A method for quantifying dental arch irregularities

Abstract

The application discloses a kind of dental arch arrangement irregularity quantitative analysis method, it is related to oral digital analysis and orthodontic diagnosis technical field.The application is first based on the extracted occlusion key anatomical landmark point and carries out overall dental arch curve fitting to dental arch;Again, the same group of landmark points is carried out local dental arch curve fitting;By calculating the difference of overall dental arch curve and local dental arch curve on the corresponding position, and using the normalized root mean square error to the difference is quantified, to obtain the numerical index reflecting the degree of dental arch irregularity.To improve the comparability between different individuals, after the error value is normalized, it is divided into normal, mild, moderate and severe irregular grade according to the preset threshold interval.The application simultaneously reflects the overall trend of dental arch and local arrangement deviation;Without manual intervention, it is suitable for large-scale clinical analysis;And high sensitivity to tooth crowding, rotation and local arrangement disorder, can be used for pre-treatment evaluation, efficacy monitoring and dentition data analysis.

Description

Technical Field

[0001] This invention relates to the field of oral digital analysis and orthodontic diagnosis technology, and in particular to a method for quantitative analysis of irregularity in dental arch alignment. Background Technology

[0002] With the development of 3D intraoral scanning, cone-beam computed tomography (CBCT), and digital dental modeling technology, the acquisition of dental arch morphology data has gradually shifted from traditional plaster models to high-precision 3D digital models. Digital dental models not only improve data acquisition efficiency but also provide a technological foundation for the objective analysis, storage, and computation of dental arch morphology. In orthodontic clinical practice, the morphology of the dental arch and the alignment of teeth are important factors affecting diagnosis, treatment plan design, and efficacy evaluation. Therefore, the quantitative description of irregularities in dental arch alignment has significant clinical and research value.

[0003] Currently, the assessment methods for tooth alignment irregularities can be mainly divided into the following categories:

[0004] The first category consists of assessment methods based on linear distance or gap measurements. These methods typically reflect tooth alignment by measuring the offset distance of contact points between adjacent teeth or the distance of tooth misalignment. For example, Little's Irregularity Index describes the degree of crowding by linearly summing the displacements of adjacent contact points of anterior teeth. These methods are simple to implement and widely used clinically, but their measurement range is usually limited to local tooth segments and only considers two-dimensional or one-dimensional linear offsets, making it difficult to reflect tooth rotation, nonlinear misalignment, and changes in the overall morphology of the dental arch.

[0005] The second category is representative measurement methods based on key teeth or local tooth segments. These methods typically select incisors, canines, or specific tooth segments as representatives, indirectly reflecting the dental arch alignment by measuring their position, angles, or relative relationships. However, due to significant differences in dental arch morphology and alignment irregularities across different tooth segments, a few teeth or local areas cannot fully reflect the overall dental arch alignment characteristics, and the results are easily affected by individual differences and the method of point selection.

[0006] The third category is the overall morphological analysis method based on dental arch curve fitting. This type of method typically uses polynomial curves, circular arcs, or spline curves to fit the dental arch as a whole, describing the arch's morphological characteristics through curve parameters or fitting residuals. This method can reflect the overall geometric trend of the dental arch to some extent, but because it only uses a single curve model, it often focuses on macroscopic morphological description and has limited ability to depict local details such as tooth crowding, rotation, and local misalignment. Furthermore, when there is significant misalignment in a local area of ​​the dental arch, the overall fitted curve is easily affected by local outliers, thus reducing the stability of the evaluation results.

[0007] Furthermore, in existing technologies, different studies or clinical applications often employ different measurement scales, fitting methods, or evaluation indicators, lacking a unified normalization and standardization mechanism. This results in insufficient consistency and comparability of evaluation results when comparing different individuals, different dental arch sizes, or different data sources, hindering large-scale data analysis and objective evaluation.

[0008] In summary, existing technologies generally have the following shortcomings in assessing dental arch irregularities:

[0009] (1) It focuses on local linear measurement, which makes it difficult to reflect the overall shape of the dental arch and local arrangement deviations at the same time;

[0010] (2) The modeling method based on a single overall curve is not sensitive enough to local irregular changes;

[0011] (3) The evaluation indicators lack a unified normalization and standardization mechanism, and the stability of cross-individual comparisons is limited;

[0012] (4) The degree of automation is not high, and some methods still rely on manual point selection or manual measurement.

[0013] Therefore, how to achieve a comprehensive quantitative description of the overall morphology and local irregularities of the dental arch based on digital dental models, and improve the objectivity, stability and comparability of the evaluation results, remains a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0014] This invention provides a method for quantitative analysis of dental arch alignment irregularities. Based on a digital dental arch model, it comprehensively analyzes and quantitatively describes the overall morphology of the dental arch and local tooth alignment deviations. This solves the problems of existing technologies that rely solely on local linear measurements or single overall curve modeling, which make it difficult to simultaneously reflect the overall trend of the dental arch and local alignment irregularities, lack consistency in cross-individual comparisons, and have limited automation.

[0015] The technical solution adopted in this invention is: a method for quantitative analysis of dental arch alignment irregularities, which includes the following steps:

[0016] Step 1: Tooth segmentation and extraction of key occlusal landmarks

[0017] Step 101: Segment the 3D scan model of the teeth to obtain each crown;

[0018] Step 102: Extract key occlusal landmarks from each tooth crown to construct a landmark set P. The landmarks include buccal cusp tips, canine cusp tips, and incisal edge points.

[0019] Step 2: Select a certain number of key occlusal anatomical landmarks on each tooth, and fit the selected key occlusal anatomical landmarks using polynomial curves and spline curves respectively. Based on the polynomial curve fitting, obtain the overall dental arch curve representing the overall morphology of the dental arch; based on the spline curve fitting, obtain the local dental arch curve representing the local changes of the dental arch.

[0020] Step 3: Calculate the positional deviation between the overall dental arch curve and the local dental arch curve to obtain a quantitative assessment of local irregularities.

[0021]

[0022] Where i represents the i-th sampling position number on the dental arch fitting curve, and N is the total number of sampling points. This represents the deviation at the i-th sampling point. This represents the value of the local dental arch curve at the i-th sampling point. This represents the value of the overall dental arch curve at the i-th sampling point;

[0023] Based on the selected error metric, the deviation is... The irregularity index of the dental arch is quantified into an index of dental arch irregularity, and the index of dental arch irregularity at each dental arch landmark is obtained.

[0024] The dental arch irregularity index of each dental arch landmark is standardized, and then the dental arch irregularity index is classified according to the set threshold range, and the dental arch irregularity level of each dental arch landmark is output.

[0025] Furthermore, in step 101, the segmentation methods for segmenting the 3D scanned tooth model include, but are not limited to: surface segmentation based on curvature guidance, crown segmentation based on region growing or watershed algorithms, and crown segmentation based on deep learning methods.

[0026] Furthermore, in step 102, the extraction methods for key anatomical landmarks of occlusion include, but are not limited to: peak curvature and height gradient analysis, local geometric feature analysis, template matching, or deep learning methods.

[0027] Furthermore, in step 2, the fitting methods for the overall dental arch curve fitting include, but are not limited to: polynomial fitting (preferably sixth-order polynomial), smooth spline fitting, and least squares fitting.

[0028] Furthermore, in step 2, the fitting methods for local dental arch curve fitting include, but are not limited to: higher-order spline fitting, local polynomial or local weighted regression (LOESS), piecewise curves or piecewise splines.

[0029] Furthermore, in step 3, the error metrics include, but are not limited to: root mean square error, mean absolute deviation, and maximum deviation.

[0030] Furthermore, in step 3, the grades of dental arch irregularities include: normal, mild, moderate, and severe.

[0031] Furthermore, in step 3, the dental arch irregularity index quantified using root mean square error is:

[0032]

[0033] in, and These are the maximum and minimum vertical coordinates of the dental arch markers, respectively.

[0034] Furthermore, in step 3, when using root mean square error to quantify the dental arch irregularity index, the corresponding standardization process is as follows:

[0035]

[0036] in, Represents all samples The mean, Represents all samples standard deviation This indicates a standardized index of dental arch irregularity.

[0037] Furthermore, in step 3, the four dental arch irregularity levels are specifically set as follows:

[0038] For normal For mild cases For moderate, It is severe.

[0039] The technical solution provided by this invention brings at least the following beneficial effects:

[0040] (1) It can simultaneously reflect the overall shape of the dental arch and the irregularity of its local arrangement.

[0041] This invention constructs both a global dental arch curve and a local dental arch curve on the same dental arch, and quantitatively analyzes the deviation between the two. This allows the evaluation results to reflect both the macroscopic morphological trend of the dental arch and detailed changes such as tooth crowding, rotation, and local misalignment. Compared to existing evaluation methods that rely solely on linear distance measurements or fitting a single global curve, this application overcomes the shortcomings of existing technologies in simultaneously considering both global and local characteristics.

[0042] (2) It has higher sensitivity and stability to local nonlinear arrangement changes.

[0043] By introducing local fine curves to model the local morphology of the dental arch, this invention can effectively capture the nonlinear morphological changes caused by local tooth misalignment, and avoid the problem of excessive smoothing or distortion of the evaluation results when there are local abnormal points in the overall curve, thereby improving the ability to identify local irregularities and the stability of the evaluation.

[0044] (3) Improve the comparability of evaluation results among different individuals

[0045] This invention introduces normalization and standardized analysis mechanisms during the quantification of irregularities, converting dental arch irregularity indicators into statistically significant standardized results, thus reducing the impact of differences in dental arch size, morphology, and measurement scale on the evaluation results. Compared with existing schemes lacking a unified standardization mechanism, the scheme in this application is more suitable for comparative analysis between different individuals, different populations, and different data sources.

[0046] (4) Suitable for automated implementation, reducing errors caused by manual intervention.

[0047] Based on a digital dental arch model, this invention achieves automatic calculation of dental arch irregularities through steps such as tooth segmentation, landmark extraction, curve construction, and error quantification. This reduces the subjective and repetitive errors caused by manual point selection and measurement, and helps improve analysis efficiency and result consistency.

[0048] (5) It has good versatility and scalability.

[0049] The technical solution of this invention is replaceable in terms of tooth segmentation method, marker point acquisition method, curve construction method and irregularity quantification method. It can be adapted to different types of digital dental arch data and different clinical application scenarios, and has strong versatility and scalability.

[0050] (6) It has a sound theoretical basis and verification foundation.

[0051] Theoretically, this invention can reasonably characterize the irregularity of dental arch alignment by comparing the degree of deviation between the overall trend and local changes. In practical applications, this indicator can be statistically analyzed and verified through multi-sample data, providing a stable and reliable quantitative basis for clinical applications and subsequent research. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A flowchart of a method for quantitative analysis of dental arch alignment irregularities provided in an embodiment of the present invention;

[0054] Figure 2 Examples of irregularities in dental arch shape. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be described in detail and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Generally, the components of the embodiments of the present invention described and shown in the accompanying drawings can be arranged and designed using different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present invention.

[0056] Arch Curve Irregularity (ACI) is used to quantitatively describe the degree of local irregularity of a dental arch based on its overall morphology. Its core idea is to simultaneously establish an overall reference curve and a local fine curve for the same dental arch, and quantify the deviation between the two, thereby reflecting the irregularity of the dental arch arrangement.

[0057] In the specific implementation process, firstly, based on the extracted key occlusal landmarks of the dental arch, a polynomial function of a preset order (preferably a sixth-order polynomial) is used to perform overall curve fitting on the dental arch. This overall curve is used to describe the macroscopic morphological trend of the dental arch, which can effectively smooth local noise and reflect the overall curvature characteristics of the dental arch.

[0058] After obtaining the overall reference curve, a higher-order spline function is used to fit the same set of key occlusal landmarks. The spline function can more accurately capture local morphological changes in the dental arch, such as curve fluctuations caused by tooth crowding, rotation, or local misalignment.

[0059] Subsequently, by calculating the difference between the overall polynomial curve and the higher-order spline curve at corresponding positions, and quantifying this difference using the normalized root mean square error (NRMSE), a numerical index reflecting the degree of dental arch irregularity is obtained. To improve comparability among different individuals, the error value can be standardized and converted into a z-score, and then classified into normal, mild, moderate, and severe irregularity levels according to a preset threshold range.

[0060] In this way, the ACI index can simultaneously reflect the deviation between the overall shape of the dental arch and the local arrangement, overcoming the limitations of traditional evaluation that relies only on linear distance or a few tooth measurement points.

[0061] In one embodiment, the present invention provides a method for quantitative analysis of dental arch alignment irregularities, such as... Figure 1 As shown, the specific steps include the following:

[0062] Step 1: Tooth segmentation and extraction of key occlusal landmarks

[0063] Step 101: Segment the 3D scan model of the teeth to obtain each crown.

[0064] This step can be achieved using one or more of the following methods:

[0065] Curvature-guided surface segmentation: By calculating the curvature of the tooth surface, high curvature areas and contour transitions between teeth are identified, enabling automatic separation of the crown.

[0066] Region growing or watershed algorithm: By selecting an initial seed point, the tooth crown is automatically separated by expanding along the surface to the crown region.

[0067] Deep learning methods: Using trained neural network models (such as 3D U-Net, PointNet++, etc.) to achieve automatic recognition and segmentation of tooth crowns.

[0068] Step 102: Extract key occlusal landmarks from each tooth crown to construct a landmark set P. The landmarks include buccal cusp tips, canine cusp tips, and incisal edge points.

[0069] In this step, the available methods include, but are not limited to: peak curvature and height gradient analysis, local geometric feature analysis (such as cusp morphology and crown contour), template matching, or deep learning methods.

[0070] The extracted set of marker points is denoted as ,in, For the first There are M marker points, where M is the number of marker points. The extracted set of marker points. Used to provide a data foundation for dental arch curve analysis.

[0071] Step 2, Overall dental arch curve fitting

[0072] Based on the set of marker points P, an overall dental arch curve is established to describe the macroscopic morphological trend of the dental arch.

[0073] A certain number of key occlusal anatomical landmarks were selected on each tooth, and a polynomial curve was used to fit these landmarks to obtain a global dental arch curve that characterizes the overall morphology of the dental arch. This global dental arch curve can smooth out local noise while preserving the overall morphological features of the dental arch.

[0074] Among them, the fitting methods for the overall dental arch curve include, but are not limited to: polynomial fitting (preferably sixth-order polynomial), smooth spline fitting, least squares fitting, or other alternative overall fitting methods.

[0075] The overall curve can smooth out local noise while preserving the overall morphological characteristics of the dental arch.

[0076] Step 3, Local dental arch curve fitting

[0077] Based on the set of landmarks P, a certain number of key occlusal landmarks are selected on each tooth. Spline curves are then used to fit the selected key occlusal landmarks to obtain local arch curves that characterize local changes in the dental arch. That is, fine-fitting is performed on the same set of key occlusal landmarks to capture local morphological changes in the dental arch (such as crowding, rotation, or local misalignment of teeth).

[0078] When fitting local dental arch curves, the fitting methods used include, but are not limited to: higher-order spline fitting, local polynomial or local weighted regression (LOESS), piecewise curves or piecewise splines.

[0079] In this invention, a combination of overall and local curves is used to simultaneously describe the overall trend and local deviations of the dental arch. See also... Figure 2 , Figure 2 The yellow dots in the image represent the set of key anatomical landmarks for occlusion. In the diagram, the red curve represents a local dental arch curve, and the blue curve represents the overall dental arch curve.

[0080] Step 4: Deviation calculation and quantification of dental arch irregularities

[0081] Step 401, Deviation Calculation

[0082] Calculate the positional deviation between the overall dental arch curve and the local curve at the corresponding sampling points:

[0083]

[0084] in, For the local curve in the first The values ​​at each sampling point This represents the value at the sampling point corresponding to the overall curve. This represents the number of sampling points.

[0085] Step 402, Normalized Root Mean Square Error (NRMSE)

[0086] The deviation is quantified as an index of dental arch irregularity:

[0087]

[0088] in, and These are the maximum and minimum vertical coordinates of the dental arch markers, respectively.

[0089] Step 403, Standardization and Grading (z-score)

[0090]

[0091] in, Represents all samples The mean, Represents all samples standard deviation This indicates a standardized index of dental arch irregularity.

[0092] Based on the z-value, threshold ranges are set to classify dental arch irregularities into four levels: normal, mild, moderate, and severe, to ensure comparability between individuals. For example, the four levels of dental arch irregularities are specifically set as follows: For normal For mild cases For moderate, It is severe.

[0093] In one embodiment, in step 4, the normalized root mean square error can be replaced with an index such as mean absolute deviation or maximum deviation, but z-score standardization can still be used for cross-individual comparisons.

[0094] The method proposed in this invention simultaneously reflects the overall trend of the dental arch and local alignment deviations; it is automated, requires no manual intervention, and is suitable for large-scale clinical analysis; it has high sensitivity to tooth crowding, rotation, and local malocclusion; and it can be used for pre-treatment assessment, efficacy monitoring, and dentition data analysis. Furthermore, it can be extended to other applications by combining different dental arch types (children, adults), scan data types (optical scans, CT scans), and tooth alignment characteristics.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0096] The above descriptions are merely some embodiments of the present invention. Those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A method for quantitative analysis of irregularity in dental arch arrangement, characterized in that, Includes the following steps: Step 1: Tooth segmentation and extraction of key occlusal landmarks Step 101: Segment the 3D scanning model of the tooth to obtain a single tooth crown; Step 102: Extract key anatomical landmarks of occlusion from each tooth crown and construct a landmark set P; the landmarks include the buccal cusp, canine cusp, and incisal point; Step 2: Select a certain number of key occlusal anatomical landmarks on each tooth, and fit the selected key occlusal anatomical landmarks using polynomial curves and spline curves respectively. Based on the polynomial curve fitting, obtain the overall dental arch curve representing the overall morphology of the dental arch; based on the spline curve fitting, obtain the local dental arch curve representing the local changes of the dental arch. Step 3: Calculate the positional deviation between the overall dental arch curve and the local dental arch curve: ； Where i represents the i-th sampling position number on the dental arch fitting curve, and N is the total number of sampling points. This represents the positional deviation at the i-th sampling point. This represents the value of the local dental arch curve at the i-th sampling point. This represents the value of the overall dental arch curve at the i-th sampling point; Based on the selected error metric, the deviation is... The irregularity index of the dental arch is quantified into an index of dental arch irregularity, and the index of dental arch irregularity at each dental arch landmark is obtained. The dental arch irregularity index of each dental arch landmark is standardized, and then the dental arch irregularity index is classified according to the set threshold range, and the dental arch irregularity level of each dental arch landmark is output.

2. The method as described in claim 1, characterized in that, In step 101, the segmentation methods for segmenting the 3D scan model of the tooth include: surface segmentation based on curvature guidance, crown segmentation based on region growing or watershed algorithms, and crown segmentation based on deep learning methods.

3. The method as described in claim 1, characterized in that, In step 102, the extraction methods for key occlusal anatomical landmarks from each crown include: peak curvature and height gradient analysis, local geometric feature analysis, template matching, or deep learning methods.

4. The method as described in claim 1, characterized in that, In step 2, the fitting method for the overall dental arch curve is replaced with polynomial fitting, smooth spline fitting, or least squares fitting.

5. The method as described in claim 1, characterized in that, In step 2, the fitting method for local dental arch curve fitting is replaced with: higher-order spline fitting, local polynomial fitting, local weighted regression fitting, or piecewise curve fitting.

6. The method as described in claim 1, characterized in that, In step 3, the error metrics include: root mean square error, mean absolute deviation, and maximum deviation.

7. The method as described in claim 6, characterized in that, In step 3, the dental arch irregularity index quantified using root mean square error is: ； in, and These are the maximum and minimum vertical coordinates of the dental arch markers, respectively.

8. The method as described in claim 7, characterized in that, In step 3, when using root mean square error to quantify the dental arch irregularity index, the corresponding standardization process is as follows: ； in, Represents all samples The mean, Represents all samples standard deviation This indicates a standardized index of dental arch irregularity.

9. The method as described in claim 6, characterized in that, In step 3, the grades of dental arch irregularities include: normal, mild, moderate, and severe.

10. The method as described in claim 9, characterized in that, In step 3, the four dental arch irregularity levels are specifically set as follows: For normal For mild cases For moderate, It is severe.

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