Method of determining the effectiveness of orthodontic treatment and method of designing an orthodontic appliance

Abstract

This invention relates to the field of medical device technology and discloses a method for determining the effectiveness of orthodontic treatment and a method for designing orthodontic appliances. The invention obtains a patient's actual dentition model and a projected dentition model. The actual dentition model is obtained by scanning the patient's dentition after a preset treatment time. The projected dentition model is obtained by designing the tooth arrangement on an initial dentition model, which is the dentition model obtained by scanning the patient before treatment. The actual dentition model is processed by tooth segmentation. The position and posture of a single tooth in the actual dentition model are compared with the corresponding single tooth in the projected dentition model to obtain the transformation parameters corresponding to that single tooth. Based on the relationship between the transformation parameters corresponding to each single tooth and the expected threshold range, the orthodontic effectiveness of each single tooth in the actual dentition model can be determined more accurately.

Description

[0001] This application is a divisional application of Chinese invention patent application filed on April 29, 2022, with application number 202210474036.8 and entitled "Method for determining the effect of orthodontic treatment, method for designing orthodontic appliances, equipment and media". Technical Field

[0002] This invention relates to the field of medical device technology, and in particular to a method for determining the effectiveness of orthodontic treatment and a method for designing orthodontic appliances. Background Technology

[0003] Currently, patients typically correct malocclusion by wearing orthodontic appliances. For aesthetic reasons, patients can choose clear aligners, which use transparent aligners to move teeth and correct malocclusion. However, clear aligner treatment requires a longer treatment period and multiple stages. At each stage, the patient needs to change the aligners to progressively correct the teeth. As the treatment progresses, there may be a discrepancy between the patient's actual dentition after treatment and the planned dentition alignment. This could lead to the aligners becoming unsuitable or unsuitable for the next stage.

[0004] To effectively avoid the aforementioned problem of mismatch between orthodontic appliances and the patient's teeth, it is possible to analyze whether the patient's actual dentition conforms to the expected outcome after a period of treatment, thus determining the effectiveness of the orthodontic treatment. When analyzing the effectiveness of treatment, the expected dentition and the actual dentition are usually considered as a whole, and then aligned as a whole. However, the inventors discovered that teeth in the actual dentition that deviate from the expected dentition will affect the overall alignment result. This leads to deviations in position or posture of teeth in the actual dentition that conform to the expected dentition arrangement design after final overall alignment, affecting the final analysis results. Summary of the Invention

[0005] The purpose of this invention is to provide a method for determining the effectiveness of orthodontic treatment and a method for designing orthodontic appliances, so that the analyzed and determined effectiveness of orthodontic treatment is more accurate.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for determining the effectiveness of orthodontic treatment, comprising: acquiring a patient's actual dentition model and a projected dentition model; wherein, the actual dentition model is the patient's dentition model obtained by scanning equipment after a preset treatment time, and the projected dentition model is the dentition model obtained by designing tooth arrangement on an initial dentition model, the initial dentition model being the dentition model obtained by scanning equipment before the patient received orthodontic treatment; and performing tooth separation processing on the actual dentition model; The positions and orientations of individual teeth in the actual dental arch model and their corresponding positions in the expected dental arch model are compared to obtain the transformation parameters corresponding to each individual tooth. These transformation parameters include displacement and rotation parameters. Based on the transformation parameters corresponding to each individual tooth, an expected threshold range is calculated, including: calculating the median of the expected threshold range based on the transformation parameters corresponding to each individual tooth, and determining the expected threshold range based on the median and error; or, clustering the transformation parameters corresponding to each individual tooth and determining the expected threshold range based on the clustering results; or, setting the expected threshold range based on the position and orientation of each individual tooth in the expected dental arch model. The minimum value within the expected threshold range is the median of the expected threshold range minus the error, and the maximum value within the expected threshold range is the median of the expected threshold range plus the error. Based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, the orthodontic effect of each individual tooth in the actual dental arch model is determined.

[0007] Embodiments of the present invention also provide a method for designing an orthodontic appliance, comprising: using the method described above for determining the orthodontic achievement effect, analyzing the orthodontic achievement effect of each individual tooth after a preset orthodontic time; if there are individual teeth whose orthodontic achievement effect is lower than expected, designing a supplementary orthodontic plan based on the transformation parameters of all individual teeth whose orthodontic achievement effect is lower than expected.

[0008] Compared to existing technologies, this invention provides a scanning device that obtains the patient's actual dentition model after a preset treatment period. Simultaneously, it obtains a projected dentition model derived from the initial dentition model through tooth arrangement design. The initial dentition model is the one obtained by scanning the patient before treatment. The obtained actual dentition model undergoes tooth segmentation processing to distinguish individual teeth, allowing for comparison of the position and orientation of corresponding teeth in the actual and projected dentition models. This, in turn, yields the transformation parameters of the corresponding teeth in the actual and projected dentition models. Then, based on the relationship between the transformation parameters of a single tooth in the actual dentition model and the corresponding single tooth in the expected dentition model and the expected threshold range, the orthodontic achievement of that tooth in the actual dentition model is determined. That is, the orthodontic achievement of that single tooth is determined by the position and posture changes of the single tooth in the corresponding tooth position between the two models. This reduces the influence of the position or posture changes of other teeth on the determination of the orthodontic achievement of that single tooth to a certain extent, so as to make the analysis and determination of the orthodontic achievement of each tooth in the actual dentition model more accurate.

[0009] In addition, the positions and orientations of individual teeth in the actual dental arch model and their corresponding positions in the expected dental arch model are compared to obtain the transformation parameters for each individual tooth. This includes: individually aligning individual teeth in the actual dental arch model with their corresponding positions in the expected dental arch model; determining the changes in position and orientation between individual teeth in the actual dental arch model and their corresponding positions in the expected dental arch model after individual alignment; wherein the change in position is used as a displacement parameter, and the change in orientation is used as a rotation parameter. Since the adjustment of teeth during orthodontic treatment includes not only translational changes but also changes in the orientation of teeth at certain angles, when considering the transformation parameters between individual teeth in the actual dental arch model and their corresponding positions in the expected dental arch model, it is necessary to consider both the change in position and the change in orientation between individual teeth in the actual dental arch model and their corresponding positions after alignment. This ensures that the obtained transformation parameters can more comprehensively reflect the changes between a single tooth in the actual dentition model and a single tooth at the corresponding position in the expected dentition model.

[0010] In addition, individual teeth in the actual dental arch model are individually aligned with their corresponding positions in the expected dental arch model. This includes: aligning individual teeth in the actual dental arch model with their corresponding positions in the expected dental arch model using coordinate axis alignment; or aligning individual teeth in the actual dental arch model with their corresponding positions in the expected dental arch model based on tooth feature points; or first aligning individual teeth in the actual dental arch model with their corresponding positions in the expected dental arch model using coordinate axis alignment, and then aligning the aligned individual teeth in the actual dental arch model with their corresponding positions in the expected dental arch model based on tooth feature points. Multiple individual tooth alignment methods are provided, each yielding different results in terms of alignment accuracy or efficiency, allowing technical personnel to use different individual alignment methods according to different needs.

[0011] In addition, the alignment of individual teeth in the actual dentition model with the corresponding teeth in the expected dentition model is achieved by aligning coordinate axes. This includes: calibrating the coordinate systems of individual teeth in the actual dentition model and the corresponding teeth in the expected dentition model using the same calibration rules; and aligning individual teeth in the actual dentition model with the corresponding teeth in the expected dentition model by aligning coordinate axes.

[0012] In addition, the calibration rules include: taking any one of the following positions as the origin of the coordinate system: the centroid of a single tooth, the midpoint of the gingival line of a single tooth, the center point of the resistance of a single tooth movement, and the apex of the root of a single tooth; taking the direction of the tooth axis as the Z-axis; taking the direction of the line connecting the labial and lingual sides as the Y-axis; and taking the X-axis direction perpendicular to the X-axis direction and the Y-axis direction perpendicular to the Z-axis direction.

[0013] Furthermore, the X-axis direction is set according to the tooth type; when the tooth type is an incisor or canine, the X-axis direction is parallel to the incisal edge of that tooth; when the tooth type is a premolar or molar, the X-axis direction is parallel to the central groove of that tooth. Since different types of teeth differ in shape, feature points of different tooth types are used as reference points for coordinate axis setting based on their morphological characteristics. When calibrating the coordinate axes of the same type of teeth in different dentition models, it is easier to determine the position of the coordinate axes for that type of tooth, thus ensuring that the same type of teeth in the actual dentition model and the expected dentition model are calibrated using a unified calibration rule.

[0014] In addition, based on the feature points of the teeth, the single teeth in the actual dentition model are aligned with the single teeth in the corresponding positions in the expected dentition model. This includes: using the Iterative Closest Point (ICP) algorithm to make the preset feature points of the single teeth in the actual dentition model coincide with the preset feature points of the single teeth in the corresponding positions in the expected dentition model, so that the single teeth in the actual dentition model are aligned with the single teeth in the corresponding positions in the expected dentition model.

[0015] In addition, preset feature points are set according to tooth type; if the tooth type is an incisor or canine, the preset feature points are the mesial and distal incisal edges; if the tooth type is a premolar, the preset feature points are the lingual and labial protrusions; if the tooth type is a molar, the preset feature points are the lingual mesial protrusion, the labial mesial protrusion, the lingual distal protrusion, and the labial distal protrusion. Since different types of teeth differ in shape, feature points on teeth that are easily distinguishable are selected based on their morphological characteristics for use as data for single-tooth alignment. This allows for quick and accurate acquisition of these feature points from the teeth and ensures that the positions of feature points acquired from different dentitions are consistent, increasing the alignment accuracy between individual teeth.

[0016] In addition, the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth, or the expected threshold range is set based on the position and orientation of each individual tooth in the expected dentition model.

[0017] In addition, the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth, including: calculating the median of the expected threshold range based on the transformation parameters corresponding to each individual tooth, and determining the expected threshold range based on the median of the expected threshold range and the error; wherein, the minimum value within the expected threshold range is the median of the expected threshold range minus the error, and the maximum value within the expected threshold range is the median of the expected threshold range plus the error.

[0018] In addition, the median of the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth, and the expected threshold range is determined based on the median and error of the expected threshold range, including: calculating the first average value of the transformation parameters corresponding to all teeth; using the first average value as the median of the expected threshold range; calculating the first variance of the transformation parameters corresponding to all teeth and the first average value, and using the product of the first variance and the first preset weighting coefficient as the error; and determining the expected threshold range based on the median and error of the expected threshold range.

[0019] In addition, calculating the first average value of the transformation parameters corresponding to all teeth includes: converting the transformation parameters corresponding to all teeth into coordinate points in a spatial coordinate system, where each coordinate point represents a transformation parameter; and calculating the first average value based on the distribution of the coordinate points. Treating the transformation parameter as a coordinate point in a spatial coordinate system transforms the calculation of multi-dimensional data into an analysis of the distribution of multiple points in the coordinate system, which reduces the computational difficulty to some extent.

[0020] Furthermore, the median of the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth. The expected threshold range is then determined based on the median and error. This process includes: calculating the normal distribution parameters of the transformation parameters for all teeth; determining the median of the normal distribution based on the normal distribution parameters; using the median of the normal distribution as the median of the expected threshold range; using the product of the standard deviation of the normal distribution and a second preset weighting coefficient as the error; and finally, determining the expected threshold range based on the median and error. By calculating the median of the normal distribution to determine the expected threshold range, the determination of the expected threshold range is not affected by extreme data, meaning it is less susceptible to the influence of severely misaligned teeth expected to be corrected, thus ensuring the accuracy of the treatment outcome.

[0021] In addition, the normal distribution parameters of the transformation parameters corresponding to all teeth are calculated, including: converting the transformation parameters corresponding to all teeth into coordinate points in a spatial coordinate system, where each coordinate point represents one of the transformation parameters; and calculating the normal distribution parameters based on the distribution of the coordinate points.

[0022] Furthermore, the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth, including: clustering the transformation parameters corresponding to each individual tooth, and determining the expected threshold range based on the clustering results. Clustering can quickly and accurately obtain the range of the majority of data among all transformation parameters, and this range is used as the expected threshold range. This method is suitable when a large proportion of the teeth meet the expected orthodontic treatment requirements, and clustering improves the efficiency of determining the expected threshold range.

[0023] In addition, the transformation parameters corresponding to each individual tooth are clustered, including: converting the transformation parameters corresponding to all teeth into coordinate points in a spatial coordinate system, wherein each coordinate point represents one of the transformation parameters; and clustering based on the distribution of the coordinate points.

[0024] In addition, based on the transformation parameters corresponding to each individual tooth, the expected threshold range is calculated, including: determining the target teeth based on the transformation parameters corresponding to each individual tooth; wherein, the target teeth are N individual teeth in the actual dentition model whose movement and rotation conform to the expected orthodontic treatment, and N individual teeth in the expected dentition model with the same tooth position as the N individual teeth in the actual dentition model; N is greater than 1; using the target teeth as alignment references, the actual dentition model and the expected dentition model are aligned as a whole; based on the overall aligned actual dentition model and the expected dentition model, the expected threshold range is determined. This allows for a direct observation of the deviations between corresponding teeth in the two dentition models after overall alignment.

[0025] Furthermore, using the target teeth as alignment references, the actual dentition model and the expected dentition model are aligned as a whole. This includes: forming a first set of N individual teeth from the target teeth that belong to the actual dentition model; forming a second set of N individual teeth from the target teeth that belong to the expected dentition model; aligning the N individual teeth in the first set with the N individual teeth in the second set to obtain overall transformation parameters, which include overall displacement parameters and overall rotation parameters; and aligning the actual dentition model and the expected dentition model as a whole based on the overall transformation parameters. Since the median of the expected threshold range confirmed by aligning the actual dentition model and the expected dentition model as a whole is zero, the deviation between corresponding teeth in the two dentition models can be intuitively observed after overall alignment, allowing for a direct determination of the orthodontic effect without further calculations.

[0026] In addition, based on the overall aligned actual dentition model and the expected dentition model, the expected threshold range is determined, including: determining the deviation of a single tooth in the aligned actual dentition model and a single tooth in the corresponding position in the expected dentition model, the deviation including displacement deviation and posture deviation; and calculating the expected threshold range based on the deviation of each single tooth.

[0027] In addition, the transformation parameters include six variables; three of which represent displacement parameters and the other three represent rotation parameters; the expected threshold range includes: a displacement expected threshold range and a rotation expected threshold range; the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth, including: calculating the displacement expected threshold range based on the displacement parameters; calculating the rotation expected threshold range based on the rotation parameters; the orthodontic achievement of each individual tooth in the actual dental arch model is determined based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, including: using the relationship between the displacement parameters corresponding to each individual tooth and the displacement expected threshold range as the first result; using the relationship between the rotation parameters corresponding to each individual tooth and the rotation expected threshold range as the second result; and comprehensively analyzing the first and second results to determine the orthodontic achievement of each individual tooth in the actual dental arch model.

[0028] In addition, the displacement expectation threshold range is calculated based on the displacement parameters, including: calculating the second average value of the displacement parameters of all teeth; calculating the second variance of the displacement parameters corresponding to all teeth and the second average value; determining the displacement expectation threshold range based on the second average value and the second variance; wherein the minimum value within the displacement expectation threshold range is the difference between the product of the second average value, the second variance, and the third preset weighting coefficient, and the maximum value within the displacement expectation threshold range is the sum of the products of the second average value, the second variance, and the third preset weighting coefficient. The rotation expectation threshold range is calculated based on the rotation parameters, including: calculating the third average value of the rotation parameters of all teeth; calculating the third variance of the rotation parameters corresponding to all teeth and the third average value; determining the rotation expectation threshold range based on the third average value and the third variance; wherein the minimum value within the rotation expectation threshold range is the difference between the product of the third average value, the third variance, and the fourth preset weighting coefficient, and the maximum value within the rotation expectation threshold range is the sum of the products of the third average value, the third variance, and the fourth preset weighting coefficient.

[0029] In addition, the third preset weight coefficient and the fourth preset weight coefficient are the same, or different values ​​are set for the third preset weight coefficient and the fourth preset weight coefficient according to the proportion of rotation parameters and displacement parameters. The proportion of rotation parameters and displacement parameters is determined based on the degree of influence on the treatment effect. In this way, different weight coefficients can be set for rotation parameters or displacement parameters according to the needs of different degrees of influence, so that the proportion of each component in the effect can be set as needed to meet the needs of different application scenarios.

[0030] In addition, the transformation parameters include M variables; the expected threshold range includes: M expected threshold sub-ranges corresponding to the M variables respectively; the expected threshold range is calculated based on the transformation parameters corresponding to each individual tooth, including: calculating the expected threshold sub-range corresponding to any variable of the transformation parameters, until M expected threshold sub-ranges are calculated; the orthodontic achievement of each individual tooth in the actual dental arch model is determined based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, including: comprehensively analyzing the relationship between each variable and its corresponding expected threshold sub-range to determine the orthodontic achievement of each individual tooth in the actual dental arch model.

[0031] In addition, by comprehensively analyzing the relationship between each variable and its corresponding expected threshold sub-range, the orthodontic achievement of each individual tooth in the actual dental arch model is determined, including: identifying several variables that are outside the corresponding expected threshold sub-range, and determining the extent to which each of these variables exceeds its corresponding expected threshold sub-range; and determining the orthodontic achievement of each individual tooth in the actual dental arch model based on the variable that exceeds its corresponding expected threshold sub-range the most.

[0032] In addition, based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, the orthodontic achievement of each individual tooth in the actual dental arch model is determined, including: if the transformation parameters corresponding to an individual tooth are within the expected threshold range, then the orthodontic achievement of that individual tooth is achieved; if the transformation parameters corresponding to an individual tooth are outside the expected threshold range, then the orthodontic achievement of that individual tooth is not achieved.

[0033] In addition, based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, the orthodontic achievement of each individual tooth in the actual dental arch model is determined, including: classifying the orthodontic achievement of each individual tooth into levels based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range; wherein, the greater the difference between the transformation parameters and the expected threshold range, the lower the level of the orthodontic achievement of the corresponding individual tooth.

[0034] Furthermore, after classifying the orthodontic outcomes of each individual tooth based on the relationship between the transformation parameters corresponding to each tooth and the expected threshold range, the process also includes labeling the individual teeth corresponding to different grades using different markers. This labeling allows technicians to more clearly and intuitively determine the orthodontic outcomes of each individual tooth, thereby more efficiently identifying teeth that do not meet the expected treatment goals.

[0035] In addition, the actual dental arch model is processed by tooth segmentation, including: obtaining the tooth surface color information and geometric feature information of the actual dental arch model; using clustering methods and combining the tooth surface color information and geometric feature information to segment each tooth and the tooth and gingiva of the actual dental arch model, so as to obtain each individual tooth of the actual dental arch model.

[0036] In addition, the actual dental arch model is segmented into individual teeth, including: obtaining the triangular mesh of the actual dental arch model; obtaining the vertex height function of the triangular mesh; finding the local minimum of the vertex height function in the actual dental arch model; determining the first type of feature points based on the local minimum; confirming the second type of feature points based on the vertices of the triangular mesh in the actual dental arch model; classifying the first type of feature points to determine whether they belong to the tooth region or the gingival region; classifying the second type of feature points on the digital dental arch model based on the classification results of the first type of feature points to determine whether they belong to the tooth region or the gingival region; and segmenting the tooth region to obtain the individual teeth of the actual dental arch model. Attached Figure Description

[0037] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments, and unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0038] Figure 1 This is a flowchart of a method for determining the effectiveness of correction according to an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of a model of a desired dentition provided according to an embodiment of the present invention;

[0040] Figure 3 This is a flowchart of a tooth separation process provided in step 102 of an embodiment of the present invention;

[0041] Figure 4 This is a flowchart of another tooth separation process provided in step 102 of an embodiment of the present invention;

[0042] Figure 5 This is a flowchart of a single tooth alignment method provided in step 103 of an embodiment of the present invention;

[0043] Figure 6 This is a flowchart of a method for calculating an expected threshold range provided in step 104 of an embodiment of the present invention;

[0044] Figure 7 This is a flowchart of another expected threshold range calculation method provided in step 104 of the present invention;

[0045] Figure 8 This is a flowchart of another expected threshold range calculation method provided in step 104 of the present invention;

[0046] Figure 9 This is a flowchart of another expected threshold range calculation method provided in step 104 of the present invention;

[0047] Figure 10 This is a schematic diagram of an image of the teeth after overall alignment, as described in an embodiment of the present invention;

[0048] Figure 11 This is a flowchart of an orthodontic appliance design method provided according to an embodiment of the present invention;

[0049] Figure 12 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been presented in the various embodiments of the present invention to enable the reader to better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments.

[0051] The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0052] Embodiments of the present invention relate to a method for determining the effectiveness of orthodontic treatment, such as... Figure 1 As shown, the method includes the following steps:

[0053] Step 101: Obtain the patient's actual dentition model and expected dentition model; wherein, the actual dentition model is the patient's dentition model obtained by scanning equipment after a preset treatment time, and the expected dentition model is the dentition model obtained after tooth arrangement design of the initial dentition model, and the initial dentition model is the dentition model obtained by scanning equipment when the patient has not undergone orthodontic treatment.

[0054] Step 102: Perform tooth segmentation processing on the actual dental arch model.

[0055] Step 103: Compare the position and orientation of a single tooth in the actual dentition model with the corresponding tooth position in the expected dentition model to obtain the transformation parameters corresponding to the single tooth; wherein, the transformation parameters include: displacement parameters and rotation parameters.

[0056] Step 104: Based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, determine the orthodontic effect achieved by each individual tooth in the actual dental arch model.

[0057] Compared to existing technologies, this invention provides a scanning device that obtains the patient's actual dentition model after a preset treatment period. Simultaneously, it obtains a projected dentition model derived from the initial dentition model through tooth arrangement design. The initial dentition model is the one obtained by scanning the patient before treatment. The obtained actual dentition model undergoes tooth segmentation processing to distinguish individual teeth, allowing for comparison of the position and orientation of corresponding teeth in the actual and projected dentition models. This, in turn, yields the transformation parameters of the corresponding teeth in the actual and projected dentition models. Then, based on the relationship between the transformation parameters of a single tooth in the actual dentition model and the corresponding single tooth in the expected dentition model and the expected threshold range, the orthodontic achievement of that tooth in the actual dentition model is determined. That is, the orthodontic achievement of that single tooth is determined by the position and posture changes of the single tooth in the corresponding tooth position between the two models. This reduces the influence of the position or posture changes of other teeth on the determination of the orthodontic achievement of that single tooth to a certain extent, so as to make the analysis and determination of the orthodontic achievement of each tooth in the actual dentition model more accurate.

[0058] The implementation details of the embodiments of this application are described in detail below. The following content is only for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0059] In step 101, during the acquisition of the patient's actual dentition model and expected dentition model, the actual dentition model is the patient's dentition model obtained through scanning equipment when the patient undergoes a pre-set treatment period for a treatment status check. For example... Figure 2 As shown, the expected dentition model 20 is a dentition model obtained by scanning equipment before the patient's orthodontic treatment, and is designed to suit the patient's staged treatment. The preset treatment time for obtaining the actual dentition model and the expected treatment time for achieving the desired orthodontic effect of the expected dentition model, as anticipated by the relevant medical staff during the tooth arrangement design, should ideally be the same, or the preset treatment time should be less than the expected treatment time. This specifies the time for patient orthodontic verification, allowing for the planning of opportunities to verify the patient's orthodontic achievement, and facilitating the estimation of the overall orthodontic outcome based on the verification results.

[0060] In addition, in step 102, the actual dental arch model is segmented. This can be done using segmentation methods such as region growth, seed diffusion, triangular facet vertex curvature, and tooth feature point extraction. The following sections will explain the two segmentation methods in detail.

[0061] like Figure 3 The diagram illustrates specific steps in a tooth separation process, including:

[0062] Step 1021a: Obtain the tooth surface color information and geometric feature information of the actual dental arch model.

[0063] Specifically, by using intraoral scan data of the actual dental arch model obtained through scanning equipment, the surface color information of each part contained in the intraoral scan data is determined, and an actual dental arch model with surface color information is built based on the intraoral scan data. Alternatively, the actual dental arch model can be a digital model using triangular meshes. Each triangular mesh contains three vertices and three edges, which together form a triangular facet.

[0064] Step 1022a: Using clustering methods and combining tooth surface color information and geometric feature information, the actual dental arch model is segmented between each tooth and between the teeth and the gingiva to obtain each individual tooth of the actual dental arch model.

[0065] Specifically, a similarity matrix is ​​calculated based on the distances between adjacent triangular facets in the actual dental arch model. The distances between adjacent triangular facets are obtained by combining color and angle functions. A normalized Laplacian matrix is ​​then calculated based on the similarity matrix. Based on the eigenvalues ​​and corresponding eigenvectors in the Laplacian matrix, the positions of each tooth and gingiva in the actual dental arch model are determined. This allows for segmentation of the actual dental arch model between individual teeth and between teeth and gingiva, thus achieving tooth segmentation.

[0066] The above-mentioned tooth segmentation method incorporates the color difference between the tooth and gum surfaces into the tooth segmentation process, which enables more precise segmentation of the teeth and gums in the acquired actual dental arch model, thereby making the segmentation of individual teeth in the dental portion more accurate.

[0067] In addition, such as Figure 4 The diagram illustrates the specific steps of another tooth separation process, including:

[0068] Step 1021b: Obtain the triangular mesh of the actual dental arch model; obtain the vertex height function of the triangular mesh; find the local minimum of the vertex height function in the actual dental arch model;

[0069] Step 1022b: Determine the first type of feature points based on the local minimum points;

[0070] Specifically, the vertex height function H = (-H1) + (-H2) of the triangular patch network in the actual dental arch model is obtained, where H1 represents the vertex curvature of the actual dental arch model, and H2 represents the distance from the vertex of the actual dental arch model to the bottom of the jaw. Local minima Oi of the vertex height function are found in the actual dental arch model. Based on the height of each Oi and the height of the region edge to which the Oi belongs, the region height of each region to which the Oi belongs is calculated. Several regions with the smallest region height are selected, and the Oi in these selected regions are used as the first type of feature points.

[0071] Step 1023b: Identify the second type of feature points based on the vertices of the triangular mesh in the actual dental arch model;

[0072] Step 1024b: Classify the first type of feature points to determine whether the first type of feature points belong to the tooth region or the gingival region;

[0073] Specifically, the classification can be done manually, or the first type of feature points can be automatically classified using a clustering algorithm, dividing the first feature points into tooth feature points and gum feature points.

[0074] Step 1025b: Classify the second type of feature points on the digital dental model according to the classification results of the first type of feature points, and determine whether the second type of feature points belong to the tooth region or the gingival region.

[0075] Calculate the graph shortest distance L1 from all second-class feature points in the actual dental arch model to the classified tooth feature points, and calculate the graph shortest distance L2 from all second-class feature points in the actual dental arch model to the gingival feature points. The graph shortest distance can be calculated using the Dijastra algorithm. For any second-class feature point in the actual dental arch model, if its graph shortest distance to the tooth feature point is greater than its graph shortest distance to the gingival feature point, then the second-class feature point belongs to the gingival region; otherwise, it belongs to the tooth region.

[0076] The tooth region is obtained by merging all the second feature points that are divided into the tooth region, and the gingival region is obtained by merging all the second feature points that are divided into the gingival region. The boundary between the tooth region and the gingival region is smoothed to obtain the final tooth region or gingival region.

[0077] Step 1026b: The tooth region is segmented to obtain individual teeth of the actual dental arch model.

[0078] Specifically, the inflection points of the lingual and buccal gingival lines at the interdental spaces are calculated separately. The gingival lines and interdental spaces of each tooth are combined to form a closed segmentation line for each tooth. Then, a region growing algorithm is used to segment each tooth from the actual dental arch model.

[0079] The above tooth segmentation process uses a method of selecting feature points from the global model and classifying them to segment the model into two regions: teeth and gums. This makes the segmentation results less affected by model noise and more tolerant of errors. Based on the tooth regions obtained in this way, the individual teeth further segmented will also be more accurate.

[0080] In addition, step 103 compares the position and orientation of a single tooth in the actual dentition model with the corresponding tooth in the expected dentition model to obtain the transformation parameters for that single tooth. This comparison can be achieved by aligning individual teeth individually, determining the positional and orientation changes between the aligned teeth in the actual and expected dentition models; the positional change is used as the displacement parameter in the transformation parameters, and the orientation change as the rotation parameter. Alternatively, the comparison can be achieved through digital geometric calculations, obtaining the transformation parameters by calculating the spatial coordinates of the single tooth.

[0081] The following example illustrates how to achieve position and orientation comparison using single-tooth alignment. For instance, a single tooth in the actual dental arch model can be aligned with its corresponding position in the expected dental arch model using coordinate axis alignment; alternatively, a single tooth in the actual dental arch model can be aligned with its corresponding position in the expected dental arch model based on the tooth's feature points; or, first, a single tooth in the actual dental arch model can be aligned with its corresponding position in the expected dental arch model using coordinate axis alignment, and then the aligned single tooth in the actual dental arch model can be aligned with its corresponding position in the expected dental arch model based on the tooth's feature points, thus achieving individual tooth alignment. Since different individual alignment methods yield different results in terms of alignment accuracy or efficiency, technicians can use different individual alignment methods according to different needs.

[0082] This paper explains the specific process of individual alignment by first aligning a single tooth in the actual dental arch model with the corresponding tooth in the expected dental arch model using coordinate axis alignment, and then aligning the aligned single tooth in the actual dental arch model with the corresponding tooth in the expected dental arch model based on the tooth's feature points. Figure 5 As shown, the steps for individual alignment include:

[0083] Step 1031: Align a single tooth in the actual dentition model with the corresponding single tooth in the expected dentition model using coordinate axis alignment.

[0084] Specifically, using the same calibration rules, coordinate systems are calibrated for individual teeth in the actual dentition model and for individual teeth in the corresponding positions in the expected dentition model; then, the individual teeth in the actual dentition model are aligned with the individual teeth in the corresponding positions in the expected dentition model by aligning the coordinate axes.

[0085] The calibration rules can be set as follows: take any one of the following positions as the origin of the coordinate system: the centroid of a single tooth, the midpoint of the gingival line of a single tooth, the center point of the resistance of a single tooth movement, or the apex of the root of a single tooth; take the direction of the tooth axis as the Z-axis; take the direction of the line connecting the labial and lingual sides as the Y-axis; and take the X-axis direction as perpendicular to the X-axis direction and the Y-axis direction as perpendicular to the Z-axis direction.

[0086] Furthermore, different calibration rules can be set for individual teeth of different tooth types, as long as the calibration rules are consistent between the corresponding teeth in the actual dentition model and the expected dentition model. For example, regarding the X-axis setting, if the tooth type is an incisor or canine, the X-axis direction is parallel to the incisal edge of that tooth; if the tooth type is a premolar or molar, the X-axis direction is parallel to the central groove of that tooth. Since different types of teeth differ in shape, using the characteristic points of different tooth types as reference points for coordinate axis setting makes it easier to determine the coordinate axis setting position for the same type of tooth in different dentition models when calibrating the coordinate axis. This ensures that the same type of tooth in the actual dentition model and the expected dentition model is calibrated using a consistent calibration rule.

[0087] Step 1032: Align a single tooth in the aligned actual dentition model with the corresponding single tooth in the expected dentition model based on the tooth's feature points.

[0088] Specifically, aligning individual teeth using coordinate axis alignment has low precision and can only achieve coarse alignment. To improve alignment accuracy, a more precise alignment can be performed based on the tooth's feature points, building upon the initial alignment using coordinate axis alignment. For example, the Iterative Closest Point (ICP) algorithm can be used to match some preset feature points of individual teeth in the actual dentition model with the same preset feature points of individual teeth in the corresponding positions of individual teeth in the expected dentition model, thus aligning the individual teeth in the actual and expected dentition models. When determining the overlap between these preset feature points, the alignment precision can be adjusted by modifying the parameters of the ICP algorithm. This ensures the required precision while improving alignment efficiency and saving time.

[0089] Furthermore, the selection of preset feature points in the ICP algorithm can be set according to tooth type. For incisors or canines, the preset feature points are the mesial and distal edges of the incisal edge. For premolars, the preset feature points are the lingual and labial protrusions. For molars, the preset feature points are the lingual mesial protrusion, labial mesial protrusion, lingual distal protrusion, and labial distal protrusion. Since different types of teeth differ in shape, feature points on teeth that are easily distinguishable are selected based on their morphological characteristics for use as data for single-tooth alignment. This allows for quick and accurate acquisition of these feature points from teeth and ensures that the positions of feature points obtained from different dentitions are consistent, increasing the alignment accuracy between individual teeth.

[0090] Furthermore, in step 104, the orthodontic outcome of each individual tooth in the actual dental arch model is determined based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range. This includes at least two aspects: firstly, how to determine the expected threshold range; and secondly, how to determine the orthodontic outcome of each individual tooth in the actual dental arch model.

[0091] There are at least two ways to determine the expected threshold range. One is to calculate it based on the transformation parameters corresponding to each individual tooth; the other is to set it based on the position and orientation of each individual tooth in the expected dentition model. For example, the expected threshold range for each individual tooth can be determined by adding or subtracting an error from the position and orientation of each individual tooth in the expected dentition model.

[0092] The following section details the methods for calculating the expected threshold range based on the transformation parameters corresponding to each individual tooth. Different calculation methods for the expected threshold range can be obtained by considering the transformation parameter as a whole or by decomposing it. For example, a transformation parameter can be viewed as a coordinate point in a spatial coordinate system, and the expected threshold range can be calculated by calculating the coordinate point in the spatial coordinate system. Alternatively, several variables contained in a transformation parameter can be calculated separately to obtain several expected threshold ranges corresponding to different variables. Furthermore, different reference standards for the expected threshold range can also lead to different calculation methods. For example, the average, median, or mode calculated based on the transformation parameter can be used as the reference standard for the expected threshold range, and an error can be added or subtracted from the reference standard to set the expected threshold range. Alternatively, the transformation parameter can be converted into a normally distributed parameter to determine the expected threshold range; or the expected threshold range can be determined through clustering, and so on.

[0093] The following examples illustrate the calculation methods for different expectation thresholds:

[0094] like Figure 6 The diagram illustrates how to calculate the expected threshold based on the average value of the transformation parameters. The steps include:

[0095] Step 1041a: Calculate the first average value of the transformation parameters corresponding to all teeth; use the first average value as the median of the expected threshold range;

[0096] Step 1042a: Calculate the first variance of the transformation parameters corresponding to all teeth and the first average value, and use the product of the first variance and the first preset weight coefficient as the error;

[0097] Step 1043a: Determine the expected threshold range based on the median and error of the expected threshold range.

[0098] Specifically, the transformation parameters corresponding to all teeth can be converted into coordinate points in a spatial coordinate system, where each coordinate point represents a transformation parameter. A first average value is calculated based on the distribution of these coordinate points. For example, the average value can be calculated based on the distances of all coordinate points to the origin of the spatial coordinate system, and this result is used as the first average value. Next, the first variance of all transformation parameters is calculated based on the distance of each coordinate point to the origin of the spatial coordinate system and the first average value calculated above. This leads to the expected threshold range of [M-k1*d1, M+k1*d1], where M is the first average value, k1 is the first preset weight coefficient, and d1 is the first variance.

[0099] Alternatively, the M variables contained in a transformation parameter can be calculated separately to obtain the sub-expected threshold ranges corresponding to the M different variables. M is greater than 1. For example, suppose the transformation parameter contains six variables: variable 1, variable 2, variable 3, variable 4, variable 5, and variable 6. Calculate the mean and variance of variable 1 among all transformation parameters, and use the product of the mean, variance, and weight of variable 1 to obtain the sub-expected threshold range corresponding to variable 1. Similarly, calculate the six expected threshold ranges corresponding to the six variables. Different weights can be assigned to the sub-expected threshold ranges corresponding to different variables, resulting in different degrees of influence of the magnitude of the variable's shift on the treatment outcome.

[0100] Additionally, several variables contained in a transformation parameter can be grouped. For example, the six variables of the transformation parameter can be divided into position parameters and rotation parameters, where the position parameters include three different variables and the rotation parameters include three different variables. Different grouping methods can also be used; there is no limit to the number of groups or the number of variables in each group. For each group containing a transformation parameter, the mean and variance of all variables belonging to that group are calculated. The expected threshold range corresponding to that group is obtained by multiplying the mean, variance, and weights. Specifically, taking the transformation parameter divided into displacement and rotation parameters as an example, the second mean of the displacement parameters of all teeth is calculated; the second variance of the displacement parameters corresponding to all teeth and the second mean is calculated; the expected threshold range of displacement is determined based on the second mean and the second variance; the minimum value within the expected threshold range of displacement is the difference between the product of the second mean, the second variance, and the third preset weight coefficient. The maximum value within the expected threshold range is the sum of the products of the second average, the second variance, and the third preset weighting coefficient. The rotation expected threshold range is calculated based on the rotation parameters, including: calculating the third average of the rotation parameters for all teeth; calculating the third difference between the rotation parameters for all teeth and the third average; and determining the rotation expected threshold range based on the third average and the third difference. The minimum value within the rotation expected threshold range is the difference between the products of the third average, the third difference, and the fourth preset weighting coefficient, and the maximum value is the sum of the products of the third average, the third difference, and the fourth preset weighting coefficient. The displacement expected threshold range corresponding to the position parameters and the rotation expected threshold range corresponding to the rotation parameters can be calculated using the above method. Similarly, the third preset weighting coefficient for the position parameters and the fourth preset weighting coefficient for the rotation parameters can be set to different weight values. If the position parameters have a significant impact on the treatment outcome, the third preset weighting coefficient for the position parameters should be set smaller; conversely, if the position parameters have a smaller impact on the treatment outcome, the third preset weighting coefficient for the position parameters should be set larger.

[0101] Furthermore, when dividing the transformation parameters into displacement and rotation parameters, the relationship between the displacement parameter corresponding to each individual tooth and the expected displacement threshold range is used as the first result; the relationship between the rotation parameter corresponding to each individual tooth and the expected rotation threshold range is used as the second result. A comprehensive analysis of both results is needed to determine the orthodontic achievement of each individual tooth in the actual dental arch model. For example, the result with the worse orthodontic achievement for the same individual tooth in the first and second results can be taken as the orthodontic achievement for that individual tooth. Similarly, when calculating M variables contained in a transformation parameter to obtain M different sub-expected threshold ranges corresponding to these variables, a comprehensive analysis of the relationship between the M variables and the M sub-expected threshold ranges is needed to determine M results, and the result with the worst performance is taken as the orthodontic achievement for that individual tooth.

[0102] like Figure 7 As shown, the implementation method for converting the transformation parameters into normally distributed parameters to calculate the expected threshold includes the following steps:

[0103] Step 1041b: Calculate the normal distribution parameters of the transformation parameters corresponding to all teeth;

[0104] Step 1042b: Determine the median of the normal distribution based on the normal distribution parameters;

[0105] Step 1043b: Take the median of the normal distribution as the median of the expected threshold range; take the product of the standard deviation of the normal distribution and the second preset weighting coefficient as the error;

[0106] Step 1044b: Determine the expected threshold range based on the median and error of the expected threshold range.

[0107] Specifically, the transformation parameters corresponding to all teeth can be converted into coordinate points in a spatial coordinate system, where each coordinate point represents a transformation parameter. Normal distribution parameters are then calculated based on the distribution of these coordinate points. For example, the normal distribution function of the spatial coordinate system can be calculated from all coordinate points, and the normal distribution parameters can be determined based on this function. The median and standard deviation of the normal distribution can be determined from these parameters. The product of the standard deviation and a second preset weighting coefficient is used as the error, which in turn yields the upper and lower limits of the expected threshold range. The upper limit of the expected threshold range is the sum of the median and the error, and the lower limit is the difference between the median and the error.

[0108] Alternatively, the method of converting the transformation parameters into normally distributed parameters for calculating the expected threshold can also be achieved by calculating the values ​​of several variables contained in a single transformation parameter separately or in groups, thereby obtaining several expected threshold ranges. The method of grouping variables is roughly the same as the grouping method described above for calculating the expected threshold range based on the average value, and the calculation method for each expected threshold range is roughly the same as the overall calculation method described above. To avoid repetition, it will not be elaborated here.

[0109] like Figure 8 The diagram illustrates how clustering is used to calculate the expected threshold. The steps include:

[0110] Step 1041c: Cluster the transformation parameters corresponding to each individual tooth.

[0111] Step 1042c: Determine the expected threshold range based on the clustering results.

[0112] Specifically, the transformation parameters corresponding to all teeth can be converted into coordinate points in a spatial coordinate system, where each coordinate point represents a transformation parameter. Clustering is then performed based on the distribution of these coordinate points. For example, clustering algorithms can be used to obtain the concentrated regions of all transformation parameters in the spatial coordinate system, and the coordinate range of these concentrated regions can be used as the expected threshold range. For instance, K-Means clustering or mean-shift clustering algorithms can be used to cluster the transformation parameters, obtaining the mean of all transformation parameters, and thus determining the expected threshold range.

[0113] Alternatively, the clustering method for calculating the expected threshold can be implemented by calculating the values ​​of several variables within a transformation parameter separately or in groups, thereby obtaining several expected threshold ranges. The method of grouping variables is roughly the same as the grouping method described above for calculating the expected threshold range based on the average value, and the calculation method for each expected threshold range is roughly the same as the overall calculation method described above. To avoid repetition, it will not be elaborated here.

[0114] In addition to the method of obtaining the expected threshold range through simple calculation, as mentioned above, the actual dentition model and the expected dentition model can be aligned as a whole before calculating the expected threshold range, and the expected threshold range can then be calculated based on the result of the overall alignment. The process for obtaining the expected threshold range in this way is as follows: Figure 9 As shown, it includes:

[0115] Step 1041d: Determine the target teeth based on the transformation parameters corresponding to each individual tooth; wherein, the target teeth are N individual teeth in the actual dentition model whose movement and rotation conform to the expected orthodontic treatment, and N individual teeth in the expected dentition model whose tooth positions are the same as those of the N individual teeth in the actual dentition model; N is greater than 1.

[0116] Specifically, the transformation parameters corresponding to N individual teeth need to be within the designed range of variation. The designed range of variation is the allowable error range in the tooth arrangement design of the expected dentition model. That is, if the degree of displacement and posture change of an individual tooth is within the designed range of variation, then the change of that individual tooth is considered a normal change in the orthodontic process. If the transformation parameters of an individual tooth are not within the designed range of variation, then that individual tooth can be directly identified as not being a target tooth. The results of determining the target tooth based on the transformation parameters are then examined, and individual teeth that are clearly unlikely to meet the expected orthodontic treatment are removed from the results.

[0117] Target teeth can be determined in various ways. For example, the average of the transformation parameters of all individual teeth can be calculated, and the teeth that are closest to the average can be selected as target teeth. Alternatively, the median of the transformation parameters of all individual teeth can be calculated by converting the transformation parameters to a normal distribution function, and the individual teeth whose transformation parameters are close to the median of the normal distribution function can be selected as target teeth. Or, teeth that meet the requirements for movement and rotation and are in line with the expected orthodontic treatment can be selected by clustering.

[0118] Step 1042d: Using the target tooth as an alignment reference, align the actual dentition model and the expected dentition model as a whole.

[0119] Specifically, the N individual teeth belonging to the actual dentition model in the target tooth are formed into a first set; the N individual teeth belonging to the expected dentition model in the target tooth are formed into a second set; the N individual teeth in the first set and the N individual teeth in the second set are aligned as a whole to obtain the overall transformation parameters, which include overall displacement parameters and overall rotation parameters; based on the overall transformation parameters, the actual dentition model and the expected dentition model are aligned as a whole.

[0120] Step 1043d: Determine the expected threshold range based on the actual dentition model and the expected dentition model after overall alignment.

[0121] Specifically, the deviation between individual teeth in the actual dentition model and their corresponding positions in the expected dentition model after alignment is determined. This deviation includes displacement and postural deviations. Based on the deviations of each individual tooth, the expected threshold range is calculated. For example, zero deviation can be used as the median of the expected threshold range; different error values ​​yield different expected threshold ranges. It is clear that a larger deviation results in a worse treatment outcome. Since the median of the expected threshold range is zero, determined by aligning the actual and expected dentition models as a whole, the deviations between corresponding teeth in the two dentition models after alignment can be directly observed, allowing for a direct determination of the treatment outcome without further calculations.

[0122] Regarding how to determine the orthodontic outcome for each individual tooth in an actual dental arch model, at least two methods are included.

[0123] One approach involves determining whether the transformation parameters corresponding to a single tooth are within the expected threshold range. If they are, the treatment outcome for that tooth is considered achieved; otherwise, it is considered unsuccessful. This method yields a relatively straightforward result, with the advantage of requiring less computation and taking less time to determine the outcome.

[0124] Another approach is to categorize the orthodontic outcome of each individual tooth based on the relationship between the transformation parameters corresponding to each tooth and the expected threshold range. The greater the difference between the transformation parameters and the expected threshold range, the lower the grade of the orthodontic outcome for that individual tooth. This method allows for a richer understanding of the orthodontic outcome, specifically determining the degree of deviation for each tooth and classifying teeth into different levels of deviation, such as normal, slight deviation, moderate deviation, or severe deviation, rather than simply classifying all deviations as "deviations" and labeling individual teeth according to the degree of deviation.

[0125] After classifying the treatment outcomes of individual teeth into different grades, the process also includes labeling the individual teeth corresponding to different grades using various markers. For example... Figure 10 The image shown is a schematic diagram of the aligned dentition model. Tooth deviations are represented by grayscale values. In practical applications, different colors can be used to represent different deviation ranges. The deviation of each individual tooth can directly determine whether it meets the treatment's desired outcome; a larger deviation indicates a poorer treatment outcome. In addition to using color to represent different deviation ranges, the degree of tooth offset can also be directly labeled on the aligned dentition model with text, such as "Normal," "Slight Deviation," "Moderate Deviation," or "Severe Deviation," to facilitate review by technicians. These markings allow technicians to more clearly and intuitively determine the treatment outcome of each individual tooth, thus more efficiently identifying teeth that do not meet the expected treatment goals.

[0126] The various implementation methods described above for each step can be freely combined. Whether it's using coordinate axis alignment in step 103 to compare the position and posture of a single tooth, calculating the average value to determine the expected threshold range in step 104, and using the first method of judging whether the transformation parameters are within the expected threshold range to determine the orthodontic effect; or using coordinate axis alignment in step 103 followed by feature point alignment to compare the position and posture of a single tooth, determining the expected threshold range based on the overall alignment result in step 104, and using the second method of judging the difference between the transformation parameters and the expected threshold range to determine different degrees of orthodontic effect achievement, all the above freely combined solutions are within the scope of protection of this application.

[0127] Embodiments of the present invention relate to a method for designing an orthodontic appliance, such as... Figure 11 As shown, it includes the following steps:

[0128] Step 1101: Determine the method for achieving the desired orthodontic effect, and analyze the orthodontic effect of each individual tooth after the preset treatment time.

[0129] Step 1102: For any single tooth where the effect is lower than expected, a supplementary orthodontic plan is designed based on the transformation parameters of all single teeth where the effect is lower than expected.

[0130] This involves designing supplementary orthodontic plans based on the changes in parameters of all individual teeth that are below expectations. This can be done by using the designed supplementary plan to provide additional treatment to the patient's actual dentition during the time between the current and next treatment phase; or by redesigning the subsequent treatment plan based on the changes in parameters of all individual teeth that are below expectations. Typically, for cases where the deviation from the expected treatment outcome is minor, supplementary orthodontic plans are designed during the time between the current and next treatment phases to provide minimal modifications and supplementary treatment without increasing the overall treatment time.

[0131] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0132] This invention relates to an electronic device, such as... Figure 12As shown, it includes at least one processor 1201; and a memory 1202 communicatively connected to the at least one processor 1201; wherein the memory 1202 stores instructions that can be executed by the at least one processor 1201, the instructions being executed by the at least one processor 1201 to enable the at least one processor 1201 to perform the above-described method for determining the effect of orthodontic treatment or to perform the above-described orthodontic appliance design method.

[0133] The memory 1202 and processor 1201 are connected via a bus. This bus can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 1201 and memory 1202. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives and transmits data to the processor 1201.

[0134] Processor 1201 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 1202 can be used to store data used by the processor during operation.

[0135] This invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the method embodiments described above.

[0136] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0137] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method of determining the effectiveness of orthodontic treatment, characterized by, include: Obtain the patient's actual dentition model and expected dentition model; wherein, the actual dentition model is the patient's dentition model obtained by scanning device after a preset treatment time, and the expected dentition model is the dentition model obtained by designing tooth arrangement on the initial dentition model, and the initial dentition model is the dentition model obtained by scanning device when the patient has not undergone orthodontic treatment; The actual dental arch model is then processed by tooth segmentation. The position and orientation of a single tooth in the actual dentition model are compared with the position of a single tooth in the corresponding tooth position in the expected dentition model to obtain the transformation parameters corresponding to the single tooth; wherein, the transformation parameters include: displacement parameters and rotation parameters; Based on the transformation parameters corresponding to each individual tooth, the expected threshold range is calculated, including: The expected threshold range is calculated by taking the median of the transformation parameters corresponding to each individual tooth, and then determined based on the median and error of the expected threshold range; alternatively, the transformation parameters corresponding to each individual tooth are clustered, and the expected threshold range is determined based on the clustering results; or, the expected threshold range is set based on the position and orientation of each individual tooth in the expected dentition model; wherein the minimum value within the expected threshold range is the median of the expected threshold range minus the error, and the maximum value within the expected threshold range is the median of the expected threshold range plus the error; Based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, the orthodontic effect of each individual tooth in the actual dental arch model is determined.

2. The method of determining the effectiveness of the orthodontic treatment according to claim 1, wherein, The step of calculating the median of the expected threshold range based on the transformation parameters corresponding to each individual tooth, and determining the expected threshold range based on the median and error of the expected threshold range, includes: Calculate the first average value of the transformation parameters corresponding to all teeth; use the first average value as the median of the expected threshold range; Calculate the first variance of the transformation parameters corresponding to all teeth and the first average value, and use the product of the first variance and the first preset weighting coefficient as the error; The expected threshold range is determined based on the median and error of the expected threshold range.

3. The method of determining the effectiveness of the orthodontic treatment of claim 1, wherein, The step of calculating the median of the expected threshold range based on the transformation parameters corresponding to each individual tooth, and determining the expected threshold range based on the median and error of the expected threshold range, includes: Calculate the normal distribution parameters of the transformation parameters for all teeth; Based on the normal distribution parameters, determine the median of the normal distribution; use the median of the normal distribution as the median of the expected threshold range; The error is the product of the standard deviation of the normal distribution and the second preset weighting coefficient. The expected threshold range is determined based on the median and error of the expected threshold range.

4. The method of determining treatment effectiveness of claim 3, wherein, The calculation of the normal distribution parameters of the transformation parameters corresponding to all teeth includes: The transformation parameters corresponding to all teeth are converted into coordinate points in a spatial coordinate system, where each coordinate point represents one of the transformation parameters; The normal distribution parameters are calculated based on the distribution of the coordinate points.

5. The method of determining the effectiveness of a treatment according to claim 1, wherein, The clustering of transformation parameters corresponding to each individual tooth includes: The transformation parameters corresponding to all teeth are converted into coordinate points in a spatial coordinate system, where each coordinate point represents one of the transformation parameters; Clustering is performed based on the distribution of the coordinate points.

6. The method of determining the effectiveness of a treatment according to any one of claims 1 to 5, wherein, The transformation parameters include six variables; Three variables represent the displacement parameters, and the other three variables represent the rotation parameters; The expected threshold range includes: the displacement expected threshold range and the rotation expected threshold range; The step of calculating the expected threshold range based on the transformation parameters corresponding to each individual tooth includes: Calculate the expected displacement threshold range based on the displacement parameters; Calculate the expected rotation threshold range based on the rotation parameters; The step of determining the orthodontic outcome of each individual tooth in the actual dentition model based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range includes: The first result is based on the relationship between the displacement parameters corresponding to each individual tooth and the expected displacement threshold range. The second result is based on the relationship between the rotation parameters corresponding to each individual tooth and the expected rotation threshold range; The orthodontic effect of each individual tooth in the actual dental arch model is determined by comprehensively analyzing the first and second results.

7. The method of determining treatment effectiveness of claim 6, wherein, The expected displacement threshold range is calculated based on the displacement parameters, including: Calculate the second average value of the displacement parameters for all teeth; Calculate the second variance of the displacement parameters corresponding to all teeth and the second average value; The expected displacement threshold range is determined based on the second average value and the second variance; Wherein, the minimum value within the displacement expectation threshold range is the difference between the product of the second average value, the second variance, and the third preset weighting coefficient, and the maximum value within the displacement expectation threshold range is the sum of the products of the second average value, the second variance, and the third preset weighting coefficient. The rotation expectation threshold range is calculated based on the rotation parameters, including: Calculate the third average of the rotational parameters for all teeth; Calculate the third difference between the rotational parameters corresponding to all teeth and the third average value; The rotation expectation threshold range is determined based on the third average value and the third difference. The minimum value within the rotation expectation threshold range is the difference between the product of the third average value, the third difference, and the fourth preset weight coefficient, and the maximum value within the rotation expectation threshold range is the sum of the products of the third average value, the third difference, and the fourth preset weight coefficient.

8. The method of determining the effectiveness of a treatment according to any one of claims 1 to 5, wherein, The transformation parameters include M variables; the expected threshold range includes M expected threshold sub-ranges corresponding to the M variables respectively; The step of calculating the expected threshold range based on the transformation parameters corresponding to each individual tooth includes: Based on any one of the transformation parameters, calculate the expected threshold sub-range corresponding to that variable, until the M expected threshold sub-ranges are calculated; The step of determining the orthodontic outcome of each individual tooth in the actual dentition model based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range includes: Identify several variables that are outside the corresponding expected threshold sub-range, and determine the extent to which each of the several variables exceeds its corresponding expected threshold sub-range; The orthodontic outcome of each individual tooth in the actual dentition model is determined based on the variable that exceeds its corresponding expected threshold range the largest.

9. The method of determining the effectiveness of a treatment according to any one of claims 1 to 5, wherein, The step of determining the orthodontic outcome of each individual tooth in the actual dentition model based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range includes: Based on the relationship between the transformation parameters corresponding to each individual tooth and the expected threshold range, the orthodontic effect of each individual tooth is classified into levels; wherein, the greater the difference between the transformation parameters and the expected threshold range, the lower the level of the orthodontic effect of the corresponding individual tooth. Different markings are used to label individual teeth corresponding to different grades.

10. A method of designing an appliance, characterized by, include: Using the method for determining the orthodontic effect as described in any one of claims 1 to 9 above, the orthodontic effect of each individual tooth of the patient after a preset orthodontic time is analyzed. If there is a single tooth for which the orthodontic treatment outcome is lower than expected, a supplementary orthodontic plan will be designed based on the transformation parameters of all such teeth for which the outcome is lower than expected.

Images