingivitis evaluation system as described above.
According to the construction method of the gingivitis evaluation system, the tooth evaluation system and the evaluation method provided by the embodiment of the invention, the dentition and the periodontal soft tissue morphology are divided into single tooth data by acquiring the dentition and the periodontal soft tissue morphology, teeth with gingivitis are labeled based on the single tooth data, and an expert knowledge base is established; training the expert knowledge base, and establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base. The dentition and periodontal soft tissue morphology is more beneficial to realizing the segmentation of teeth, and the three-dimensional morphology of the teeth and the gingiva can be stored while the gingival state information is stored, so that the color information of the periodontal soft tissue can be clearly recorded; and a gingival inflammation score result of a specific tooth position can be obtained by segmenting the teeth.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for constructing a gingivitis evaluation system according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating a method for establishing an expert knowledge base according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a gingivitis evaluation system according to an embodiment of the present invention.
Fig. 4 is a schematic flow chart of a gingivitis evaluation method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The inventor of the application discovers that the gum color is evaluated by a common clinical visual method at present, the visual method compares the color of a reference object with the target color through vision to carry out color comparison so as to obtain the target chromatic value, but no standard and effective soft tissue color card or color plate exists at present, and the influence of ambient light on the visual result is large; the digital photo is also used for measuring the color of soft tissues in the mouth, a digital camera is used for shooting a photo of a required gum area, and corresponding image processing software is used for obtaining the chromatic value of a required site, so that the method is a method for indirectly measuring the gum color, the measuring method is relatively simple and noninvasive and is not limited by the size of a probe of a direct color measuring instrument, however, the information acquired by the digital image is influenced by an operator, a camera brand, a shooting environment, a shooting angle and the like, and the obtained result still has certain deviation; the instrument direct measurement method can also be used for measuring the color of the soft tissues of the periodontal in the mouth, the instrument for directly measuring the color comprises a spectrophotometer, a colorimeter and the like, a probe is placed at a position to be measured, the colorimetric information can be directly obtained, corresponding software is converted into a needed color system for statistical analysis, and the method is widely applied at present. Different studies have found that the colorimetric values of normal gingiva are different, which depends on the equipment used and the working conditions, and the instrument needs to contact soft tissues, so the pressure of the probe on the soft tissues of the oral cavity may reduce the accuracy of the result.
With the further development of digital oral cavity and internet of things information technology, artificial intelligence is gradually applied to various fields of the oral cavity. The digitization technology and the artificial intelligence bring a new idea for the prevention, diagnosis and evaluation of periodontal diseases. Imangaliyev S and the like establish a model for classifying dental plaque of the quantitative light-induced fluorescence picture by using a convolutional neural network algorithm so as to judge the individual periodontal disease risk through the picture, and the F1 score of the model can reach 0.75 +/-0.05. Rana A and the like directly adopt the intra-oral local digital photos as objects to train a convolutional neural network model to judge whether the gum is inflamed or not. Other studies mostly use an imaging plain film or CT as an object to judge whether the established model can accurately judge the absorption degree of the affected teeth and bones affected by periodontitis, and the accuracy rate can reach 70-90%. Zhangran et al have proposed a vision-based periodontal health assessment method. And (3) taking the color photos in the mouth as a training model, training the effective Net neural network and verifying the learning result of the effective Net neural network, and obtaining the severity scores of the tartar and the gingival inflammation. However, the above studies all use a two-dimensional image as a training model, and the two-dimensional image only includes color information and does not include morphological information of periodontal soft tissue, so that the accuracy of the evaluation system is low and a single tooth cannot be evaluated.
Aiming at the problems, the invention is more beneficial to realizing the segmentation of teeth by acquiring the dentition and the periodontal soft tissue form and can clearly record the color information of the periodontal soft tissue; and a gingival inflammation score result of a specific tooth position can be obtained by segmenting the teeth. The specific embodiments are described in the following examples.
The present embodiment provides a method for constructing a gingivitis evaluation system, as shown in fig. 1, the method for constructing the gingivitis evaluation system includes:
Optionally, dentition and periodontal soft tissue morphology is currently acquired by intraoral scanners. The intraoral scanner is developed from initial colorless to present true color scanning, can obtain the true or near-true color of intraoral tissues, is internally provided with a tooth colorimetric system in a scanning system of the intraoral scanner, can perform digital tooth colorimetric while completing intraoral dentition scanning, and can perform three-dimensional storage on palate soft tissues in an intraoral scanning mode instead of a plaster model.
Optionally, the dentition and periodontal soft tissue morphology is textured periodontal soft tissue three-dimensional data.
The plurality of dentition and periodontal soft tissue morphologies include those of healthy humans, and those of gingivitis patients of varying degrees.
optionally, the dentition and the periodontal soft tissue morphology are segmented by taking a single tooth as a unit by using a graph segmentation algorithm, the single tooth data comprises the single tooth and a gum corresponding to the single tooth, and the single tooth data is color three-dimensional data. Specifically, dentition and periodontal soft tissue morphology includes a gingival margin line, and dentition data is extracted using a margin algorithm based on the gingival margin line; and (3) segmenting the dentition and the periodontal soft tissue morphology by using a weak supervision image segmentation algorithm and a convolutional neural network to obtain a single tooth and a gum corresponding to the tooth.
And 03, marking the teeth with gingivitis based on the single-tooth data, and establishing an expert knowledge base. As shown in fig. 2, the specific steps include:
performing gingivitis grade scoring on teeth based on the single-tooth data to form a single-tooth data set, and marking whether each tooth in the data set has gingival inflammation and the severity grade of the gingival inflammation;
and 032, performing data amplification on the data set, and optionally performing data amplification in a scaling, local cutting, rotation mode and other modes to ensure the robustness of the model.
The augmented data set includes training data, validation data, and test data. Alternatively, 3/5 were randomly drawn from the amplified dataset as training models, 1/5 as validation data, and the remainder 1/5 as test data.
And 033, establishing an expert knowledge base based on the data set after data amplification. Optionally, the expert knowledge base is a parameterized expert knowledge base.
And step 04, training the expert knowledge base, and establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base. Specifically, the expert knowledge base is trained based on a deep learning network model, a characteristic knowledge graph intrinsic in the periodontal field is learned from an expert system, and the gingivitis evaluation system is established based on an artificial intelligence reasoning algorithm.
After the step of training the expert knowledge base based on the deep learning network model, the method further comprises the following steps: the accuracy of the output results after learning is evaluated based on the clinician's label for gingivitis. And (3) evaluating the accuracy of the output result after learning by taking the fact that whether the clinician marks the inflammation of the gum as a reference value.
The construction method of the gingivitis evaluation system provided by the application expands periodontal disease examination and detection means, improves the existing periodontal disease diagnosis mode, solves the problem of automatic evaluation of periodontal health, and dynamically monitors periodontal diseases. By collecting dentition and periodontal soft tissue forms, the method is more beneficial to realizing tooth segmentation, and can store the three-dimensional forms of teeth and gingiva while storing gingival state information, thereby clearly recording the color information of periodontal soft tissue; and a gingival inflammation score result of a specific tooth position can be obtained by dividing the tooth.
The present embodiment provides a gingivitis evaluation system 100, as shown in fig. 3, the gingivitis evaluation system 100 includes an obtaining module 10, a processing module 20, an analyzing module 30, a training module 40 and a building module 50.
Wherein:
the acquisition module 10 is used to acquire a plurality of dentitions and periodontal soft tissue morphologies.
The processing module 20 is configured to segment the dentition and the periodontal soft tissue morphology into single-tooth data, wherein the single-tooth data is color three-dimensional data.
The analysis module 30 is used to label teeth with gingivitis based on the single tooth data and establish an expert knowledge base.
The training module 40 is used for training the expert knowledge base.
The construction module 50 is configured to establish the gingivitis evaluation system based on the feature knowledge graph and the trained expert knowledge base.
The acquisition module 10 acquires the forms of a plurality of dentitions and periodontal soft tissues and then sends the forms to the processing module 20; the processing module 20 divides the dentition and periodontal soft tissue morphology into single-tooth data; then, the analysis module 30 labels the teeth with gingivitis based on the single tooth data and establishes an expert knowledge base; the training module 40 trains the expert knowledge base established by the analysis module 30; finally, the construction module 50 builds the gingivitis evaluation system based on the feature knowledge graph and the trained expert knowledge base.
The analysis module 30 is further configured to score gingivitis levels of the teeth based on the single tooth data to form a single tooth data set; performing data amplification on the data set; and establishing an expert knowledge base based on the data set after data amplification.
The construction module 50 is further configured to train the expert knowledge base based on a deep learning network model, learn a characteristic knowledge graph intrinsic in the periodontal field from an expert system, and establish the gingivitis evaluation system based on an artificial intelligence reasoning algorithm.
The gingivitis evaluation system further comprises a flat price module 60 for outputting the precision of the result after the clinician evaluates the learning of the label of gingivitis based on the training of the expert knowledge base based on the deep learning network model.
The present embodiment provides a method for evaluating gingivitis, which uses the gingivitis evaluation system described in the above embodiments to evaluate gingivitis. As shown in fig. 4, the gingivitis evaluation method comprises:
And 003, outputting a gingivitis scoring result of the patient by the gingivitis evaluation system.
The present embodiment provides an electronic device, which may include a memory and a processor, where the memory stores a computer program, and the computer program is executed by the processor to implement the method for constructing the gingivitis evaluation system as described in the above embodiments. It is to be appreciated that the electronic device can also include input/output (I/O) interfaces, as well as communication components.
Wherein the processor is adapted to perform all or part of the steps in the method of constructing the gingivitis evaluation system as in the embodiments. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to execute the method for constructing the gingivitis evaluation system in the above embodiments.
The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The present embodiments also provide a computer-readable storage medium. Each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium.
Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
And the aforementioned storage medium includes: flash memory, hard disk, multimedia card, card type memory (e.g., SD or DX memory, etc.), Random Access Memory (RAM), Static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, server, APP application mall, etc., various media that can store program check codes, on which computer programs are stored, which when executed by a processor can implement the following method steps:
And 03, marking the teeth with gingivitis based on the single-tooth data, and establishing an expert knowledge base.
And step 04, training the expert knowledge base, and establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base.
The specific implementation and the resulting effects can be referred to the above embodiments, and the present invention is not described herein again.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art.
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. All directional indicators in the embodiments of the present application (such as upper, lower, left, right, front, rear, top, bottom … …) are only used to explain the relative positional relationship between the components, the movement, etc. in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Furthermore, reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims. The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and the like that are within the spirit and principle of the present invention are included in the present invention.
Claims (10)
1. A method for constructing a gingivitis evaluation system, comprising:
obtaining a plurality of dentition and periodontal soft tissue morphologies;
segmenting the dentition and periodontal soft tissue morphology into single tooth data;
labeling teeth with gingivitis based on the single tooth data, and establishing an expert knowledge base;
training the expert knowledge base, and establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base.
2. The method of constructing a gingivitis evaluation system of claim 1, wherein the step of labeling teeth with gingivitis based on the single tooth data and establishing an expert knowledge base comprises:
scoring teeth for gingivitis grade based on the single tooth data and forming a data set, wherein the single tooth data is color three-dimensional data;
performing data amplification on the data set;
and establishing an expert knowledge base based on the data set after data amplification.
3. The method of constructing a gingivitis evaluation system according to claim 2, wherein the method of data expansion of the data set comprises: at least one of scaling, local cropping and rotating is adopted.
4. The method of constructing a gingivitis evaluation system according to claim 2, wherein the augmented data set comprises training data, verification data and test data.
5. The method for constructing a gingivitis evaluation system according to claim 1, wherein the training of the expert knowledge base and the establishing of the gingivitis evaluation system based on the feature knowledge graph and the trained expert knowledge base comprises: training the expert knowledge base based on a deep learning network model, learning a characteristic knowledge graph in the periodontal field from an expert system, and establishing the gingivitis evaluation system based on an artificial intelligence reasoning algorithm.
6. The method for constructing gingivitis evaluation system according to claim 1, wherein the training the expert knowledge base based on the deep learning network model further comprises: the accuracy of the output results after learning is evaluated based on the clinician's indicia of gingivitis.
7. A gingivitis evaluation system comprising:
the acquisition module is used for acquiring the forms of a plurality of dentitions and periodontal soft tissues;
the processing module is used for segmenting the dentition and the periodontal soft tissue form into single-tooth data;
the analysis module is used for marking teeth with gingivitis based on the single-tooth data and establishing an expert knowledge base;
the training module is used for training the expert knowledge base;
and the construction module is used for establishing the gingivitis evaluation system based on the characteristic knowledge graph and the trained expert knowledge base.
8. A method of evaluating gingivitis using the gingivitis evaluation system of claim 7, comprising:
collecting dentition and periodontal soft tissue morphology of a patient;
inputting the dentition and periodontal soft tissue morphology into the gingivitis evaluation system;
the gingivitis evaluation system outputs a gingivitis score of the patient.
9. An electronic device comprising a memory and a processor, the memory for storing one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement a method of constructing a gingivitis evaluation system according to any of claims 1-6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, is adapted to carry out a method of constructing a gingivitis evaluation system according to any one of claims 1-6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170146532A1 (en) * | 2014-06-05 | 2017-05-25 | Colgate-Palmolive Company | Assay for Oral Inflammation |
| CN106875386A (en) * | 2017-02-13 | 2017-06-20 | 苏州江奥光电科技有限公司 | A kind of method for carrying out dental health detection automatically using deep learning |
| US20170172418A1 (en) * | 2015-12-01 | 2017-06-22 | University Of South Florida | Standardized oral health assessment and scoring using digital imaging |
| CN107368679A (en) * | 2017-07-13 | 2017-11-21 | 无限极（中国）有限公司 | A kind of oral health assessment algorithm, apparatus and system |
| CN107909630A (en) * | 2017-11-06 | 2018-04-13 | 南京齿贝犀科技有限公司 | A kind of tooth bitmap generation method |
| CN111563887A (en) * | 2020-04-30 | 2020-08-21 | 北京航空航天大学杭州创新研究院 | Intelligent analysis method and device for oral cavity image |
| CN112638312A (en) * | 2018-09-04 | 2021-04-09 | 普罗马顿控股有限责任公司 | Automated orthodontic treatment planning using deep learning |
| CN113178239A (en) * | 2021-05-10 | 2021-07-27 | 赣南医学院第一附属医院 | Method and system for guiding daily health care of patients with gingival atrophy |
| CN113223010A (en) * | 2021-04-22 | 2021-08-06 | 北京大学口腔医学院 | Method and system for fully automatically segmenting multiple tissues of oral cavity image |
| CN113436734A (en) * | 2020-03-23 | 2021-09-24 | 北京好啦科技有限公司 | Tooth health assessment method and device based on face structure positioning and storage medium |
-
2022
- 2022-01-25 CN CN202210087487.6A patent/CN114496254A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170146532A1 (en) * | 2014-06-05 | 2017-05-25 | Colgate-Palmolive Company | Assay for Oral Inflammation |
| US20170172418A1 (en) * | 2015-12-01 | 2017-06-22 | University Of South Florida | Standardized oral health assessment and scoring using digital imaging |
| CN106875386A (en) * | 2017-02-13 | 2017-06-20 | 苏州江奥光电科技有限公司 | A kind of method for carrying out dental health detection automatically using deep learning |
| CN107368679A (en) * | 2017-07-13 | 2017-11-21 | 无限极（中国）有限公司 | A kind of oral health assessment algorithm, apparatus and system |
| CN107909630A (en) * | 2017-11-06 | 2018-04-13 | 南京齿贝犀科技有限公司 | A kind of tooth bitmap generation method |
| CN112638312A (en) * | 2018-09-04 | 2021-04-09 | 普罗马顿控股有限责任公司 | Automated orthodontic treatment planning using deep learning |
| US20210322136A1 (en) * | 2018-09-04 | 2021-10-21 | Promaton Holding B.V. | Automated orthodontic treatment planning using deep learning |
| CN113436734A (en) * | 2020-03-23 | 2021-09-24 | 北京好啦科技有限公司 | Tooth health assessment method and device based on face structure positioning and storage medium |
| CN111563887A (en) * | 2020-04-30 | 2020-08-21 | 北京航空航天大学杭州创新研究院 | Intelligent analysis method and device for oral cavity image |
| CN113223010A (en) * | 2021-04-22 | 2021-08-06 | 北京大学口腔医学院 | Method and system for fully automatically segmenting multiple tissues of oral cavity image |
| CN113178239A (en) * | 2021-05-10 | 2021-07-27 | 赣南医学院第一附属医院 | Method and system for guiding daily health care of patients with gingival atrophy |
Non-Patent Citations (1)
| Title |
|---|
| 王勇, 天津：天津科学技术出版社 * |
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