A dental restoration material selection method and system based on adjacent tooth color extraction

Abstract

This invention relates to the field of medical image processing technology, and more particularly to a method and system for selecting dental restoration materials based on adjacent tooth color extraction. The method includes: acquiring the color information of the abutment tooth to be restored; acquiring the adjacent teeth of the abutment tooth to be restored; determining a target restoration color based on the adjacent teeth; traversing combinations of restoration materials and thicknesses in a material database; calculating the restoration material color corresponding to any combination; overlaying any abutment tooth to be restored with the restoration material using a mask; calculating a predicted restoration color based on the color information of the abutment tooth, the color of the restoration material, and the thickness using a transparency calculation model; calculating the color difference between the predicted restoration color and the target restoration color; and selecting the restoration material and thickness based on a preset color difference threshold and preset multi-objective optimization conditions. Compared to existing technologies, this invention achieves the superposition prediction of restoration color on the abutment tooth, thereby enabling the selection of the restoration.

Description

Technical Field

[0001] This invention relates to the field of medical image processing technology, and in particular to a method and system for selecting dental restorative materials based on the color extraction of adjacent teeth. Background Technology

[0002] In clinical practice of prosthodontics, color matching of restorations is a crucial factor affecting the final aesthetic outcome. Currently, clinical practice mainly relies on dentists to select colors through visual color comparison or with the aid of standard shade guides (such as the VITA series). However, the results largely depend on the operator's subjective experience and judgment. With the gradual development of digital technologies, including digital colorimeters, hyperspectral imaging, and deep learning-based color analysis models, the repeatability and scientific rigor of color assessment can be improved through objective data acquisition and algorithmic processing, driving the evolution of prosthodontics towards precision and visualization.

[0003] Currently, existing technical solutions still have significant limitations. Traditional visual colorimetry is easily affected by factors such as the stability of ambient light sources, background color interference, differences in observer color vision, and clinical experience, making it highly subjective. Although existing digital prediction methods have improved in terms of data objectivity, their algorithm models generally fail to systematically integrate the key optical parameters of the restorative material, resulting in discrepancies between the color prediction results and the actual intraoral presentation. Summary of the Invention

[0004] To overcome the inaccurate color selection of restorations described in the prior art, this invention provides a method and system for selecting dental restoration materials based on the color extraction of adjacent teeth.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A method for selecting dental restorative materials based on adjacent tooth color extraction includes the following steps: Obtain the color information of the abutment tooth to be restored, obtain the adjacent teeth of the abutment tooth to be restored, and determine the target restoration color based on the adjacent teeth; Iterate through the combination of repair materials and thickness in the material database, and calculate the color of the repair material corresponding to any combination; A mask is overlaid on any abutment tooth to be restored and the restorative material. The restoration color is predicted based on a transparency calculation model using the color information of the abutment tooth, the color of the restorative material, and the thickness. The color difference between the predicted repair color and the target repair color is calculated, and the selection results of repair materials and thickness are obtained based on the preset color difference threshold and preset multi-objective optimization conditions.

[0007] As a preferred embodiment, the transparency calculation model is constructed based on the Beer-Lambert law and combined with a material scattering correction factor, and its expression is as follows: T(t) = exp(-α×t) Where T(t) is the transparency of the restorative material with a thickness of t, and its value ranges from 0 to 1. T=1 means completely transparent, with 100% of the abutment tooth color showing through, and T=0 means completely covered, with 100% of the material color showing through. α is the optical attenuation coefficient of the material. exp is an exponential function.

[0008] As a preferred embodiment, the material database stores at least the bulk color data and optical attenuation coefficient of each repair material; the formula for predicting the repair color is as follows:

[0009]

[0010]

[0011] in,[ , , [The LAB color is used to predict the repair color.] , To repair the material's original color, [ , , [ ] indicates the color of the abutment tooth.

[0012] As a preferred embodiment, the steps for screening the preset multi-objective optimization conditions include: A weighted score summation is performed on multiple objective optimization conditions, and any combination of repair material and thickness is sorted according to the score results. At least one selection result is obtained based on the sorting results. The weighted scoring includes: normalizing the values ​​of multiple target optimization conditions and performing weighted summation based on preset weights under different scenarios.

[0013] As a preferred embodiment, the preset multi-objective optimization conditions include at least one of the following: color matching degree, material cost, repair thickness, or mechanical strength.

[0014] As a preferred option, if the color difference is greater than the preset threshold, the stacking combination and thickness of two or more repair materials in the material database are traversed. For each layer combination and the thickness of each layer, the predicted repair color after stacking is calculated layer by layer; The color difference between the predicted repair color and the target repair color after the stacking is calculated, and the selection results including the stacking material and the thickness of each layer are obtained based on the preset multi-objective optimization conditions according to the preset color difference threshold.

[0015] As a preferred embodiment, the step of obtaining the target restoration color includes: querying a color database based on the adjacent tooth color information to obtain an initial recommended color; receiving user adjustment or confirmation of the initial recommended color to determine the target restoration color.

[0016] This invention also proposes a dental restorative material selection system based on adjacent tooth color extraction, the system comprising: The data acquisition module acquires color information of the abutment tooth and adjacent teeth to be restored, as well as the target restoration color. The color prediction module iterates through the combination of restoration materials and thicknesses in the material database and calculates the color of the restoration material corresponding to any combination. It then overlays a mask over any abutment tooth to be restored with the restoration material and calculates the predicted restoration color based on the color information of the abutment tooth, the color of the restoration material, and the thickness using a transparency calculation model. The scheme selection module calculates the color difference between the predicted repair color and the target repair color, and selects the repair material and thickness based on a preset color difference threshold and preset multi-objective optimization conditions.

[0017] The present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for selecting dental restorative materials based on adjacent tooth color extraction as described in the present invention.

[0018] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for selecting dental restorative materials based on adjacent tooth color extraction as described in the present invention.

[0019] Compared with the prior art, the beneficial effects of the technical solution of the present invention are as follows: This invention overcomes the limitations of existing manual color matching and simple linear superposition methods by mathematically constructing a color mask and transparency calculation model and using nonlinear fitting, thus achieving accurate prediction and intuitive simulation of the color effect of dental restorations. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the dental restoration material selection method based on adjacent tooth color extraction in Example 1; Figure 2 This is a schematic diagram of the adjacent tooth acquisition results in Example 1; Figure 3 This is a before-and-after comparison diagram of Example 1; Figure 4 This is a schematic diagram illustrating the repair effect of multi-layered materials in Example 1; Figure 5This is an architecture diagram of a dental restoration material selection system based on adjacent tooth color extraction, as shown in Example 2. Detailed Implementation

[0021] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Example 1 This embodiment proposes a method for selecting dental restorative materials based on adjacent tooth color extraction, such as... Figure 1 The diagram shown is a flowchart illustrating a method for selecting dental restorative materials based on the color extraction of adjacent teeth in this embodiment.

[0024] A method for selecting dental restorative materials based on adjacent tooth color extraction, comprising: S1. Obtain the color information of the abutment tooth to be restored, obtain the adjacent teeth of the abutment tooth to be restored, and determine the target restoration color based on the adjacent teeth; S2. Traverse the combination of repair materials and thickness in the material database, and calculate the color of the repair material corresponding to any combination; S3. Overlay any abutment tooth to be restored with the restoration material using a mask, and calculate and predict the restoration color based on the color information of the abutment tooth, the color of the restoration material, and the thickness using a transparency calculation model. S4. Calculate the color difference between the predicted repair color and the target repair color, and select the repair material and thickness based on the preset color difference threshold and preset multi-objective optimization conditions.

[0025] In this embodiment, the subjective visual color comparison is replaced by objective extraction of adjacent tooth colors, and the superposition effect of dynamic materials and abutment teeth is combined with the transparency calculation model to realize the digital pre-show of restoration effect. The introduction of color difference threshold and multi-objective optimization collaborative screening mechanism can systematically recommend restoration plans, which significantly improves the accuracy of restoration color matching and the scientific nature of treatment decision.

[0026] like Figure 2 The image shown is a schematic diagram of the results obtained from acquiring adjacent teeth.

[0027] Figure 2 The green and blue boxes represent the results obtained from adjacent teeth, and the color of the adjacent teeth can be used to help select the restoration material.

[0028] like Figure 3 The image shown is a before-and-after comparison diagram.

[0029] from Figure 3 It can be seen that by selecting a suitable restoration using this method, the restorative effect of the tooth can be similar to that of the adjacent teeth. In an optional embodiment, the transparency calculation model is constructed based on Beer-Lambert's law and combined with a material scattering correction factor, and its expression is as follows: T(t) = exp(-α×t) Where T(t) is the transparency of the restorative material with a thickness of t, and its value ranges from 0 to 1. T=1 means completely transparent, with 100% of the abutment tooth color showing through, and T=0 means completely covered, with 100% of the material color showing through. α is the optical attenuation coefficient of the material. exp is an exponential function.

[0030] In this embodiment, a transparency calculation model based on the laws of physical optics is introduced to simulate the effect of restorative materials on the color of the underlying tooth at different thicknesses, thereby improving the prediction accuracy of the final material and thickness selection results.

[0031] In an optional embodiment, the material database stores at least the bulk color data and optical attenuation coefficient of each repair material; the formula for predicting the repair color is as follows:

[0032]

[0033]

[0034] in,[ , , [The LAB color is used to predict the repair color.] , To repair the material's original color, [ , , [ ] indicates the color of the abutment tooth.

[0035] More specifically, L Indicates the lightness component; a b represents the red-green axis component; b represents the yellow-blue axis component.

[0036] In this embodiment, the transparency parameter is linearly fused with the CIELAB color space to simulate the superposition effect of the base color of the abutment tooth and the restorative material.

[0037] In an optional embodiment, the step of screening the preset multi-objective optimization conditions includes: A weighted score summation is performed on multiple objective optimization conditions, and any combination of repair material and thickness is sorted according to the score results. At least one selection result is obtained based on the sorting results. The weighted scoring includes: normalizing the values ​​of multiple target optimization conditions and performing weighted summation based on preset weights under different scenarios.

[0038] In this embodiment, normalization is used to eliminate dimensional differences and weighted summation is used to achieve the fusion of multi-dimensional indicators, making the screening process both scientific and clinically flexible. Doctors can dynamically adjust the weights according to the patient's needs to generate personalized optimal solutions.

[0039] In one optional embodiment, the preset multi-objective optimization conditions include at least one of color matching degree, material cost, restoration thickness, or mechanical strength.

[0040] As an example, the color matching precision is min f1(M,t)=ΔE00( , This represents the perceptual difference between the predicted color and the target color; The material cost is min f2(M,t) = Cost(M) × Area × t, which means choosing the most economical option while meeting the color requirements. Restoration thickness: min f3(M,t) = t; reduce tooth preparation amount; Mechanical strength: max f4(M) = Strength(M) improves the durability of the restoration and extends its clinical lifespan; The ranking is based on a weighted scoring method: Score(M,t) = w1·f1'(M,t) + w2·f2'(M,t) + w3·f3'(M,t) + w4·f4'(M) Wherein the normalized sub-objective function is: f1'(M,t) = 1 - ΔE00(M,t) / Dref (The smaller the color difference, the higher the score) Dref is the color difference normalization reference value, which is used to adaptively adjust the color difference sensitivity according to different clinical application scenarios.

[0041] In the preferred embodiment: When the application scenario is routine repair or the requirements for color matching are not high, take Dref=3.6 When the application scenario involves the anterior teeth area, visible area, or when the patient has high requirements for color matching, take... Dref=1.8 The above method enables scene adaptation for color difference evaluation, allowing the scoring model to automatically adjust its sensitivity to meet different aesthetic needs.

[0042] f2'(M,t) = 1 - Cost(M,t) / Cost_max (The lower the cost, the higher the score) f3'(M,t) = 1 - (t-0.3) / (2.0-0.2) (The thinner the thickness, the higher the score) f4'(M) = Strength(M) / Strength_max (The higher the strength, the higher the score) The weighting coefficients w1, w2, w3, and w4 are adaptively adjusted according to the clinical scenario. As another example: Anterior aesthetic zone: w1=0.5, w2=0.1, w3=0.3, w4=0.1 (prioritizing color and aesthetics); Posterior functional area: w1=0.3, w2=0.1, w3=0.2, w4=0.4 (preferred strength); Economical option: w1=0.3, w2=0.4, w3=0.2, w4=0.1 (prioritizing cost control); Minimally invasive tooth preservation: w1=0.3, w2=0.1, w3=0.5, w4=0.1 (minimum thickness preferred).

[0043] Furthermore, to ensure that each normalized sub-objective function has a consistent physical meaning in the weighted scoring and to avoid negative values ​​in extreme cases, non-negativity constraints are applied to each normalized sub-objective function to ensure that its value range satisfies the following:

[0044] Preferably, a lower bound truncation function is used for constraint, that is:

[0045] in ( ) represents the untrunculated primitive normalization function.

[0046] Doctors can select different weight combinations based on the patient's needs, and the system will output alternative solutions for the corresponding scenario.

[0047] In an optional embodiment, if the color difference is greater than a preset threshold, the stacking combination of two or more repair materials and the thickness of each layer are traversed in the material database. For each layer combination and the thickness of each layer, the predicted repair color after stacking is calculated layer by layer; The color difference between the predicted repair color and the target repair color after the stacking is calculated, and the selection results including the stacking material and the thickness of each layer are obtained based on the preset multi-objective optimization conditions according to the preset color difference threshold.

[0048] like Figure 4 The image shows a schematic diagram illustrating the repair effect of multiple layers of materials.

[0049] from Figure 4 It can be seen that even if the original abutment teeth have a large color difference, the expected amount of effect can still be achieved by layering multiple layers.

[0050] As an example, the color difference is calculated based on the CIEDE2000 formula, and the color difference threshold is 3.5.

[0051] In this embodiment, by introducing the traversal and computational capabilities of layered material combinations, the processing range of this application for complex color cases or cases with large color differences is expanded. By combining different material layers and adjusting their thickness, the final color performance can be finely controlled, thereby solving the technical problem that single-layer materials cannot achieve ideal color effects and improving the applicability of the method.

[0052] In one optional embodiment, the step of obtaining the target restoration color includes: querying a color database based on the adjacent tooth color information to obtain an initial recommended color; receiving user adjustment or confirmation of the initial recommended color, and determining the target restoration color.

[0053] In this embodiment, while maintaining the objective quantitative advantages of the method, the user's final judgment and aesthetic subjectivity are respected.

[0054] Example 2 This embodiment proposes a dental restorative material selection system based on adjacent tooth color extraction, such as... Figure 5 As shown, a dental restorative material selection system based on adjacent tooth color extraction includes: The data acquisition module acquires color information of the abutment tooth and adjacent teeth to be restored, as well as the target restoration color. The color prediction module iterates through the combination of restoration materials and thicknesses in the material database and calculates the color of the restoration material corresponding to any combination. It then overlays a mask over any abutment tooth to be restored with the restoration material and calculates the predicted restoration color based on the color information of the abutment tooth, the color of the restoration material, and the thickness using a transparency calculation model. The scheme selection module calculates the color difference between the predicted repair color and the target repair color, and selects the repair material and thickness based on a preset color difference threshold and preset multi-objective optimization conditions.

[0055] It is understood that the system in this embodiment corresponds to the method in Embodiment 1 above, and the options in Embodiment 1 above are also applicable to this embodiment, so they will not be described again here.

[0056] Example 3 This embodiment proposes a computer device, including a memory and a processor. The memory stores computer-readable instructions, wherein when the computer-readable instructions are executed by the processor, the processor performs the steps of the dental restoration material selection method based on adjacent tooth color extraction proposed in Embodiment 1.

[0057] Example 4 This embodiment proposes a storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by a processor, they implement the steps of the dental restoration material selection method based on adjacent tooth color extraction proposed in Embodiment 1.

[0058] By way of example, the storage medium includes, but is not limited to, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks, and other media capable of storing program code.

[0059] By way of example, the instructions, programs, code sets, or instruction sets may be implemented using conventional programming languages.

[0060] By way of example, the processor includes, but is not limited to, smartphones, personal computers, servers, network devices, etc., for performing all or part of the steps of the dental restoration material selection method based on adjacent tooth color extraction described in Example 1.

[0061] The terminology used in the accompanying drawings is for illustrative purposes only and should not be construed as limiting the scope of this patent. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for selecting dental restorative materials based on adjacent tooth color extraction, characterized in that, Includes the following steps: Obtain the color information of the abutment tooth to be restored, obtain the adjacent teeth of the abutment tooth to be restored, and determine the target restoration color based on the adjacent teeth; Iterate through the combination of repair materials and thickness in the material database, and calculate the color of the repair material corresponding to any combination; A mask is overlaid on any abutment tooth to be restored and the restorative material. The restoration color is predicted based on a transparency calculation model using the color information of the abutment tooth, the color of the restorative material, and the thickness. The color difference between the predicted repair color and the target repair color is calculated, and the selection results of repair materials and thickness are obtained based on the preset color difference threshold and preset multi-objective optimization conditions.

2. The method for selecting dental restorative materials based on adjacent tooth color extraction according to claim 1, characterized in that, The transparency calculation model is based on Beer-Lambert's law and combined with a material scattering correction factor. Its expression is as follows: T(t) = exp(-α×t) Where T(t) is the transparency of the restorative material with a thickness of t, and its value ranges from 0 to 1. T=1 means completely transparent, with 100% of the abutment tooth color showing through, and T=0 means completely covered, with 100% of the material color showing through. α is the optical attenuation coefficient of the material. exp is an exponential function.

3. The method for selecting dental restorative materials based on adjacent tooth color extraction according to claim 2, characterized in that, The material database stores at least the material's intrinsic color data and optical attenuation coefficient for each repair material; the formula for predicting the repair color is as follows: in,[ , , [The LAB color is used to predict the repair color.] , To repair the material's original color, [ , , [ ] indicates the color of the abutment tooth.

4. The method for selecting dental restorative materials based on adjacent tooth color extraction according to claim 1, characterized in that, The steps for screening the preset multi-objective optimization conditions include: A weighted score summation is performed on multiple objective optimization conditions, and any combination of repair material and thickness is sorted according to the score results. At least one selection result is obtained based on the sorting results. The weighted scoring includes: normalizing the values ​​of multiple target optimization conditions and performing weighted summation based on preset weights under different scenarios.

5. The method for selecting dental restorative materials based on adjacent tooth color extraction according to claim 4, characterized in that, The preset multi-objective optimization conditions include at least one of the following: color matching degree, material cost, repair thickness, or mechanical strength.

6. The method for selecting dental restorative materials based on adjacent tooth color extraction according to claim 5, characterized in that, If the color difference is greater than the preset threshold, iterate through the material database to find the stacking combination and thickness of two or more repair materials. For each layer combination and the thickness of each layer, the predicted repair color after stacking is calculated layer by layer; The color difference between the predicted repair color and the target repair color after the stacking is calculated, and the selection results including the stacking material and the thickness of each layer are obtained based on the preset multi-objective optimization conditions according to the preset color difference threshold.

7. The method for selecting dental restorative materials based on adjacent tooth color extraction according to claim 1, characterized in that, The steps for obtaining the target restoration color include: querying a color database based on the color information of the adjacent teeth to obtain an initial recommended color; receiving the user's adjustment or confirmation of the initial recommended color to determine the target restoration color.

8. A dental restorative material selection system based on adjacent tooth color extraction, characterized in that, The system applied to the dental restorative material selection method based on adjacent tooth color extraction as described in any one of claims 1 to 7, the system comprising: The data acquisition module acquires color information of the abutment tooth and adjacent teeth to be restored, as well as the target restoration color. The color prediction module iterates through the combination of restoration materials and thicknesses in the material database and calculates the color of the restoration material corresponding to any combination. It then overlays a mask over any abutment tooth to be restored with the restoration material and calculates the predicted restoration color based on the color information of the abutment tooth, the color of the restoration material, and the thickness using a transparency calculation model. The scheme selection module calculates the color difference between the predicted repair color and the target repair color, and selects the repair material and thickness based on a preset color difference threshold and preset multi-objective optimization conditions.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for selecting dental restorative materials based on adjacent tooth color extraction as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for selecting dental restorative materials based on adjacent tooth color extraction as described in any one of claims 1 to 7.

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