KR20210048756A - Teeth setup method for orthodontic treatment and orthodontic setup apparatus therefor - Google Patents

Abstract

Disclosed are an orthodontic design method and a device therefor. According to one embodiment of the present invention, the orthodontic design method comprises the steps of: obtaining a measurement value for each diagnostic item through analysis of clinical data of a patient; classifying a patient type using the obtained measurement value for each diagnostic item; and automatically setting teeth in accordance with the classified patient type. Accordingly, diagnosis and analysis of the clinical data of the patient, the classification of the patient type in accordance with the analysis, and a setup process in accordance with the classified patient type can be continuously performed through a single program.

Description

Orthodontic design method and apparatus therefor {Teeth setup method for orthodontic treatment and orthodontic setup apparatus therefor}

The present invention relates to an image processing technology, and more particularly, to a digital orthodontic diagnosis and treatment plan establishment technology.

A condition in which the teeth are not right and the upper and lower teeth are abnormal is called malocclusion. Such malocclusion may cause functional problems such as mastication and pronunciation problems and aesthetic problems for the face, as well as health problems such as tooth decay and gum disease. Therefore, orthodontic treatment must be performed to make this malocclusion a normal bite. In order to determine a suitable treatment method, a setup work is required to create virtual setup data so that teeth are arranged in an ideal shape by establishing an orthodontic diagnosis and treatment plan from clinical data before performing orthodontic treatment. This process is done through the Computer Aided Design (CAD) program.

When setting up a tooth using a dental CAD program, a diagnosis process using the patient's clinical data should be preceded. In the current orthodontic treatment, analysis is performed using the patient's diagnostic data through a separate diagnostic program. Then, the patient's treatment plan is established by taking the analysis result value. After that, the teeth are set up manually as planned. This procedure has to be analyzed and calculated anew each time, so there is a hassle in the procedure. Therefore, it has a problem that requires a lot of time, effort, and user input work.

According to an embodiment, we propose a correction design method and apparatus for increasing user convenience by minimizing user manipulation when setting up data for orthodontic treatment, and allowing users to quickly apply data analysis to patient clinical data. .

The orthodontic design method according to an embodiment includes the steps of classifying diagnosis items according to facial structure, skeletal structure, and tooth structure, and obtaining clinical data of a patient in a state in which at least one of measurement points and connection lines is set for each classified diagnosis item. And obtaining a measurement value by measuring each diagnosis item with reference to at least one of the set measurement points and connection lines; classifying the patient type using the measured value for each obtained diagnosis item; and And automatically setting up the teeth accordingly.

The clinical data includes at least one of the patient's facial scan data, head radiograph, frontal radiograph, tooth model data, CT data, and panoramic data, or includes registration data obtained by matching at least two of them. In the step of acquiring, the facial hair measurement value is obtained through facial analysis using the patient's clinical data, the skeletal measurement value is obtained through the skeletal analysis using the patient's clinical data, and the tooth Measured values can be obtained.

The step of classifying the patient type is classification of the patient type using the facial hair measurement values obtained through facial analysis.The upper lip to E-line, the nasiolabial angle, and the lower lip are classified. Lip to E-line), each patient type may be classified from each measurement value of at least one.

The step of classifying the patient type is classification of the patient type using the skeletal measurement values obtained through skeletal analysis, and each of the measured values obtained from the vertical relation elements of the skeleton, the skeletal-tooth relation elements, and the vertical-horizontal relation elements of the skeleton. Classify patient types. The vertical relationship element of the skeleton includes at least one of FMA (Frankfort Mandibular plane Angle) (°), SN-MP (°), and PFH (Posterior Facial Height) / AFH (Anterior Facial Height) (%), and skeletal-tooth The relationship element includes at least one of Incisor Mandibular Plane Angle (IMPA) (°), Frankfort Mandibular Incisor Angle (FMIA) (°), and L1 to A-Pog (mm), and the vertical-horizontal relationship element of the skeleton is EI ( Extraction Index), and the EI (Extraction Index) includes an overbite depth indicator (ODI), an anteroposterior dysplasia indicator (APDI), an interincisor angle (LLA), It may be a value measured using the upper lip to E-line (mm) and the lower lip to E-line (mm).

In the step of classifying the patient type, a patient type is classified using a tooth measurement value obtained through tooth analysis, and a patient type may be classified from a measurement value obtained from an intramaxillary element and a measurement value obtained from an intermaxillary relationship element. The measured values obtained from the intramaxillary elements are the maxillary arch length discrepancy (Mx ALD) (mm), the mandibular arch length discrepancy (Mn) and the mandibular arch length discrepancy (Mn). ALD) (mm) and at least one of the Curve of Spee (mm), and the measured value obtained from the intermaxillary relationship element is the Bolton excess in Mx (mm) (mm ), the mandible excess in Mn (mm) according to the Bolton ratio, and an Irregularity Index (mm) of the mandibular anterior teeth.

In the step of classifying patient types, when a plurality of patient types are mixed, the main type may be determined according to a preset criterion.

The steps of automatically setting up teeth include generating digital setup data for each categorized patient type, and performing setup to adjust to a normal range when the measured value exceeds the normal range in each diagnostic item. Providing a result, but when the patient type is an extraction type, providing a setup result of automatically performing tooth extraction, and correcting the setup result according to a user manipulation input.

The orthodontic design method performs registration between two data constituting clinical data, separates individual teeth from at least one of the data on which the registration has been performed or two data constituting the clinical data, and provides feature information for the separated individual teeth. The extraction may further include a preparation step of extracting reference information of at least one of the two data constituting the clinical data.

The orthodontic design apparatus according to another embodiment classifies diagnosis items according to facial structure, skeletal structure, and tooth structure, and acquires clinical data of a patient in a state in which at least one of a measurement point and a connection line is set for each classified diagnosis item, A data analysis unit that obtains measurement values by measuring each diagnosis item by referring to at least one of the set measurement points and connection lines, and classifies the patient type by using the obtained measurement value for each diagnosis item, and the tooth according to the classified patient type. It includes a data setup unit for automatically setting up.

The data analysis unit includes a facial analysis unit that acquires facial hair measurement values through facial structure analysis using the patient's clinical data, a skeletal analysis unit that acquires skeletal measurement values through skeletal structure analysis using the patient’s clinical data, and A tooth analysis unit that acquires tooth measurement values through tooth structure analysis using clinical data, and patients who classify patient types by diagnosis items into extraction type, non-extraction type, and mixed type according to facial structure, skeletal structure, and tooth structure analysis. It may include a type classification unit.

The patient type classification unit classifies patient types using facial hair measurement values obtained through facial analysis, and includes upper lip to E-line, nasiolabial angle, and lower lip to E-line. line), each patient type may be classified from each measurement value.

The patient type classification unit classifies patient types using skeletal measurement values obtained through skeletal analysis, and classifies each patient type from measurement values obtained from each of the skeletal vertical relationship element, skeletal-tooth relationship element, and skeletal vertical-horizontal relationship element. can do.

The patient type classification unit is a patient type classification using a tooth measurement value obtained through tooth analysis, and may classify a patient type from a measurement value obtained from an intra-jaw element and a measurement value obtained from an inter-jaw relationship element.

The data setup unit generates digital setup data for each categorized patient type, and if the measured value in each diagnostic item exceeds the normal range, it performs setup to adjust it to the normal range value and provides the setup result. In the case of extraction type, it provides the setup result of automatically performing tooth extraction, and the setup result can be corrected according to the user's manipulation input.

According to an orthodontic design method and apparatus thereof according to an embodiment, as long as a single orthodontic design program receives clinical data of a patient, diagnosis, analysis, treatment planning, and setup are continuously executed to reduce the hassle of procedures, Various analysis methods can be quickly applied to patient data by users.

For example, diagnosis and analysis of clinical data of patients with orthodontic treatment, patient type classification according to analysis, and setup process according to the classified patient type are continuously performed automatically through a single program, minimizing the user's effort and It saves time and enables quick and easy calibration. At this time, it is possible to obtain a measurement value for each diagnosis item through analysis of the patient's clinical data, and automatically classify the patient type using the obtained measurement value for each diagnosis item. In addition, by automatically presenting not only the patient type result but also the setup data for each patient type, the user's convenience can be increased, and the degree of freedom of treatment can be increased through the user's free set-up modification.

Furthermore, after newly combining the diagnostic items to be analyzed and redefining the measurement points and connection lines for each diagnostic item, refer to at least one of the redefined measurement points and connection lines, and ), it is possible to classify patient types more accurately and objectively.

1 is a view showing the configuration of a calibration design device according to an embodiment of the present invention,
2 is a diagram showing a detailed configuration of the control unit of FIG. 1 according to an embodiment of the present invention;
3 is a diagram showing a detailed configuration of the data processing unit of FIG. 2 according to an embodiment of the present invention;
4 is a diagram showing a detailed configuration of the data analysis unit of FIG. 2 according to an embodiment of the present invention;
5 is a diagram showing the flow of a calibration design method according to an embodiment of the present invention;
6 and 7 are diagrams showing measurement points and connection lines for each diagnosis item according to an embodiment of the present invention;
8 is a diagram showing an extraction polygon chart according to measurement values for each diagnosis item according to an embodiment of the present invention;
9 is a diagram illustrating an example of arranging clinical data as digital setup data according to a patient type according to an embodiment of the present invention;
10 is a diagram showing an example of classifying patient types using an Upper Lip to E-line (mm) measurement value according to an embodiment of the present invention;
11 is a diagram showing an example of classifying patient types using a Nasiolabial Angle (°) measurement value according to an embodiment of the present invention;
12 is a diagram showing an example of classifying patient types using a measurement value of Lower Lip to E-line (mm) according to an embodiment of the present invention;
13 is a diagram showing an example of classifying patient types using a Frankfort Mandibular plane Angle (FMA) (°) measurement value according to an embodiment of the present invention;
14 is a diagram showing an example of classifying patient types using SN-MP (°) measurement values according to an embodiment of the present invention;
15 is a diagram showing an example of classifying patient types using a measurement value of a posterior facial height (PFH) / anterior facial height (AFH) (%) according to an embodiment of the present invention;
16 is a diagram illustrating an example of classifying patient types using an Incisor Mandibular Plane Angle (IMPA) (°) measurement value according to an embodiment of the present invention;
17 is a diagram showing an example of patient type classification using a Frankfort Mandibular Incisor Angle (FMIA) (°) measurement value according to an embodiment of the present invention;
18 is a diagram showing an example of classifying patient types using L1 to A-Pog (mm) measurement values according to an embodiment of the present invention;
19 to 22 are diagrams showing an example of classifying patient types using an extraction index (EI) measurement value according to an embodiment of the present invention;
FIG. 23 is a diagram illustrating an example of classifying patient types using Mx ALD (Maxillary Arch Length Discrepancy) (mm) measurement values according to an embodiment of the present invention;
24 and 25 are views showing an example of classifying a patient type using a Bolton (excess in Mx) (mm) measurement value according to an embodiment of the present invention;
26 is a diagram illustrating an example of classifying patient types using a measurement value of Mn ALD (Mandibular Arch Length Discrepancy) (mm) according to an embodiment of the present invention;
27 and 28 are views showing an example of classifying a patient type using a measured value of Bolton (excess in Mn) (mm) according to an embodiment of the present invention;
29 is a diagram illustrating an example of classifying patient types using a measurement value of Curve of Spee (mm) according to an embodiment of the present invention;
30 and 31 are diagrams illustrating an example of classifying patient types using an Irregularity Index (mm) measurement value according to an embodiment of the present invention.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments make the disclosure of the present invention complete, and the general knowledge in the technical field to which the present invention pertains. It is provided to completely inform the scope of the invention to the possessor, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same elements throughout the specification.

In describing the embodiments of the present invention, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted, and terms to be described later are in the embodiment of the present invention. These terms are defined in consideration of the functions of the user and may vary according to the intention or custom of users or operators. Therefore, the definition should be made based on the contents throughout the present specification.

Combinations of each block of the attached block diagram and each step of the flowchart may be executed by computer program instructions (execution engine), and these computer program instructions are used on a processor of a general-purpose computer, special purpose computer, or other programmable data processing device. As can be mounted, the instructions executed by the processor of a computer or other programmable data processing device generate means for performing the functions described in each block of the block diagram or each step of the flowchart.

These computer program instructions may also be stored in a computer-usable or computer-readable memory that can be directed to a computer or other programmable data processing device to implement a function in a particular manner, so that the computer-usable or computer-readable memory It is also possible to produce an article of manufacture in which the instructions stored in the block diagram contain instruction means for performing the functions described in each block of the block diagram or each step of the flowchart.

In addition, since computer program instructions can be mounted on a computer or other programmable data processing device, a series of operation steps are performed on a computer or other programmable data processing device to create a computer-executable process. It is also possible that the instructions for performing the data processing apparatus provide steps for executing the functions described in each block in the block diagram and in each step in the flowchart.

In addition, each block or each step may represent a module, segment, or part of code containing one or more executable instructions for executing specified logical functions, and in some alternative embodiments mentioned in the blocks or steps. It should be noted that it is also possible for functions to occur out of order. For example, two blocks or steps shown in succession may in fact be performed substantially simultaneously, and the blocks or steps may be performed in the reverse order of a corresponding function as necessary.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the embodiments of the present invention exemplified below may be modified in various other forms, and the scope of the present invention is not limited to the embodiments described below. Embodiments of the present invention are provided to more completely describe the present invention to those of ordinary skill in the art to which the present invention pertains.

1 is a diagram showing the configuration of a calibration design apparatus according to an embodiment of the present invention.

Referring to FIG. 1, the orthodontic design apparatus 1 performs a dental CAD process to help correct teeth in an actual dentistry. The dental CAD process for orthodontics acquires patient clinical data, establishes a treatment plan through diagnosis and analysis using clinical data through control by a computer program, and produces virtual tooth setup data according to the established treatment plan. It means a series of processes. In this embodiment, as long as a single calibration design program receives patient clinical data, diagnosis, analysis, treatment planning, and setup processes are continuously executed to reduce the hassle of procedures, and users can quickly use various analysis methods. So that it can be grafted into.

The orthodontic design apparatus 1 according to an exemplary embodiment may include an electronic device capable of executing an orthodontic design program as a dental CAD program, and a server communicating with the electronic device through a network. Electronic devices include computers, notebook computers, laptop computers, tablet PCs, smartphones, mobile phones, personal media players (PMPs), personal digital assistants (PDAs), and the like.

Hereinafter, a configuration of the calibration design apparatus 1 having the above-described characteristics will be described later with reference to FIG. 1.

Referring to FIG. 1, a calibration design apparatus 1 according to an exemplary embodiment includes a data acquisition unit 10, a storage unit 12, a control unit 14, an input unit 16, and an output unit 18.

The data acquisition unit 10 acquires clinical data from orthodontic treatment patients. Clinical data required for orthodontic treatment includes tooth model data, CT data, panoramic data, face scan data, cephalometric X-ray data, and frontal radiograph (PA X-ray) data. have.

Here, the tooth model data refers to data representing the appearance of teeth such as oral scan data and tooth model scan data, and in this description, oral scan data will be described as an example.

The storage unit 12 stores various types of data such as information necessary for performing the operation of the calibration design apparatus 1 and information generated according to the operation. The storage unit 12 may provide data to the control unit 14 for data analysis by the control unit 14.

The control unit 14 controls each component while continuously performing diagnosis and analysis of clinical data of orthodontic patients through control by a computer program, classification of patient types according to analysis, and tooth setup process according to the classified patient types. . The tooth setup process refers to a process of generating virtual digital setup data by arranging clinical data of a patient out of the reference range into a normal range. In general, the clinical data diagnosis process, analysis process, and tooth setup process are each performed through separate programs, but the control unit 14 according to an embodiment continuously performs a series of diagnosis, analysis, and setup processes through a single program. do. At this time, the control unit 14 obtains the measured value for each diagnosis item through analysis of the patient's clinical data, and automatically classifies the patient type using the measured value for each diagnosis item. And it automatically sets up teeth according to the categorized patient types. Accordingly, not only patient type classification but also tooth setup results for each patient type can be automatically presented through a program. The control unit 14 configures screen information displayed on the screen through the output unit 18 and performs a simulation of arranging virtual digital setup data in clinical data.

The output unit 18 displays a screen including clinical data, patient type classification results generated through the control unit 14, digital setup data, and the like.

The input unit 16 receives a user manipulation signal. For example, through the output unit 18, an operation signal for user modification such as movement and rotation of digital setup data displayed on the screen is input. The input unit 16 may receive a user manipulation signal for adding, deleting, or changing a diagnosis item for classifying a patient type. In addition, a reference value for classifying patient types for each diagnosis item can be input from the user.

2 is a diagram showing a detailed configuration of the control unit of FIG. 1 according to an embodiment of the present invention.

1 and 2, the control unit 14 includes a data processing unit 140, a data analysis unit 142, and a data setup unit 144.

The data processing unit 140 prepares data for analyzing clinical data of a patient. Data preparation may include data matching, individual tooth separation, feature information extraction, and reference information extraction. The detailed configuration of the data processing unit 140 will be described later with reference to FIG. 3.

The data analysis unit 142 obtains a measurement value for each diagnosis item through clinical data analysis, and classifies the patient type using the obtained measurement value for each diagnosis item. The data analysis unit 142 according to an embodiment classifies diagnosis items according to facial structure, skeletal structure, and tooth structure, and acquires clinical data of a patient in a state in which at least one of measurement points and connection lines is set for each classified diagnosis item. And, by referring to at least one of the set measurement points and connection lines, each diagnostic item is measured to obtain a measurement value. Then, the patient type is classified using the measured values for each diagnosis item obtained. For example, after setting a reference value for determining the patient type, the measured value for each diagnosis item and the set reference value are compared to classify the patient type by diagnosis item. The detailed configuration of the data analysis unit 142 will be described later with reference to FIG. 4.

The data setup unit 144 automatically sets up teeth according to the patient types classified by the data analysis unit 142. The data setup unit 144 according to an embodiment generates digital setup data for each classified patient type, and performs setup for adjusting to a normal range value when the measured value exceeds the normal range in each diagnostic item. Provides setup results. If the patient type is an extraction type, it is possible to provide the result of the setup of automatically performing tooth extraction. Furthermore, the setup result may be corrected according to the user manipulation input.

3 is a diagram illustrating a detailed configuration of the data processing unit of FIG. 2 according to an embodiment of the present invention.

1 to 3, the data processing unit 140 includes a data matching unit 1400, a tooth separation unit 1402, a feature extraction unit 1404, and a reference extraction unit 1406.

The data matching unit 1400 matches between two data constituting clinical data. Matching is to match the location information of two data. The two data may be frontal radiograph data and CT data, head radiograph data and CT data, oral scan data and CT data, respectively.

The tooth separation unit 1402 separates individual teeth from at least one of data on which registration has been performed or two data constituting clinical data. Separation of individual teeth can be performed using a tooth number.

The feature extracting unit 1404 may extract feature information for the individual teeth separated by the tooth separation unit 1402 and correct it. Feature information includes, for example, tooth axis, occlusal point, tooth direction, contact point with surrounding teeth, FACC (Facial Axis of the Clinical Crown), FA point, and the like. FA points are the focus of FACC.

The reference extraction unit 1406 extracts and corrects at least one reference information of two data constituting clinical data. For example, the reference extraction unit 1406 may extract and correct reference information including an upper and lower tooth model and a face, a midsagittal line on a skeleton, an occlusal plane, and the like.

4 is a diagram showing a detailed configuration of the data analysis unit of FIG. 2 according to an embodiment of the present invention.

1, 2 and 4, the data analysis unit 142 includes a facial hair analysis unit 1420, a skeleton analysis unit 1422, a tooth analysis unit 1424, and a patient type classification unit 1426. .

The facial hair analysis unit 1420 acquires a facial hair measurement value through facial structure analysis using clinical data of a patient, for example, facial hair scan data. The skeleton analysis unit 1422 obtains a skeletal measurement value through analysis of a skeleton structure using clinical data of a patient, for example, a head radiograph and a front radiograph. The tooth analysis unit 1424 obtains a tooth measurement value through analysis of a tooth structure using clinical data of a patient, for example, oral scan data.

The patient type classification unit 1426 classifies patient types for each diagnosis item into extraction, non-extraction, and borderline types according to facial structure, skeletal structure, and tooth structure analysis. The borderline type is a type located at the boundary between extraction and non-extraction.

The patient type classification unit 1426 according to an embodiment classifies the extraction and non-extraction types from measured values obtained from the protrusion of the upper lip, the protrusion of the nasolabial leg, and the protrusion of the lower lip in the facial structure. An embodiment of this will be described later with reference to FIGS. 10 to 12.

The patient type classification unit 1426 according to an embodiment classifies the extraction and non-extraction types from the measured values obtained from the vertical relationship element of the skeletal structure, and classifies the extraction and non-extraction types from the measurement values obtained from the skeletal-tooth relationship element. can do. In addition, extraction and non-extraction types can be classified from the measurement values obtained from the vertical-horizontal relationship elements of the skeleton, including the vertical relationship element of the skeleton structure, the horizontal relationship element of the skeleton, the angle element between the incisors, and the protrusion of the upper and lower lip. I can. An embodiment of this will be described later with reference to FIGS. 13 to 22.

The patient type classification unit 1426 according to an embodiment may classify extraction and non-extraction types from measurement values obtained from intra-maxillary elements including an occlusal plane element and an incongruity element between the tooth length of the upper and lower jaw and the length of the dentition arch. In addition, extraction and non-extraction types can be classified from the measured values obtained from the intermaxillary relation elements including the maxillary and mandibular excess element and the mandibular anterior tooth arrangement relation element according to the Bolton ratio. An embodiment of this will be described later with reference to FIGS. 23 to 31.

5 is a diagram illustrating a flow of a calibration design method according to an embodiment of the present invention.

Referring to FIG. 5, the orthodontic design apparatus acquires clinical data of a patient and prepares for a correction design (S510). The patient's clinical data includes oral scan data, CT data, panoramic data, face scan data, cephalometric X-ray data, and frontal radiograph (PA X-ray) data.

In the preparation step S510, the calibration design apparatus may perform matching between two data constituting clinical data. Matching is to match the location information of two data. The two data may be frontal radiograph data and CT data, head radiograph data and CT data, oral scan data and CT data, respectively. Individual teeth can be separated from at least one of the data on which the registration has been performed or the two data constituting the clinical data, and feature information is extracted for the separated individual teeth, and this can be corrected. Separation of individual teeth can be performed using a tooth number. Feature information includes, for example, tooth axis, occlusal point, tooth direction, contact point with surrounding teeth, FACC (Facial Axis of the Clinical Crown), FA point, and the like. FA points are the focus of FACC. In addition, the orthodontic design apparatus may extract reference information including a maxillary and mandibular tooth model and a face, a midsagittal line on a skeleton, an occlusal plane, and the like, and correct them.

Subsequently, the calibration design apparatus acquires a measurement value for each diagnosis item through clinical data analysis (S520). For clinical data analysis, data in the form of matching patient's oral scan data, facial hair scan data, head radiograph data, frontal radiograph data, and CT data can be provided as a basic screen.

In the step of acquiring measurement values for each diagnosis item (S520), the orthodontic design apparatus may largely classify the diagnosis items into faces, skeletons, and teeth, and may further classify them into faces, skeletons, and teeth. Subsequently, measurement points and connection lines are set for each classified diagnosis item. For example, measurement points and connection lines for each diagnosis item are set from feature information of individual teeth extracted in the preparation step (S510), phase information of each data, and reference information. An example of setting measurement points and connection lines for each diagnosis item will be described later with reference to FIGS. 6 and 7. In a state in which all the measurement points and connection lines for each diagnostic item for measurement are set in advance, the calibration design apparatus obtains measurement values by measuring each diagnostic item using at least one of the set measurement points and connection lines.

Subsequently, the calibration design apparatus classifies the patient type by using the measured value for each diagnosis item (S530). Patient types can be divided into Extraction, Non-Extraction and Borderline. Borderline is a type located at the boundary between extraction and non-extraction. At this time, the calibration design apparatus sets a reference value for determining the type for each diagnosis item, compares the measured value for each diagnosis item with the set reference value, and classifies the patient type for each diagnosis item. For example, if the diagnostic item is UL to E-line (mm), if the measured value is greater than the reference value 1, it is an extraction type, and if it is less than the reference value -3, it is a non-extraction type. If the reference value is between -3 and 1, it is classified as a borderline type. An example of classifying patient types according to measured values will be described later with reference to FIG. 8.

In the patient type classification step (S530), the orthodontic design apparatus may classify the patient type using measurement values for each detailed item of face, skeleton, and teeth.

Classification of patient types using facial hair measurement values obtained through facial analysis. For example, the orthodontic design device is 1-1. Upper Lip to E-line (mm), 1-2. Nasiolabial Angle (°), 1-3. Lower Lip to E-line (mm), and classify extraction and non-extraction types from the measured values obtained from each. It will be described later with reference to 10 and 11.

Classification of patient types using skeletal measurement values obtained through skeletal analysis.For example, the orthodontic design device is used to extract extractions and ratios from measurement values obtained from each of the skeletal vertical relation element, skeletal-tooth relation element, and vertical-horizontal relation element Classify the type of extraction. The vertical relationship element of the skeleton is 2-1. Frankfort Mandibular plane Angle (FMA) (°), 2-2. SN-MP (°), 2-3. Includes PFH (Posterior Facial Height) / AFH (Anterior Facial Height) (%). The skeletal-tooth relationship element is 2-4. IMPA (Incisor Mandibular Plane Angle) (°), 2-5. FMIA (Frankfort Mandibular Incisor Angle) (°), 2-6. Includes L1 to A-Pog (mm). The vertical-horizontal relationship element of the skeleton is 2-7. Includes EI (Extraction Index). 2-7. EI (Extraction Index) is the vertical relation element ODI (Overbite Depth Indicator) of the skeleton, the horizontal relation element APDI (Anteroposterior Dysplasia Indicator) of the skeleton, the inter-incisor angle element LLA (Interincisor Angle), and the upper lip to E- It is measured using line) (mm) and Lower Lip to E-line (mm). An example of classifying patient types using skeletal measurement values will be described later with reference to FIGS. 12 to 22.

Classification of patient types using tooth measurement values obtained through tooth analysis For example, the orthodontic design apparatus classifies extraction and non-extraction types from measurement values obtained from each of the intra-jaw element and the inter-jaw relationship element. The element within evil is 3-1. Incongruity between maxillary tooth length and dentition arch length Mx ALD (Maxillary Arch Length Discrepancy) (mm), 3-3. Mandibular arch length discrepancy factor Mn Mandibular Arch Length Discrepancy (ALD) (mm), 3-5. It may include the occlusal plane element Curve of Spee (mm). The element of the relationship between evil is 3-2. Bolton (excess in Mx) (mm), 3-4. Bolton (excess in Mn) (mm), 3-6. It may include an Irregularity Index (mm) of an arrangement-related element of the mandibular anterior teeth. An example of classifying patient types using tooth measurement values will be described later with reference to FIGS. 23 to 31.

In the patient type classification step (S530), when a plurality of patient types (extraction/non-extraction/mixed types) are mixed as a result of the patient type classification, the orthodontic design device determines the main case according to a preset criterion. I can. For example, you can decide the type of patient with the most occurrence as the main type, and present the rest of the types together. Alternatively, the weights for each diagnosis item can be set in advance, and the patient types of the corresponding diagnosis items can be presented in the order of the highest weight.

Subsequently, the orthodontic design apparatus automatically sets up the teeth according to the classified patient types (S540). The calibration design device automatically presents the setup for each type when there is a measurement value corresponding to the extraction/non-extraction/mixing type for each diagnostic item. When the three types are mixed, the patient type with the highest weight or the patient type with the highest weight is classified as the main case and the patient type is presented, and the set-up result is determined by extraction and ratio for each diagnosis item. All patient types, including tooth extraction, can be presented. The orthodontic design device automatically arranges the teeth when it is determined whether or not the object to be extracted or non-extracted is determined through the patient type classification. At this time, if there is an item in which the measured value in each diagnostic item through analysis exceeds the standard range, it is adjusted to the normal range value. For example, if the Mx ALD (maxillary space excess or deficiency) value is -5.6mm among the patient's diagnostic items, the teeth are automatically arranged to return this value to the reference value of 0. Tooth arrangement is performed to return the result value of the problem item (exceeding the reference range) to the reference value among the analysis values. In addition, in the case of the extraction type according to the patient type classification, the setup result of automatically performing tooth extraction is presented to the user. The automatically presented setup results can be modified by the user. For example, it is possible to modify the setup result by vomiting movement and rotation of individual teeth and movement and rotation of group teeth.

6 and 7 are diagrams illustrating measurement points and connection lines for each diagnosis item according to an embodiment of the present invention.

Referring to FIGS. 6 and 7, the calibration design apparatus newly combines diagnosis items with diagnosis items to be analyzed, and redefines measurement points and connection lines for each diagnosis item. For example, the diagnostic items are largely classified into facial structure, skeletal structure, and dental structure. The facial structure can be divided into three categories: symmetric relationship from the spermatozoa and soft tissue from the side hair. The skeletal structure can be classified into three categories: symmetric relationship from the regular hair, vertical relationship from the side hair, horizontal relationship, vertical-horizontal relationship, and skeleton-tooth relationship. The tooth structure can be further classified into three types of intramaxillary and intermaxillary relationships. The facial structure can be analyzed using the patient's facial scan data, the skeletal structure can be analyzed using the patient's head radiograph and frontal radiograph data, and the tooth structure including the intramaxillary and intermaxillary relationships is the patient's It can be analyzed using the oral scan data of. Note in FIG. 6 is the name of the comprehensive analysis method or the name of the proposer including the analysis method of each diagnostic item. Diagnosis items classified with reference to FIGS. 6 and 7 can be added, deleted, or changed according to the user's preference.

8 is a diagram illustrating an extraction polygon chart according to measurement values for each diagnosis item according to an embodiment of the present invention.

Referring to FIG. 8, patient types can be classified into extraction, non-extraction, and borderline according to measurement values for each diagnosis item. This graph is shown in the extraction polygonal chart of FIG. 8. For example, the diagnosis item is 1-1. For UL to E-line (mm), if the measured value is greater than the reference value 1, it is an extraction type, and if it is less than the reference value -3, it is a non-extraction type, and between the reference values -3 and 1. This is a Borderline type. Referring to FIG. 8, a reference value for classifying patient types by diagnosis items can be modified by a user and may be changed according to future updates.

9 is a diagram illustrating an example of arranging clinical data as digital setup data according to a patient type according to an embodiment of the present invention.

Referring to FIG. 9, if there is an item in which the measured value in each diagnostic item through analysis exceeds the reference range, digital setup data is generated by adjusting it to a normal range value. For example, if the ALD Mx value is -6.2mm among the diagnostic items for the patient's clinical data (left), this value is returned to the reference value of 0, and if the ALD Mn value is -4.3mm, this value is used as the reference. Digital setup data (right) is generated by automatically performing the tooth arrangement to return to the value 0.

10 is a diagram illustrating an example of classifying a patient type using an Upper Lip to E-line (mm) measurement value according to an embodiment of the present invention.

Referring to Figure 10, the prosthetic design device is 1-1. Based on the measurement value of Upper Lip to E-line (mm), patient types are classified into Extraction, Non-Extraction, and Borderline. 1-1. Upper Lip to E-line (mm) represents the protrusion of the upper lip based on the Pn(Pronasale)-Pog'(Soft tissue Pogonion) connection line for the side hair of the patient's facial hair. Pn (Pronasale) is the most protruding part of the tip of the nose, and Pog' (Soft tissue Pogonion) is the most anterior point of the jawbone.

1.1 If the measured value for UL to E-line (mm) is greater than 1, it is an extraction type, if it is less than -3, it is a non-extraction type, and if it is between -3 and 1, it is a borderline type. to be. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

11 is a diagram illustrating an example of classifying a patient type using a Nasiolabial Angle (°) measurement value according to an embodiment of the present invention.

Referring to Figure 11, the prosthetic design device is 1-2. Based on the Nasiolabial Angle (°) measurement value, patient types are classified into extraction, non-extraction, and borderline. 1-2. Nasiolabial Angle (°) is a nasolabial angle element to confirm the esthetics and oral protrusion of the soft tissue outline connecting the lower part of the nose and the throat. It's an angle.

1-2. If the measured value for Nasiolabial Angle (°) is less than 85°, it is an extraction type, if it is greater than 105°, it is a non-extraction type, and if it is between 85° and 105°, it is a borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

12 is a diagram illustrating an example of classifying a patient type using a measurement value of Lower Lip to E-line (mm) according to an embodiment of the present invention.

Referring to Figure 12, the prosthetic design device is 1-3. Based on the measurement value of Lower Lip to E-line (mm), patient types are classified into Extraction, Non-Extraction, and Borderline. 1-3. Lower Lip to E-line represents the protrusion of the lower lip based on the Pn (Pronasale)-Pog' (Soft tissue Pogonion) connection line for the side hair of the patient's facial hair. Pn (Pronasale) is the most protruding part of the tip of the nose, and Pog' (Soft tissue Pogonion) is the most anterior point of the jawbone.

1.3 If the measured value for Lower Lip to E-line is greater than 5, it is an extraction type, if it is less than -1, it is a non-extraction type, and if it is between -1 and 5, it is a borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

13 is a diagram illustrating an example of classifying patient types using a Frankfort Mandibular Plane Angle (FMA) (°) measurement value according to an embodiment of the present invention.

Referring to Figure 13, the prosthetic design device is 2-1. Patient types are classified into extraction, non-extraction, and borderline based on the FMA (Frankfort Mandibular Plane Angle) (°) measurement value. 2-1. FMA (Frankfort Mandibular Plane Angle) (°) is an element for confirming the vertical relationship between the lateral hairs of the skeletal structure, and the line connecting Po(Porion)-Or(Orbitale) FH Plane and Go(Gonion)-Me(Menton) ) Means the angle between the Mandibular Plane. Po (Porion) is the uppermost point of the external ear hole, Or (Orbitale) is the lowest point of the orbit, Go (Gonion) is the intersection of the tangent line of the mandibular lower edge and the posterior edge of the mandible, Me (Menton) is the mandibular joint outline It is on the line of intersection of the mandibular line as the uppermost and lowermost point.

2-1. If the measured value for Frankfort Mandibular Plane Angle (FMA) (°) is greater than 30°, it is an extraction type, if it is less than 20°, it is a non-extraction type, and if it is between 20° and 30°, it is a mixed ( Borderline) type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

14 is a diagram illustrating an example of classifying patient types using SN-MP (°) measurement values according to an embodiment of the present invention.

Referring to Figure 14, the prosthetic design device is 2-2. Based on the SN-MP (°) measurement value, patient types are classified into extraction, non-extraction, and borderline. 2-2. SN-MP (°) is an element for confirming the vertical relationship between the lateral hairs of the skeletal structure, a line connecting S(Sella)-N(Nasion) and a line connecting SN Plane and Go(Gonion)-Me(Menton) It means the angle between the mandibular planes. Go(Gonion) is the intersection of the mandibular inferior edge and the mandibular posterior edge, and Me(Menton) is the lowest point on the mandibular coupling outline and is on the intersection of the mandibular line.

2-2. If the measured value for SN-MP (°) is greater than 34°, it is an extraction type, if it is less than 30°, it is a non-extraction type, and if it is between 30° and 34°, it is a borderline type. . However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

FIG. 15 is a diagram illustrating an example of classifying patient types using measurement values of PFH (Posterior Facial Height) / AFH (Anterior Facial Height) (%) according to an embodiment of the present invention.

Referring to Figure 15, the prosthetic design device is 2-3. Based on the measurement values of PFH (Posterior Facial Height) / AFH (Anterior Facial Height) (%), patient types are classified into extraction, non-extraction, and borderline. 2-3. PFH (Posterior Facial Height) / AFH (Anterior Facial Height) (%) is an element to check the vertical relationship with the lateral hair of the skeletal structure. Facial Height). The anterior facial height (AFH) is the length of the line connecting N (Nasion) and Me (Menton), and the posterior facial height (PFH) is the length of the line connecting S (Sella) and Go (Gonion).

2-3. If the measured value for Posterior Facial Height (PFH) / Anterior Facial Height (AFH) (%) is less than 61%, it is an extraction type, and if it is greater than 69°, it is a non-extraction type, and 61% and If it is between 69%, it is a Borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

16 is a diagram illustrating an example of classifying patient types using an Incisor Mandibular Plane Angle (IMPA) (°) measurement value according to an embodiment of the present invention.

Referring to Figure 16, the prosthetic design device 2-4. Patient types are classified into Extraction, Non-Extraction, and Borderline based on the measured value of IMPA (Incisor Mandibular Plane Angle) (°). 2-4. IMPA (Incisor Mandibular Plane Angle) (°) is a skeletal-tooth relationship element targeting the lateral hair of the skeletal structure, and represents the mandibular central incisor relationship to the mandibular basal bone. Expressed by this formula, it is the angle between the mandibular plane and the mandibular central incisor tooth axis connecting Go(Gonion)-Me(Menton).

2-4. If the measured value for Incisor Mandibular Plane Angle (IMPA) (°) is greater than 100°, it is an extraction type, if it is less than 90°, it is a non-extraction type, and if it is between 90° and 100°, it is a mixed ( Borderline) type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

17 is a diagram illustrating an example of classifying patient types using a Frankfort Mandibular Incisor Angle (FMIA) (°) measurement value according to an embodiment of the present invention.

Referring to Figure 17, the prosthetic design device is 2-5. Patient types are classified into Extraction, Non-Extraction, and Borderline based on the measured value of FMIA (Frankfort Mandibular Incisor Angle) (°). 2-5. FMIA (Frankfort Mandibular Incisor Angle) (°) is a skeletal-tooth relationship element targeting lateral hairs among skeletal structures, and represents the relationship of the mandibular central incisor to the cranial structure. Expressed by this formula, it is the angle between the line FH Plane connecting Po(Porion)-Or(Orbitale) and the mandibular central incisor tooth axis.

2-5. If the measured value for the Frankfort Mandibular Incisor Angle (FMIA) (°) is less than 55°, it is an extraction type, if it is greater than 65°, it is a non-extraction type, and if it is between 55° and 65°, it is a mixed ( Borderline) type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

18 is a diagram illustrating an example of classifying patient types using L1 to A-Pog (mm) measurement values according to an embodiment of the present invention.

Referring to Figure 18, the prosthetic design device is 2-6. Based on the measurement value of L1 to A-Pog (mm), patient types are classified into Extraction, Non-Extraction, and Borderline. 2-6. L1 to A-Pog (mm) is an element for confirming the skeletal-tooth relationship in the lateral hair of the skeletal structure, and is the distance from the front end of the mandibular central incisor to the A-Pog line. In harmonious facial hair, the mandibular incisor labial surface is located anterior (+) of the A-Pog line.

2-6. If the measured value for L1 to A-Pog (mm) is greater than 6 mm, it is an extraction type, if it is less than 2 mm, it is a non-extraction type, and if it is between 2 mm and 6 mm, it is a borderline. It is a type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

19 to 22 are diagrams illustrating an example of classifying patient types using EI (Extraction Index) measurement values according to an embodiment of the present invention.

In more detail, FIG. 19 is an example of measuring an ODI (Overbite Depth Indicator) value required for EI (Extraction Index) measurement, FIG. 20 is an example of APDI (Anteroposterior Dysplasia Indicator) measurement required for EI (Extraction Index) measurement, and FIG. Index) Interincisor angle (LLA), the upper lip to E-line (UL) (mm) for the E-line, and the lower lip to E for the E-line. -line: LL) (mm) measurement example, FIG. 22 is a diagram showing an example of measurement of an extraction index (EI), respectively.

19 to 22, 2-7. The EI (Extraction Index) is a vertical-horizontal relationship element of the skeleton, and the skeleton vertical relationship element ODI (Overbite Depth Indicator) (①+②), the skeleton horizontal relation element APDI (Anteroposterior Dysplasia Indicator) (③+④+⑤), incisors Interincisor Angle (LLA) (⑥), Upper Lip to E-line (UL) (⑦) and Lower Lip to E-line: Consider LL)(⑧). If this is expressed as an equation, EI = ODI + APDI + (130-LLA)/5-(UL+LL).

Referring to FIG. 19, an ODI (Overbite Depth Indicator) is a sum of ①∠AB to MP Plane (Angle formed by AB Plane and Mandibular Plane) and ②∠PP Plane to FH Plane (Angle formed by FH Plane and Palatal Plane). .

Referring to FIG. 20, APDI (Anteroposterior Dysplasia Indicator) includes ③∠Facial Plane to FH Plane (Angle formed by Facial Plane and FH Plane), and ④∠AB Plane to Facial Plane (Anteroposterior Dysplasia Indicator). , ⑤∠PP Plane to FH Plane (the angle formed by PP Plane and FH Plane) is summed.

2-7. If the measured value for the EI (Extraction Index) is less than 146, it is an extraction type, if it is greater than 162, it is a non-extraction type, and if it is between 146 and 162, it is a borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

23 is a diagram illustrating an example of classifying a patient type using a measurement value of Mx ALD (Maxillary Arch Length Discrepancy) (mm) according to an embodiment of the present invention.

Referring to Figure 23, the prosthetic design device 3-1. Based on the Mx ALD (Maxillary Arch Length Discrepancy) (mm) measurement value, patient types are classified into extraction, non-extraction, and borderline. 3-1. Mx ALD (Maxillary Arch Length Discrepancy) (mm) is an incongruity factor between the tooth length and the arch length of the maxilla, and means the difference between the sum of the size of each tooth between the first molar and the first molar arch length of the maxilla.

3-1. If the measured value for Mx ALD (Maxillary Arch Length Discrepancy) (mm) is less than -9 mm, it is an extraction type, and if it is greater than -4 mm, it is a non-extraction type, and -9 mm and -4 If it is between mm, it is a Borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

24 and 25 are diagrams illustrating an example of classifying patient types using Bolton (excess in Mx) (mm) measurement values according to an embodiment of the present invention.

24 and 25, the prosthetic design device is 3-2. Bolton (excess in Mx) (mm) Patient types are classified into extraction, non-extraction, and borderline based on measured values. 3-2. Bolton (excess in Mx) (mm) means the maxillary excess factor according to the Bolton ratio. Bolton ratio refers to the aesthetic ratio that looks best when the sum of the maxillary incisor widths: the sum of the mandibular incisor widths is 100:77.2. For example, if the sum of the mandibular incisor widths is 30.9 and the sum of the maxillary incisor widths is 40, the Bolton ratio is satisfied. At this time, if the sum of the widths of the mandibular incisors is actually 30.9 and the sum of the widths of the maxillary incisors is 41, the value exceeding the mandible reference maxilla compared to the Bolton ratio is 1 mm (41-40).

If the mandibular reference maxillary excess is greater than 4 mm, it is an extraction type, if it is 0 mm, it is a non-extraction type, and if it is between 0 mm and 4 mm, it is a borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

26 is a diagram illustrating an example of classifying a patient type using a measurement value of Mn ALD (Mandibular Arch Length Discrepancy) (mm) according to an embodiment of the present invention.

Referring to Figure 26, the prosthetic design device 3-3. Based on the Mn ALD (Mandibular Arch Length Discrepancy) (mm) measurement value, patient types are classified into extraction, non-extraction, and borderline. 3-3. Mn ALD (Mandibular Arch Length Discrepancy) (mm) is an incongruity factor between the length of the teeth of the mandible and the length of the dentition, and means the difference between the sum of the length of the arch length between the first molar teeth of the mandible and the size of each tooth between the first molars.

3-3. If the measured value for Mn ALD (Mandibular Arch Length Discrepancy) (mm) is less than -9 mm, it is an extraction type, and if it is greater than -4 mm, it is a non-extraction type, and -9 mm and -4 If it is between mm, it is a Borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

27 and 28 are diagrams illustrating an example of classifying patient types using Bolton (excess in Mn) (mm) measurement values according to an embodiment of the present invention.

27 and 28, the prosthetic design device is 3-4. Bolton (excess in Mn) (mm) Patient types are classified into Extraction, Non-Extraction, and Borderline based on the measured value. 3-4. Bolton (excess in Mn) (mm) refers to the mandibular excess factor according to the Bolton ratio. Bolton ratio refers to the aesthetic ratio that looks best when the sum of the maxillary incisor widths: the sum of the mandibular incisor widths is 100:77.2. For example, if the sum of the mandibular incisor widths is 30.9 and the sum of the maxillary incisor widths is 40, the Bolton ratio is satisfied. At this time, if the sum of the widths of the mandibular incisors is actually 31.9 and the sum of the widths of the maxillary incisors is 40, the value exceeding the maxillary reference mandible compared to the Bolton ratio is 1 mm (31.9-30.9).

If the maxillary reference mandibular excess is greater than 4 mm, it is an extraction type, if it is 0 mm, it is a non-extraction type, and if it is between 0 mm and 4 mm, it is a borderline type. However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

29 is a diagram illustrating an example of classifying a patient type using a measurement value of Curve of Spee (mm) according to an embodiment of the present invention.

Referring to Figure 29, the prosthetic design device is 3-5. Based on the measurement value of Curve of Spee (mm), the patient type is classified into extraction, non-extraction, and borderline. 3-5. Curve of Spee (mm) is an occlusal plane element, and means the vertical distance between the cusps located at the most gingival side in the horizontal plane connecting the mandibular anterior incisal edge (midpoint)-the distal buccal cusps of the mandibular second molar.

3-5. If the measurement value for Curve of Spee (mm) is greater than 6 mm, it is an extraction type, if it is less than 2 mm, it is a non-extraction type, and if it is between 2 mm and 6 mm, it is a borderline type. . However, this is only an exemplary embodiment for aiding understanding of the present invention, and the numerical value for determining the type may be changed.

30 and 31 are diagrams illustrating an example of classifying patient types using an Irregularity Index (mm) measurement value according to an embodiment of the present invention.

30 and 31, the prosthetic design device is 3-6. Based on the measured value of the Irregularity Index (mm), patient types are classified into Extraction, Non-Extraction, and Borderline. 3-6. The Irregularity Index (mm) is an arrangement-related element of the mandibular anterior teeth, which is a measure of the distance away from the anatomical contact point from the adjacent surface of tooth number #33~#43. This corresponds to A+B+C+D+E in FIGS. 30 and 31.

3-6. If the measured value for the Irregularity Index (mm) is greater than 6.5 mm, it is an extraction type, if it is less than 3.5 mm, it is a non-extraction type, and if it is between 3.5 mm and 6 mm, it is a borderline type. However, this is only an example to aid understanding of the present invention, and the numerical value for determining the type can be changed.

So far, the present invention has been looked at around the embodiments. Those of ordinary skill in the art to which the present invention pertains will be able to understand that the present invention may be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from a descriptive point of view rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

Claims (16)

When at least one of the measurement points and connection lines is set for each diagnosis item classified according to the facial structure, skeletal structure, and tooth structure, the patient's clinical data is acquired, and each diagnosis item is measured by referring to at least one of the set measurement points and connection lines. Obtaining a measurement value accordingly;
Classifying a patient type by using the acquired measurement value for each diagnosis item; And
Automatically setting up teeth according to the categorized patient types;
Calibration design method comprising a. The method of claim 1,
The clinical data includes at least one of the patient's facial scan data, head radiograph, frontal radiograph, tooth model data, CT data, and panoramic data, or includes registration data obtained by matching at least two of them,
The step of acquiring the measurement value is
Acquires facial measurement values through facial analysis using the patient's clinical data, acquires skeletal measurement values through skeletal analysis using the patient's clinical data, and acquires tooth measurement values through tooth analysis using the patient's clinical data. Calibration design method, characterized in that. The method of claim 1, wherein classifying patient types comprises:
As a classification of patient type using facial hair measurement values obtained through facial analysis, at least one of upper lip to E-line, nasiolabial angle, and lower lip to E-line. A calibration design method, characterized in that each patient type is classified from one respective measurement value. The method of claim 1, wherein classifying patient types comprises:
Classification of patient types using skeletal measurement values obtained through skeletal analysis, characterized by classifying patient types from measurement values obtained from each of the skeletal vertical relationship element, skeletal-tooth relationship element, and skeletal vertical-horizontal relationship element. How to design orthodontic. The method of claim 4,
The vertical relationship element of the skeleton includes at least one of FMA (Frankfort Mandibular plane Angle) (°), SN-MP (°), and PFH (Posterior Facial Height) / AFH (Anterior Facial Height) (%),
The skeletal-tooth relationship element includes at least one of Incisor Mandibular Plane Angle (IMPA) (°), Frankfort Mandibular Incisor Angle (FMIA) (°), and L1 to A-Pog (mm),
The vertical-horizontal relationship element of the skeleton includes EI (Extraction Index),
EI (Extraction Index) is the vertical relationship element of the skeleton (Overbite Depth Indicator: ODI), the horizontal relationship element of the skeleton (Anteroposterior Dysplasia Indicator: APDI), the interincisor angle (LLA), and the upper lip. Calibration design method, characterized in that the value measured by using to E-line (mm) and Lower Lip to E-line (mm). The method of claim 1, wherein classifying patient types comprises:
A patient type classification using a tooth measurement value obtained through tooth analysis, wherein the patient type is classified from a measurement value obtained from an intra-jaw element and a measurement value obtained from an inter-jaw relationship element. The method of claim 6,
The measurement values obtained from the elements in the evil are
Maxillary Arch Length Discrepancy (Mx ALD) (mm), Mandibular Arch Length Discrepancy (Mn ALD) (mm) and occlusal plane Including at least one of the elements (Curve of Spee) (mm),
The measurement values obtained from the elements of the relationship between the evils are
Bolton excess in Mx (mm) according to Bolton ratio, mandible excess in Mn according to Bolton ratio (mm), and Irregularity Index (mm) of the mandibular anterior teeth (mm) ) Calibration design method comprising at least one of. The method of claim 1, wherein classifying patient types comprises:
Correction design method, characterized in that when multiple patient types are mixed, the main type is determined according to a preset criterion. The method of claim 1, wherein the step of automatically setting up the teeth
Generating digital setup data for each classified patient type;
In each diagnostic item, if the measured value exceeds the normal range, setup to adjust to the normal range value is performed and the setup result is provided, but if the patient type is an extraction type, the setup result of automatically performing tooth extraction is provided. step; And
Correcting a setup result according to a user manipulation input;
Calibration design method comprising a. The method of claim 1, wherein the calibration design method
It performs registration between two data constituting clinical data, separates individual teeth from at least one of the data on which the registration was performed or two data constituting clinical data, and extracts feature information for the separated individual teeth, and clinical data A preparation step of extracting at least one reference information from among the two pieces of data constituting a;
Calibration design method further comprising a. Classify diagnostic items according to facial structure, skeletal structure, and tooth structure, obtain clinical data of the patient in a state in which at least one of the measurement points and connection lines are set for each classified diagnosis item, and refer to at least one of the set measurement points and connection lines. A data analysis unit that obtains a measurement value according to measurement of each diagnosis item and classifies a patient type by using the obtained measurement value for each diagnosis item; And
A data setup unit for automatically setting up teeth according to the classified patient types;
Orthodontic design device comprising a. The method of claim 11, wherein the data analysis unit
A facial hair analysis unit that obtains a facial hair measurement value through facial structure analysis using the patient's clinical data;
Skeletal analysis unit for obtaining a skeletal measurement value through skeletal structure analysis using the patient's clinical data;
A tooth analysis unit that obtains a tooth measurement value through tooth structure analysis using the patient's clinical data; And
A patient type classification unit for classifying patient types into extraction types, non-extraction types, and mixed types for each diagnosis item according to facial structure, skeletal structure, and tooth structure analysis;
Orthodontic design device comprising a. The method of claim 12, wherein the patient type classification unit
As a classification of patient type using facial hair measurement values obtained through facial analysis, at least one of upper lip to E-line, nasiolabial angle, and lower lip to E-line. Orthodontic design device, characterized in that each patient type is classified from one respective measurement value. The method of claim 12, wherein the patient type classification unit
Classification of patient types using skeletal measurement values obtained through skeletal analysis, characterized by classifying patient types from measurement values obtained from each of the skeletal vertical relationship element, skeletal-tooth relationship element, and skeletal vertical-horizontal relationship element. Orthodontic design device. The method of claim 12, wherein the patient type classification unit
A patient type classification using a tooth measurement value obtained through tooth analysis, wherein the patient type is classified from a measurement value obtained from an intra-jaw element and a measurement value obtained from an inter-jaw relationship element. The method of claim 11, wherein the data setup unit
Generate digital setup data for each categorized patient type,
In each diagnostic item, if the measured value exceeds the normal range, setup to adjust to the normal range value is performed and the setup result is provided, but if the patient type is an extraction type, the setup result of automatically performing tooth extraction is provided. ,
Calibration design device, characterized in that correcting the setup result according to the user operation input.

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