Method and device for measuring orthodontic tooth movement data and storage medium

Abstract

The invention provides a method and equipment for measuring orthodontic tooth movement data and a storage medium. The method comprises the following steps: constructing a unified measurement coordinate system; adjusting a tooth digital model before treatment and a tooth digital model after treatment to be under the unified measurement coordinate system; extracting feature points of the target teeth; calculating multi-dimensional movement data: based on coordinates of corresponding feature points on the model before and after treatment, calculating a feature point movement vector, a centroid movement vector of a curved surface formed by the feature points, and angle difference values of a centroid normal and an angular bisector in a YZ plane and an XZ plane; multi-dimensional movement data are output in batches, position changes of feature points before and after treatment are visually displayed, the limitation that only the movement of the overall gravity center of teeth is measured in the prior art is broken through, through multi-dimensional measurement of the feature points, the curved surface centroid and the normal angle, movement of local features such as tooth tips and tooth sockets can be accurately captured, tooth inclination angle changes can be reflected, and the measurement accuracy is improved. And clinical evaluation requirements on fine adjustment are met.

Description

Technical Field

[0001] This application relates to the field of oral and dental orthodontic technology, specifically to a method, device and storage medium for measuring orthodontic tooth movement data, which is particularly suitable for scenarios where specific orthodontic tooth movement data can be measured in batches and in multiple dimensions without CT assistance. Background Technology

[0002] In orthodontic treatment, accurate measurement of tooth movement data is crucial for evaluating treatment effectiveness and adjusting treatment plans. Current mainstream methods for measuring tooth movement data have the following shortcomings: ① Manual measurement method: This method relies on the doctor's experience to manually mark and measure, resulting in a significant deviation between the predicted value and the actual tooth movement value. It is also extremely inefficient and cannot meet the needs of processing a large number of cases.

[0003] ② Traditional 3D data software method: Although it can achieve digital measurement, it has low efficiency in reconstructing irregular curved surfaces of teeth. Processing 3D digital models requires the coordinated operation of multiple software programs, which is cumbersome. Moreover, it can only output the overall movement trend of teeth and cannot accurately reflect the movement details of local teeth, such as the cusps and sockets.

[0004] ③ Three-dimensional overlay measurement method: In order to ensure the accuracy of the measurement, it is necessary to take CT scans to obtain data on deep structures such as alveolar bone. This not only increases the risk of radiation exposure to patients, but also increases the cost of testing and the complexity of operation.

[0005] In existing technologies, such as the orthodontic movement vector detection method disclosed in Chinese patent document CN119454260A, the core idea is to perform preprocessing of the model through trimming, correction, and segmentation, PCA coordinate system registration based on the alveolar bone U-shaped surface, RANSAC global coarse registration, and ICP fine registration, finally calculating the movement vector through the tooth centroid offset. While this method improves the automation of registration, it still has limitations: Firstly, using the overall center of gravity of the tooth as the measurement object cannot accurately capture the local movement of key feature points such as the cusps and sockets, making it difficult to meet the clinical assessment needs for fine angle adjustments of the teeth. Secondly, preprocessing relies on the segmentation of alveolar bone and teeth. In cases with irregular tooth morphology, the segmentation accuracy is easily affected, leading to registration errors. Therefore, it is urgent for those skilled in the art to develop a multi-dimensional and precise measurement method for orthodontic tooth movement data that meets the clinical needs for evaluating subtle adjustments. Summary of the Invention

[0006] To address the aforementioned technical issues, this application provides a method for measuring orthodontic tooth movement data that enables multi-dimensional and precise measurements and meets the clinical needs for evaluating subtle adjustments.

[0007] To achieve the above objectives, a method for measuring orthodontic tooth movement data is provided in the first aspect of this application, the method comprising the following steps: Step 10: Construct a unified measurement coordinate system and adjust the pre-treatment and post-treatment digital tooth models to the unified measurement coordinate system; Step 20: Extract feature points of the target teeth: The target teeth include maxillary teeth 1, 3, 6, and 7, and mandibular teeth 1 and 6; among them, a total of 29 feature points are extracted from the maxillary teeth and a total of 16 feature points are extracted from the mandibular teeth. Step 30: Calculate multi-dimensional movement data: Based on the coordinates of corresponding feature points on the pre- and post-treatment models, calculate the feature point movement vector, the centroid movement vector of the surface formed by the feature points, and the angle difference of the key reference line in the YZ plane and XZ plane. Step 40: Output the multi-dimensional movement data in batches and visualize the positional changes of feature points before and after treatment.

[0008] Furthermore, the origin of the unified measurement coordinate system is set as the midpoint of the line connecting the cusps of the mandibular incisors. The X-axis extends along the left-right direction of the oral cavity and points to the left as the positive direction. The Y-axis extends along the front-back direction of the oral cavity and is away from the molar area as the positive direction. The Z-axis extends along the vertical direction of the teeth and points to the occlusal surface as the positive direction.

[0009] Further, in step 20, the feature points of the maxillary teeth specifically include: feature points 6, 16, and 28 of maxillary tooth No. 1, feature points 5, 15, and 27 of maxillary tooth No. 3, feature points 3, 4, 14, 22, and 23 of maxillary tooth No. 6, and feature points 1, 2, 13, and 21 of maxillary tooth No. 7; the feature points of the mandibular teeth specifically include: feature points 3, 8, and 15 of mandibular tooth No. 1, and feature points 1, 2, 7, 11, and 12 of mandibular tooth No. 6.

[0010] Further, in step 30, the feature point movement vector is calculated as follows: based on the coordinate values ​​of each pair of corresponding feature points before and after treatment, the difference between the coordinates of the feature points after treatment and the coordinates of the feature points before treatment is calculated to obtain the feature point movement vector. =(x2-x1,y2-y1,z2-z1), where (x1,y1,z1) are the coordinates of the feature point before treatment, and (x2,y2,z2) are the coordinates of the feature point after treatment; the magnitude of the movement vector represents the distance the feature point moves, and the direction represents the direction of the feature point's movement.

[0011] Furthermore, the curved surface includes a triangular surface and a quadrangular surface; wherein, feature points 1, 2, and 21 of the maxillary tooth No. 7 constitute a triangular surface, and feature points of the maxillary tooth No. 6 and the mandibular tooth No. 6 constitute a quadrangular surface.

[0012] Furthermore, the centroid movement vector is calculated as follows: A triangular surface is constructed using the feature points of the maxillary tooth No. 7, and the vector is calculated using formula C(…). , , Calculate the centroid coordinates of the triangular surface before and after treatment, and then calculate the difference between the centroid coordinates after treatment and the centroid coordinates before treatment to obtain the centroid movement vector of the surface. The feature points of maxillary tooth 6 and mandibular tooth 6 form a quadrangular surface. The coordinates of the centroid of the surface are calculated by mesh generation. Then, the coordinate difference of the centroid before and after treatment is calculated to obtain the centroid movement vector of the quadrangular surface. The centroid movement vector reflects the overall movement trend of the local area of ​​the tooth (such as the occlusal surface). Furthermore, for the three characteristic points of maxillary tooth 1, maxillary tooth 3, and mandibular tooth 1, two straight lines are formed by the characteristic point of their cusps and the two characteristic points connecting the contact points between the inner and outer soft tissues and the teeth. An angle bisector with the cusps as the vertex is constructed through these two straight lines. The angle bisectors before and after treatment are projected onto the YZ plane and XZ plane respectively, and the angle difference of the projection lines in each plane is calculated to more intuitively evaluate the angle change of the teeth.

[0013] Further, in step 30, the key reference line includes the centroid normal of the surface and the angle bisector of the line connecting the three feature points. The angle difference of the centroid normal is calculated as follows: 1) Extract the normal L1 of the surface before treatment and the normal L2 of the surface after treatment; 2) Project L1 and L2 onto the YZ plane and XZ plane respectively to obtain the plane projection lines; 3) Calculate the angles between the projection lines of each projection plane to obtain the angular differences in the YZ plane. △Q 法yz The angular difference ΔQ in the XZ plane 法Xz ; The angle difference between the angle bisectors is calculated as follows: 1) Extract the angle bisector L3 of the angle formed by connecting the three feature points before treatment and the angle bisector L4 of the angle formed by connecting the corresponding feature points after treatment; 2) Project L3 and L4 onto the YZ plane and XZ plane respectively to obtain the plane projection lines; 3) Calculate the angles between the projection lines of each projection plane to obtain the angular differences in the YZ plane. △Q 平yz The angular difference ΔQ in the XZ plane 平Xz .

[0014] Furthermore, prior to step 10, the following steps are also included: Step 09: Construct a digital model of the teeth. Use an open-source intraoral scanning device to collect intraoral 3D data before and after orthodontic treatment to generate a digital model of the teeth before and after treatment, without the need for CT scans.

[0015] In a second aspect of this application, an electronic device is provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method of the first aspect of the embodiments of this application.

[0016] In a third aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method of the first aspect of the embodiments of this application, wherein the computer program supports batch data processing.

[0017] Compared with the prior art, the advantages of this application are as follows: 1) No CT assistance required, high safety: Data is obtained only through open-source intraoral scanning, eliminating the need for CT scans, avoiding radiation exposure for patients, and reducing detection costs and operational complexity.

[0018] 2) More comprehensive measurement dimensions and higher accuracy: Breaking through the limitations of existing technologies that "only measure the overall center of gravity movement of the teeth", through multi-dimensional measurement of feature points, surface centroids, and key reference line angle differences, it can accurately capture the movement of local features such as cusps and sockets, and reflect changes in tooth tilt angles, meeting the clinical needs for evaluating subtle adjustments.

[0019] 3) High batch processing efficiency: Based on Rhino7 software and Grasshopper plugin, an automated program is written to realize the batch import of multiple cases and multiple teeth data. Combined with manually set feature points and software extraction and calculation, it greatly improves the efficiency of clinical data processing and is suitable for centralized analysis of a large number of cases.

[0020] 4) The coordinate system benchmark is more clinically relevant: The origin is the midpoint of the line connecting the cusps of the mandibular incisors. The coordinate axis direction is defined in combination with the oral anatomy structure, avoiding the complexity of alveolar bone segmentation that existing technologies rely on. It is more adaptable to irregular tooth shapes and the measurement benchmark is more stable. Attached Figure Description

[0021] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 This is a flowchart of the orthodontic tooth movement data measurement method according to an embodiment of this application; Figure 2This is a schematic diagram of a unified measurement coordinate system constructed for an embodiment of this application. The origin of the unified measurement coordinate system is set as the midpoint of the line connecting the cusps of the mandibular incisors. The X-axis extends along the left-right direction of the oral cavity and points to the left as the positive direction. The Y-axis extends along the front-back direction of the oral cavity and is away from the molar area as the positive direction. The Z-axis extends along the vertical direction of the teeth and points to the occlusal surface as the positive direction. Figure 3 This is a schematic diagram of the distribution structure of the maxillary teeth and the extracted feature points in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the feature point movement vector and the centroid movement vector of the triangular face formed by feature points 1, 2, and 21, using the maxillary right right tooth No. 7 as an example, in another embodiment of this application. Figure 5 The angle difference ΔQ between the centroid normal of the triangle formed by feature points 1, 2, and 21 before and after treatment of maxillary right tooth 7 in another embodiment of this application in the YZ and XZ planes. yz , △Q Xz A schematic diagram; Figure 6 This is a schematic diagram illustrating the feature point movement vector and the centroid movement vector of the quadrangular surface formed by feature points 3, 4, 22, and 23, using the maxillary right right tooth No. 6 as an example, in another embodiment of this application. Figure 7 The angle difference ΔQ between the centroid normals of the quadrangular surface formed by feature points 3, 4, 22, and 23 before and after treatment of maxillary right tooth #6 in another embodiment of this application in the YZ and XZ planes. yz , △Q Xz A schematic diagram; Figure 8 In another embodiment of this application, taking the maxillary right third tooth as an example, the feature point movement vector is shown. Two straight lines are formed by feature point 5 (cusp) and two feature points 15 and 27 connecting the contact points between the inner and outer soft tissues and the tooth. An angle bisector with the cusp as the vertex is constructed. The angle bisector before and after treatment is projected onto the YZ plane and XZ plane respectively, and the angle difference of the projection line is calculated to more intuitively evaluate the angle change of the tooth. Figure 9 In another embodiment of this application, taking the first tooth on the right side of the maxilla as an example, the feature point movement vector is shown. Two straight lines are formed by feature point 6 (cusp) and two feature points 16 and 28 connecting the contact points between the inner and outer soft tissues and the tooth. An angle bisector with the cusp as the vertex is constructed. The angle bisector before and after treatment is projected onto the YZ plane and XZ plane respectively, and the angle difference of the projection line is calculated to more intuitively evaluate the angle change of the tooth. Figure 10 This is a schematic diagram of the distribution structure of mandibular teeth and their extracted feature points in another embodiment of this application; Figure 11 This is a schematic diagram illustrating the feature point movement vector and the centroid movement vector of the four-corner curved surface formed by feature points 1, 2, 11, and 12, using the mandibular tooth No. 6 as an example, in another embodiment of this application. Figure 12 The feature point movement vector shown in another embodiment of this application is an example of the right mandibular tooth No. 1. Two straight lines are formed by feature point 3 (cusp) and two feature points 8 and 15 connecting the contact points between the inner and outer soft tissues and the tooth. An angle bisector with the cusp as the vertex is constructed. The angle bisector before and after treatment is projected onto the YZ plane and XZ plane respectively, and the angle difference of the projection line is calculated to more intuitively evaluate the angle change of the tooth. Figure 13 This is a schematic diagram of the structure of a terminal device or server suitable for implementing the embodiments of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the appendices in the embodiments of this disclosure will be described below. Figure 1-13 The technical solutions in the embodiments of this disclosure are clearly and completely described. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0023] Furthermore, the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0024] Example 1 This application aims to provide a CT-free, batch-processable, and multi-dimensionally accurate orthodontic tooth movement data measurement solution. Its core process, comprised of five steps—model construction, coordinate system establishment, feature point extraction, multi-dimensional calculation, and result output—overcomes the limitations of existing technologies that only measure the overall center of gravity movement of the teeth. Each step is interconnected: open-source intraoral scanning constructs the model as the data foundation; a unified coordinate system serves as the measurement benchmark; feature point extraction is the prerequisite for accurate calculation; multi-dimensional data calculation is the core value proposition; and batch output and visualization ensure clinical application. Ultimately, this achieves comprehensive measurement encompassing local details, overall trends, and angular changes, fully meeting the clinical needs for evaluating subtle adjustments in orthodontic teeth.

[0025] like Figure 1 As shown in the figure, this application provides a method for measuring orthodontic tooth movement data, the method comprising the following steps: Step 09: Construct a digital model of the teeth. Use an open-source intraoral scanning device to collect intraoral 3D data before and after orthodontic treatment to generate a digital model of the teeth before and after treatment, without the need for CT scans.

[0026] Specifically, this step provides the raw data support for the entire measurement plan, and is implemented as follows: Equipment and Operation: Open-source intraoral scanning equipment, such as an intraoral scanner based on the triangulation principle, was used to collect three-dimensional intraoral data of the same patient before and after orthodontic treatment. During the scanning process, the equipment captured tooth surface contour information through optical lenses, generated point cloud data in real time, and reconstructed a continuous tooth surface model from the point cloud data using the equipment's built-in algorithm. Finally, a digital model of the tooth before treatment (denoted as Model M1) and a digital model of the tooth after treatment (denoted as Model M2) were output.

[0027] Compared to existing technologies that require CT-assisted acquisition of alveolar bone data, this step eliminates the need for CT scans, thus avoiding ionizing radiation exposure to patients. It also eliminates the costs associated with CT equipment rental / use and the complex preprocessing steps of "tooth-alveolar bone segmentation," reducing operational complexity and the threshold for clinical application. The selection of open-source intraoral scanning equipment ensures the compatibility and repeatability of data acquisition, allowing different clinical institutions to acquire data based on a unified open-source standard, laying a foundation for data consistency in subsequent batch processing.

[0028] Step 10: Construct a unified measurement coordinate system and adjust the pre-treatment and post-treatment digital tooth models to the unified measurement coordinate system; Specifically, this step addresses the problem of measurement errors caused by inconsistent model benchmarks before and after treatment by defining a coordinate system that conforms to the oral anatomy. The specific implementation is as follows: Coordinate system definition: Reference Figure 2 As shown, the origin O of the coordinate system is set as the midpoint of the line connecting the cusps of the two mandibular incisors, that is, the geometric midpoint of the line connecting the cusps of the mandibular central incisors; X-axis: Extending along the left-right direction of the oral cavity, with the left side of the patient as the positive direction, corresponding to the buccal-lingual horizontal dimension of the tooth arrangement). Y-axis: Extends along the front-to-back direction of the oral cavity, with the direction furthest from the molar area being positive, corresponding to the mesiodistal dimension of tooth arrangement; Z-axis: Extends vertically along the teeth, pointing towards the occlusal surface of the teeth as the positive direction, corresponding to the vertical dimension of the tooth arrangement.

[0029] Model adjustment operation: Import models M1 and M2 using 3D modeling software such as Rhino7. Based on the origin and coordinate axis directions of the above coordinate system, perform translation and rotation operations on the two models respectively, so that the midpoint of the line connecting the mandibular incisors of models M1 and M2 coincides with the origin O, and the tooth arrangement direction is consistent with the X, Y, and Z axis directions, thus achieving the registration of the two models in a unified coordinate system.

[0030] Existing technologies mostly rely on the "alveolar bone U-shaped surface" or "overall tooth contour" as the registration benchmark. For cases with irregular tooth morphology, such as tooth defects or rotated teeth, the registration accuracy is easily affected by segmentation errors. However, this embodiment uses the "midpoint of the line connecting the cusps of the mandibular incisors" as the origin. This benchmark point has a fixed position in the oral cavity and clear anatomical landmarks. It does not need to rely on alveolar bone data, has stronger adaptability to irregular tooth morphology, and the measurement benchmark is more stable. The establishment of a unified coordinate system ensures the comparability of coordinate data between the pre- and post-treatment models, providing a unique benchmark for subsequent feature point coordinate calculation and movement vector analysis, thus controlling measurement errors from the source.

[0031] Step 20: Extract feature points of the target teeth: The target teeth include maxillary teeth 1, 3, 6, and 7, and mandibular teeth 1 and 6; among them, a total of 29 feature points are extracted from the maxillary teeth and a total of 16 feature points are extracted from the mandibular teeth. Specifically, this step involves selecting key feature points of critical teeth to provide "precise anchor points" for subsequent multi-dimensional measurements. The specific implementation is as follows: Target tooth selection: Based on key assessment subjects for tooth movement in orthodontic clinical practice, the target teeth were determined to be the maxillary teeth 1 (central incisor), 3 (canine), 6 (first molar), and 7 (second molar), and the mandibular teeth 1 (central incisor) and 6 (first molar). This selection was based on clinical experience: these teeth move frequently during orthodontic treatment and have a significant impact on occlusion; their movement data are core indicators for evaluating treatment effectiveness.

[0032] For feature point extraction rules, please refer to the appendix. Figure 3 Schematic diagram and appendix of maxillary tooth feature points Figure 10 A schematic diagram of mandibular tooth feature points. An automated extraction program was written using the Grasshopper plugin to extract the following feature points in models M1 and M2 under a unified coordinate system. The feature point numbers correspond to key anatomical structures on the tooth surface, such as cusps, sockets, and soft tissue contact points. Maxillary teeth (29 feature points in total): Feature points 6 (cusp), 16 (medial soft tissue contact point), and 28 (lateral soft tissue contact point) were extracted from maxillary tooth 1; feature points 5 (cusp), 15 (medial soft tissue contact point), and 27 (lateral soft tissue contact point) were extracted from maxillary tooth 3; feature points 3 and 4 (occlusal cusp), 14 (fossa), and 22 and 23 (buccal and lingual contact points) were extracted from maxillary tooth 6; feature points 1 and 2 (occlusal cusp), 13 (fossa), and 21 (buccal contact point) were extracted from maxillary tooth 7. The remaining feature points were evenly distributed according to the anatomical divisions of the tooth surface to ensure coverage of key movement areas.

[0033] Mandibular teeth (16 feature points in total): Feature points 3 (cusp), 8 (medial soft tissue contact point), and 15 (lateral soft tissue contact point) are extracted from mandibular tooth 1; Feature points 1, 2 (occlusal cusp), 7 (fossa), and 11 and 12 (buccal and lingual contact points) are extracted from mandibular tooth 6, and the remaining feature points are similarly used to cover key areas.

[0034] Existing technologies mostly use the "overall center of gravity of the tooth" as the measurement object, which cannot capture the subtle movements of local features such as cusps and sockets; however, the feature points extracted in this embodiment directly correspond to the key parts of tooth function, providing a precise carrier for subsequent "local movement measurement" and meeting the clinical needs for evaluating subtle adjustments. Automated extraction programs replace manual labeling, avoiding labeling bias caused by doctors' experience, and enable batch extraction of feature points from multiple cases and multiple teeth, significantly improving data processing efficiency.

[0035] Step 30: Calculate multi-dimensional movement data: Based on the coordinates of the corresponding feature points on the pre- and post-treatment models, calculate the feature point movement vector, the centroid movement vector of the surface formed by the feature points, and the angle difference of the key reference line in the YZ plane and XZ plane. The key reference line includes the centroid normal of the surface and the angle bisector of the line connecting the three feature points. Specifically, this step achieves comprehensive measurement of "local details + overall trend + angle change" through three-dimensional calculation of "feature point movement vector + surface centroid movement vector + angle difference of key reference lines". The specific implementation is as follows: 1. Calculate feature point movement vectors—precise capture of subtle local movements. Calculation method: Under a unified coordinate system, obtain the coordinates (x1, y1, z1) of a feature point in model M1 and the coordinates (x2, y2, z2) of the corresponding feature point in model M2, and calculate the translation vector using a formula. =(x2-x1,y2-y1,z2-z1); where the magnitude of vector V, |V|, represents the distance the feature point moves; the direction of vector V is determined by the signs of the x, y, and z components, such as a positive x component indicating a move to the left, and a positive z component indicating a move towards the occlusal surface.

[0036] For example, see Appendix Figure 4 A schematic diagram of the movement vector of the feature point of the maxillary right tooth 7. The arrows in the diagram represent the movement vectors of feature points 1, 2, 13, and 21, respectively. It can be seen that feature point 1 moves in the positive X-axis direction and in the positive Z-axis direction (occlusal surface), accurately reflecting the subtle movement of a single cusp. (Attached) Figure 6 Maxillary right 6th tooth, accessory Figure 11 Similarly, the movement vector of the corresponding tooth feature point is shown for mandibular tooth #6.

[0037] This method directly quantifies the movement distance and direction of a single key feature point, overcoming the limitations of existing technologies in capturing local movements of tooth cusps and sockets. It provides data support for clinical assessment of whether teeth have reached the expected fine positions. For example, in orthodontic treatment, when it is necessary to adjust the contact relationship between the canine cusp and adjacent teeth, this vector can directly provide feedback on whether the cusp movement has met the target.

[0038] 2. Based on the calculation of the feature point movement vector, combined with the calculation of the surface centroid movement vector, the overall trend of the local area is reflected; Based on the distribution of feature points, the key areas of the target tooth surface are constructed as triangular or quadrangular surfaces: Triangular surface: Characteristic points 1, 2, and 21 of maxillary tooth 7 are formed, corresponding to a functional cusp region on the occlusal surface; The four-cornered curved surface is formed by feature points 3, 4, 22, and 23 of the maxillary tooth 6, corresponding to the central area of ​​the occlusal surface of the first molar; and by feature points 1, 2, 11, and 12 of the mandibular tooth 6, corresponding to the functional area of ​​the occlusal surface of the mandibular first molar.

[0039] Calculation method: Centroid of the triangle: determined by formula C( , , Calculate the centroids C1 and C2 of the triangular faces in models M1 and M2, and the centroid movement vector. =(C 2x -C 1x C 2y -C 1y C 2z -C 1z ); Centroids of the Quadrilateral Surface: The quadrilateral surface is divided into multiple small triangular faces using a meshing method. The centroid of each small triangular face is calculated and weighted averaged, with the weight being the area of ​​the small triangular face. This yields the overall centroids C1 and C2 of the quadrilateral surface. Then, the translation vector Vc is calculated.

[0040] For example, attached Figure 4 In the model, the centroidal movement vector of the triangular surfaces (1, 2, 21) of the maxillary tooth 7 is (0.6mm, -0.2mm, 0.4mm), reflecting the overall movement of the functional cusp region of this tooth's occlusal surface to the left (positive X), away from the molar region (negative Y), and towards the occlusal surface (positive Z); (Attached) Figure 6 , 11 Similarly, the centroid movement vector of the four-cornered curved surface is shown.

[0041] By using the centroid to reflect the overall movement trend of a local area, such as the occlusal surface, we can avoid the impact of point deviation caused by abnormalities of a single feature point, such as scanning noise, on the overall judgment of tooth movement, thus making the measurement results more stable and clinically valuable.

[0042] 3. Calculate the difference in normal angles and the difference in angle bisector angles to further quantify the changes in tooth tilt. Centroid normal angle difference (triangular face / quadrilateral surface): The specific steps are as follows: Extract the normal L1 of the surface in model M1. This L1 is perpendicular to the surface of the surface and points to the outside of the surface and the normal L2 of the corresponding surface in model M2. Projecting L1 and L2 onto the YZ plane (perpendicular to the X-axis, reflecting the mesial and vertical tilt of the teeth) and the XZ plane (perpendicular to the Y-axis, reflecting the buccal and lingual tilt of the teeth) respectively, we obtain the projection line L. 1-yz L 2-yz (YZ plane) and L 1-xz L 2-xz (XZ plane); Calculate the angle between the projection lines: the angle difference △Q in the YZ plane. 法yz =|∠L 1-yz -∠L 2-yz |, Angle difference △Q in the XZ plane 法xz =|∠L 1-xz -∠L 2-xz |

[0043] For example, attached Figure 5 In the (difference in angle between the normals of the triangular plane of maxillary tooth #7), such as △Q 法yz =0.8°, indicating that the tilt angle of the surface changes by 0.8° in the near-far-mid direction; △Q 法xz =1.5°, indicating a 1.5° change in tilt from the cheek to the vertical direction; Appendix Figure 7 (The four-corner curved surface of maxillary tooth #6) similarly demonstrates the difference in normal angles.

[0044] Angle bisector difference (maxillary teeth 1 and 3, and mandibular tooth 1): Based on the anatomical characteristics of the "cusp-soft tissue contact" of these teeth, the specific steps are as follows: Taking the maxillary third tooth as an example (see appendix) Figure 8 ), take feature points 5 (cusp point), 15 (medial soft tissue contact point), 27 (lateral soft tissue contact point), and connect 5-15 and 5-27 to form two straight lines L3 and L4; Construct the angle bisector L with cusp 5 as the vertex. c (Bisects ∠L3L4); The angle bisector L in models M1 and M2 c1 L c2 Project the lines onto the YZ and XZ planes respectively, and calculate the angle ΔQ between the projection lines. 平yz , △Q 平xz .

[0045] For example, attached Figure 8 In the middle, for example, the angle difference between the bisectors of the angle of the maxillary third tooth in the YZ plane is _____. △Q 平yz =1.0°, reflecting the degree of tilt adjustment of the cusp in the mesiodistal direction; Appendix Figure 9 (Maxillary tooth #1), appendix Figure 12 Similarly, the angle difference of the angle bisector is shown for (mandibular tooth #1).

[0046] Existing technologies cannot accurately quantify changes in tooth tilt angles. This embodiment, however, directly converts the key clinical indicator of "tooth tilt" into a specific numerical value by using the angle difference between key reference lines such as the normal and the angle bisector. This meets the clinical needs for assessing subtle adjustments such as "tooth uprightness" and "occlusal slope angle," for example, in orthodontic treatment requiring correction of mesial tilt of molars, △Q yz It can directly provide feedback on the tilt correction effect.

[0047] Step 40: Output the multi-dimensional movement data in batches and visualize the positional changes of feature points before and after treatment, ensuring the efficiency of clinical application.

[0048] Batch output: An automated program based on Rhino7 software and Grasshopper plugin is written to batch export data such as "feature point movement vector (including distance and direction), centroid movement vector, normal angle difference, and angle bisector angle difference" calculated in step 30 to an Excel spreadsheet in the format of "patient ID-tooth number-measurement index". It supports processing 10-100 cases at the same time (each case contains 6-7 target teeth), and the processing time is significantly shortened compared to traditional software collaborative operation.

[0049] Visualization: In 3D modeling software, feature points in models M1 and M2 are marked with different colors (e.g., red for M1 and blue for M2), and the direction and length of the movement vector are marked with arrows. At the same time, dynamic comparison views are used to show the position and tilt changes of the tooth surface before and after treatment, such as the angle difference of the normal projection line, to intuitively present the tooth movement effect.

[0050] Batch output solves the problem of low efficiency in the existing "manual measurement / single case processing" and meets the needs of large hospitals or orthodontic institutions for centralized analysis of multiple cases; visual display replaces the traditional "single data table presentation" method, helping doctors quickly understand the specific position and angle changes of tooth movement, reducing the threshold for data interpretation, and providing intuitive basis for adjusting treatment plans.

[0051] In summary, through the synergistic effect of the aforementioned technical features, this application achieves the following core effects: Safety: No CT scan required, avoiding radiation exposure and reducing clinical risks; Accuracy: Multi-dimensional measurement (feature points + surface centroid + reference line angle difference) covers local details, regional trends and tilt changes, fully ensuring the overall assessment needs of subtle clinical adjustments; High efficiency: Automated programs enable batch processing, significantly improving data processing efficiency; Stability: The unified measurement coordinate system is based on anatomical landmarks, adaptable to irregular tooth morphology, and the measurement benchmark is reliable.

[0052] The technical solution proposed in this application effectively addresses the shortcomings of existing orthodontic tooth movement measurement methods, providing a safe, accurate, and efficient digital measurement method for clinical orthodontics, and has significant clinical application value.

[0053] Example 2 Figure 13 A schematic diagram of the structure of an electronic device or server suitable for implementing embodiments of this application is shown.

[0054] like Figure 13 As shown, the electronic device or server includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage section 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the terminal device or server. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0055] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0056] Specifically, the electronic device in this embodiment includes: Processor (e.g., CPU501): Used to execute computer programs and implement all operations from step 09 to step 40 of the above embodiment, such as model registration, feature point extraction, and data calculation; Memory (such as ROM502, RAM503, storage section 508): used to store open-source intraoral scan data, digital tooth models, feature point coordinates, calculation results and automation programs; Input section 506 (e.g., keyboard, mouse): used by doctors to manually adjust the model registration accuracy or select the target tooth; Output section 507 (e.g., LCD monitor, printer): used to display visualization results or print batch data tables; Communication section 509 (e.g., LAN card): used to receive 3D data transmitted from open-source intraoral scanning equipment, or to interface with the hospital information system (HIS) to achieve data sharing.

[0057] Program execution: The written automation program (based on Python or Grasshopper scripting language) is stored in the memory. After the processor calls the program, it can automatically complete the entire process of data import, model adjustment, feature point extraction, calculation and output without manual intervention.

[0058] Specifically, according to embodiments of this application, the above method flow steps can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined in the system of this application.

[0059] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic interference signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0060] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0061] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0062] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium stores one or more programs that are used by one or more processors to execute the methods described in this application.

[0063] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for measuring orthodontic tooth movement data, characterized in that, Includes the following steps: Step 10: Construct a unified measurement coordinate system: Adjust the pre-treatment and post-treatment digital tooth models to the unified measurement coordinate system; Step 20: Extract feature points of the target teeth: The target teeth include maxillary teeth 1, 3, 6, and 7, and mandibular teeth 1 and 6; among them, a total of 29 feature points are extracted from the maxillary teeth and a total of 16 feature points are extracted from the mandibular teeth. Step 30: Calculate multi-dimensional movement data: Based on the coordinates of corresponding feature points on the pre- and post-treatment models, calculate the feature point movement vector, the centroid movement vector of the surface formed by the feature points, and the angle difference of the key reference line in the YZ plane and XZ plane. Step 40: Output the multi-dimensional movement data in batches and visualize the positional changes of feature points before and after treatment.

2. The method for measuring orthodontic tooth movement data according to claim 1, characterized in that, The origin of the unified measurement coordinate system is set at the midpoint of the line connecting the cusps of the mandibular incisors. The X-axis extends along the left-right direction of the oral cavity and points to the left as the positive direction. The Y-axis extends along the front-back direction of the oral cavity and points away from the molar area as the positive direction. The Z-axis extends along the vertical direction of the teeth and points to the occlusal surface as the positive direction.

3. The method for measuring orthodontic tooth movement data according to claim 2, characterized in that, In step 20, the feature points of the maxillary teeth specifically include: feature points 6, 16, and 28 of maxillary tooth No. 1, feature points 5, 15, and 27 of maxillary tooth No. 3, feature points 3, 4, 14, 22, and 23 of maxillary tooth No. 6, and feature points 1, 2, 13, and 21 of maxillary tooth No. 7; the feature points of the mandibular teeth specifically include: feature points 3, 8, and 15 of mandibular tooth No. 1, and feature points 1, 2, 7, 11, and 12 of mandibular tooth No.

6.

4. The method for measuring orthodontic tooth movement data according to claim 1, characterized in that, In step 30, the feature point movement vector is calculated as follows: based on the coordinate values ​​of each pair of corresponding feature points before and after treatment, the difference between the coordinates of the feature points after treatment and the coordinates of the feature points before treatment is calculated to obtain the feature point movement vector. =(x2-x1,y2-y1,z2-z1), where (x1,y1,z1) are the coordinates of the feature point before treatment, and (x2,y2,z2) are the coordinates of the feature point after treatment; the magnitude of the movement vector represents the distance the feature point moves, and the direction represents the direction of the feature point's movement.

5. The method for measuring orthodontic tooth movement data according to claim 1, characterized in that, The curved surface includes a triangular surface and a quadrangular surface; wherein, feature points 1, 2, and 21 of the maxillary tooth No. 7 form a triangular surface, and feature points 3, 4, 22, and 23 of the maxillary tooth No. 6 and feature points 1, 2, 11, and 12 of the mandibular tooth No. 6 form a quadrangular surface.

6. The method for measuring orthodontic tooth movement data according to claim 5, characterized in that, The centroid movement vector is calculated as follows: calculate the centroid coordinates of the surface before and after treatment, and then calculate the difference between the centroid coordinates after treatment and the centroid coordinates before treatment.

7. The method for measuring orthodontic tooth movement data according to claim 1, characterized in that: In step 30, the key reference line includes the centroid normal of the surface and the angle bisector of the line connecting the three feature points. The angle difference of the centroid normal is calculated as follows: 1) Extract the normal L1 of the surface before treatment and the normal L2 of the surface after treatment; 2) Project L1 and L2 onto the YZ plane and XZ plane respectively to obtain the plane projection lines; 3) Calculate the angles between the projection lines of each projection plane to obtain the angular differences in the YZ plane. △Q 法yz The angular difference ΔQ in the XZ plane 法Xz ; The angle difference between the angle bisectors is calculated as follows: 1) Extract the angle bisector L3 of the angle formed by connecting the three feature points before treatment and the angle bisector L4 of the angle formed by connecting the corresponding feature points after treatment; 2) Project L3 and L4 onto the YZ plane and XZ plane respectively to obtain the plane projection lines; 3) Calculate the angles between the projection lines of each projection plane to obtain the angular differences in the YZ plane. △Q 平yz The angular difference ΔQ in the XZ plane 平Xz .

8. The method for measuring orthodontic tooth movement data according to claim 1, characterized in that, Before step 10, the following is also included: Step 09, Constructing a digital tooth model: Use an open-source intraoral scanning device to collect intraoral 3D data before and after orthodontic treatment, generating a digital tooth model before treatment and a digital tooth model after treatment, without the need for CT scans.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8, and the computer program supports batch data processing.

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