Dental diagnosis and treatment scheme generation method and system based on user image data

Abstract

The invention discloses a dental diagnosis and treatment scheme generation method and system based on user image data, relates to the technical field of dental treatment, and solves the technical problem that systematicness and accuracy are lacked in the aspect of symptom identification due to the fact that historical data of a patient and comprehensive conditions of various symptoms are not fully combined. According to the method, the preprocessed image is segmented according to the tooth partitions, the edge detection algorithm is adopted to carry out morphological feature recognition on the combined area, more detailed and accurate symptom features can be obtained, the change condition of the user is judged in combination with historical data of the user, whether a new symptom exists or not is judged by analyzing the historical data, and the user experience is improved. According to the method and the system, the image features are extracted, the relevance between the newly-added symptoms and the original symptoms is deeply analyzed based on the image features, the diagnosis and treatment schemes are finely screened according to the relevance between the newly-added symptoms and the original symptoms and abnormal parameters, it is ensured that the finally-generated diagnosis scheme information can comprehensively consider multiple symptoms, and the comprehensiveness and effectiveness of treatment are improved.

Description

Technical Field

[0001] The present invention relates to the field of dental treatment technology, and in particular to a method and system for generating a dental treatment plan based on user image data. Background Art

[0002] As people's awareness of oral health increases and their requirements for dental aesthetics improve, the demand for oral treatment / orthodontics is increasing. However, despite this, patients who go for treatment always have great doubts about oral treatment.

[0003] The patent application with publication number CN108877897B discloses a dental treatment plan generation method, device and treatment system. The dental treatment plan generation method includes: obtaining oral image information; sending the oral image information to two or more remote treatment addresses for analysis; receiving the analysis results returned by multiple remote treatment addresses to obtain a comprehensive analysis result; based on the comprehensive analysis result, performing image processing on the oral image information to form a dynamic image, and the dynamic image includes at least an initial tooth film simulation image and a treatment result simulation image; and displaying the dynamic image.

[0004] However, when some existing diagnostic plan generation methods are used, the processing of image data remains at a relatively basic level. In the generation of diagnosis and treatment plans, they often fail to fully integrate the patient's historical data and the comprehensive conditions of multiple diseases. In terms of disease identification, they lack systematicity and accuracy. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for generating dental treatment plans based on user image data, which solves the problem of not fully integrating the patient's historical data and the comprehensive situation of multiple conditions, and lacking systematicity and accuracy in condition identification.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for generating a dental treatment plan based on user image data, the method specifically comprising the following steps:

[0007] Step 1: Acquire the user's image data, perform image processing to obtain a pre-processed image, and segment the pre-processed image to obtain a segmented pre-processed image;

[0008] Step 2: Analyze the obtained segmentation preprocessing image, obtain the grayscale value corresponding to the segmentation preprocessing image, determine the abnormal area according to the grayscale value, and combine the abnormal areas to obtain the combined area;

[0009] Step 3: Analyze the obtained combined area, identify the disease type corresponding to the combined area to obtain disease type information, obtain and analyze the abnormal parameters corresponding to the combined area for a single type of disease in the disease type information, combine the user's historical data to perform specific solution generation and analysis, and generate diagnostic solution information;

[0010] Step 4: Analyze the symptom type information into multiple categories of results, analyze historical data, and screen the diagnosis and treatment plans based on the relationship between the newly added symptoms and the original symptoms in the multiple categories of results to generate diagnosis plan information.

[0011] As a further solution of the present invention, the specific method of obtaining the segmented pre-processed image in step 1 is:

[0012] The user's image data is obtained by using an oral CBCT device, and then the obtained image data is smoothed and denoised to obtain a preprocessed image. The preprocessed image is then segmented according to tooth partitions to obtain a segmented preprocessed image.

[0013] As a further solution of the present invention, the specific method of analyzing the segmented pre-processed image in step 2 is:

[0014] Obtain a segmented preprocessed image and label it as i, where i = 1, 2, ..., j, where j represents the number of segmented preprocessed images. Then, the grayscale value corresponding to the segmented preprocessed image i is recorded as Hi, and the grayscale value Hi is compared with the threshold. If the grayscale value Hi corresponding to the segmented preprocessed image i does not match the threshold, it indicates that the corresponding segmented preprocessed image is abnormal and is marked as an abnormal image. If the grayscale value Hi corresponding to the segmented preprocessed image i matches the threshold, it indicates that the corresponding segmented preprocessed image is normal and is marked as a normal image.

[0015] All abnormal images are obtained, and then the grayscale values of the abnormal images are obtained. At the same time, the abnormal images corresponding to the grayscale values of the same dimension are obtained and combined to obtain the combined area, and the corresponding morphological features of the combined area are identified to obtain the morphological features of the combined area.

[0016] As a further solution of the present invention, the specific method of analyzing the combined area in step 3 is:

[0017] The combined area and the corresponding morphological features are obtained, and then the pathological type corresponding to the combined area is identified to generate single-category results and multi-category results. The specific identification method is to judge the grayscale value corresponding to the combined area. If there are multiple groups of grayscale values, multi-category results are generated. Conversely, if there is only one group of grayscale values, a single-category result is generated, and the generated single-category results are analyzed at the same time.

[0018] As a further solution of the present invention, the specific method of analyzing the results of a single type in step 3 is:

[0019] Obtain the abnormal parameters corresponding to the combined area, then obtain the historical data corresponding to the user, and judge the changes in the abnormal parameters based on the historical data. At the same time, screen the diagnosis and treatment plans according to the changes in the abnormal parameters, and obtain the diagnosis and treatment plans with the same changes in the abnormal parameters as the pre-selected diagnosis and treatment plans;

[0020] Then, the historical treatment status in the user's historical data is judged. If the user has historical treatment, the user is marked as a treated user. Otherwise, if the user has no historical treatment, the user is marked as an untreated user. At the same time, corresponding plans are generated for the two types of users respectively.

[0021] As a further solution of the present invention, the specific manner of generating corresponding solutions for the two types of users in step 3 is as follows:

[0022] The method of generating a diagnosis plan for a treated user is as follows: obtaining the same pre-selected diagnosis and treatment plan as that of the treated user, and generating diagnosis plan information based on the pre-selected diagnosis and treatment plan obtained by screening;

[0023] The method of generating a diagnosis plan for untreated users is to obtain a pre-selected diagnosis and treatment plan, and at the same time, screen the diagnosis plan based on the current gray value as a standard, and generate diagnosis plan information.

[0024] As a further solution of the present invention, the specific method of analyzing multiple types of results in step 4 is:

[0025] Obtain the generated multiple-category results, and at the same time obtain the historical data corresponding to the user, and analyze the historical data. If the multiple-category results do not exist in the historical data, it means that the user has a new symptom, and a new symptom signal is generated. Conversely, if the multiple-category results exist in the historical data, it means that the user does not have a new symptom, and a no-new symptom signal is generated. Then, the new symptom signal and the no-new symptom signal are analyzed separately.

[0026] As a further solution of the present invention, the specific method of analyzing the newly added pathological signals and the non-newly added signals in step 4 is:

[0027] Analyze the newly added symptom signal to obtain the corresponding newly added symptom from multiple categories of results, and then judge the correlation between the newly added symptom and the original symptom. If the newly added symptom is correlated with the original symptom, generate a correlation analysis result; otherwise, if the newly added symptom is not correlated with the original symptom, generate a non-correlation analysis result.

[0028] Obtain the generated association analysis results, then obtain all diagnosis and treatment plans, and at the same time obtain similar situations in the diagnosis and treatment plans and record them as the diagnosis and treatment plans to be analyzed, and screen the diagnosis and treatment plans to be analyzed based on the abnormal parameters corresponding to the newly added symptoms and the original symptoms to obtain the diagnosis plan, and generate diagnosis plan information;

[0029] The generated non-correlated analysis results are obtained, and the diagnosis and treatment plans to be analyzed are screened based on the abnormal parameters corresponding to the newly added symptoms and the original symptoms to obtain a diagnosis plan, and the diagnosis plan information is generated at the same time.

[0030] Analyze the non-new signals to obtain multiple types of symptoms corresponding to multiple types of results, and at the same time, use the abnormal parameters corresponding to the multiple types of symptoms as the standard to screen the treatment plans to obtain the screening treatment plans. Then, obtain the abnormal parameter change values corresponding to the multiple types of symptoms, and match the screening treatment plans with the generated abnormal parameter change value standard to obtain the diagnosis plan, and generate the diagnosis plan information at the same time.

[0031] A dental treatment plan generation system based on user image data, comprising:

[0032] An image information acquisition unit, which acquires the user's image data and transmits it to the image preprocessing and analysis unit;

[0033] an image preprocessing and analysis unit, which processes the acquired image to obtain a preprocessed image, and segments the preprocessed image to obtain a segmented preprocessed image, obtains grayscale values corresponding to the segmented preprocessed image, determines abnormal regions based on the grayscale values, combines the abnormal regions to obtain combined regions, and then identifies the pathology types corresponding to the combined regions to obtain pathology type information, and transmits the pathology type information to the pathology type information analysis unit;

[0034] a symptom type analysis unit, which obtains and analyzes abnormal parameters corresponding to the combined area for a single type of result in the symptom type information, performs specific plan generation and analysis in combination with the user's historical data, and generates diagnostic plan information; analyzes symptom type information with multiple types of results, analyzes historical data, and screens treatment plans based on the relationship between the newly added symptom in the multiple type results and the original symptom, generates diagnostic plan information, and transmits the generated diagnostic plan information to the information output unit;

[0035] An information output unit is used to display the acquired diagnostic solution information to the corresponding operator.

[0036] Beneficial effects

[0037] The present invention provides a method and system for generating a dental treatment plan based on user image data. Compared with the existing technology, it has the following advantages:

[0038] The present invention can obtain more detailed and accurate pathological characteristics by segmenting the preprocessed image according to tooth partitions and using an edge detection algorithm to identify the morphological features of the combined area. For a single type of result, it not only obtains the abnormal parameters of the combined area, but also judges its changes in combination with the user's historical data, thereby screening out pre-selected diagnosis and treatment plans that match it, formulating highly personalized treatment plans for patients, and improving treatment effects. When faced with multiple types of results, it determines whether there are new pathological conditions by analyzing historical data, and conducts an in-depth analysis of the correlation between the new pathological conditions and the original pathological conditions based on image features. According to the correlation between the new pathological conditions and the original pathological conditions and the abnormal parameters, the diagnosis and treatment plans are finely screened to ensure that the final generated diagnosis plan information can comprehensively consider multiple pathological conditions and improve the comprehensiveness and effectiveness of the treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a diagram of the steps and methods of the present invention;

[0040] Figure 2 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] For example 1, please refer to Figure 1 The present application provides a method for generating a dental treatment plan based on user image data, which specifically includes the following steps:

[0043] Step 1: Acquire the user's image data, perform image processing to obtain a pre-processed image, and segment the pre-processed image to obtain a segmented pre-processed image.

[0044] First, the user's image data is obtained, and the oral CBCT (cone beam CT) equipment is used to obtain three-dimensional image data of the patient's mouth, covering structures such as teeth, periodontal tissues, and jaws. Then, the obtained image data is processed, specifically, the image data is pre-processed by smoothing, noise reduction, etc. to obtain a pre-processed image. Then, the pre-processed image is segmented according to tooth partitions to obtain a segmented pre-processed image.

[0045] Step 2: Analyze the obtained segmentation preprocessing image, obtain the grayscale value corresponding to the segmentation preprocessing image, determine the abnormal area according to the grayscale value, and combine the abnormal areas to obtain the combined area.

[0046] The segmented pre-processed image is obtained and labeled as i, and i=1, 2, ..., j, where j represents the number of segmented pre-processed images. Then, the gray value corresponding to the segmented pre-processed image i is recorded as Hi, and the gray value is obtained by a special medical image reading software, such as an image browser in the DICOM (Digital Imaging and Communications in Medicine) format. At the same time, the gray value Hi is compared with a threshold value, and the specific value of the threshold value is set by the operator, specifically representing the gray value of the tooth image under normal circumstances, for example, the gray value of normal enamel is 1000. -2000HU (HU is the unit of CT value), the corresponding threshold can be set to 1000-2000HU. If the grayscale value Hi corresponding to the segmented preprocessed image i does not match the threshold, it means that the corresponding segmented preprocessed image is abnormal and is marked as an abnormal image. If the grayscale value Hi corresponding to the segmented preprocessed image i matches the threshold, it means that the corresponding segmented preprocessed image is normal and is marked as a normal image. The specific basis for judging whether it is matched or not is whether the grayscale value exists in the threshold range. If it exists, it indicates a match; if not, it indicates a mismatch.

[0047] All abnormal images are obtained, and then the grayscale values of the abnormal images are obtained. At the same time, the abnormal images corresponding to the grayscale values of the same dimension are obtained and combined to obtain a combined area. The same dimension here means that the grayscale values are in the same threshold interval. The corresponding morphological features of the combined area are identified to obtain the morphological features of the combined area. The specific morphological feature identification is obtained through an edge detection algorithm, such as the Canny edge detection algorithm.

[0048] Step 3: Analyze the obtained combined area, obtain the symptom type information by identifying the symptom type corresponding to the combined area, obtain and analyze the abnormal parameters corresponding to the combined area for the single type result in the symptom type information, combine the user's historical data to perform specific plan generation and analysis, and generate diagnostic plan information.

[0049] Acquire the combined area and the corresponding morphological features, then identify the pathological type corresponding to the combined area, and generate single-category results and multi-category results. The specific identification method is to judge the grayscale value corresponding to the combined area. If there are multiple groups of grayscale values, it specifically means that there are multiple groups of threshold intervals corresponding to the grayscale values. For example, in an oral CBCT image, we observe a certain tooth area, part of which has a low grayscale value, which is in the threshold interval representing caries (such as 800-1000HU), while the grayscale value of the adjacent part is higher, which falls in the threshold interval representing the pulp inflammation area (such as 1200-1500HU), then multi-category results are generated. On the contrary, if there is only one group of grayscale values, for example, the grayscale value of a certain tooth area is always stable in the threshold interval representing the periodontitis lesion area (such as 1000-1300HU), then a single-category result is generated, and the generated single-category results are analyzed at the same time.

[0050] Obtain the generated single category result, and at the same time obtain the abnormal parameters corresponding to the combined area, and the abnormal parameters include the area and perimeter in the morphological characteristics, then obtain the user's corresponding historical data, and judge the change of the abnormal parameters based on the historical data, and the change here is specifically expressed as the change value of the abnormal parameters, such as the area change value and the perimeter change value, and at the same time, screen the diagnosis and treatment plans according to the change of the abnormal parameters, obtain the diagnosis and treatment plans with the same abnormal parameter change as the pre-selected diagnosis and treatment plans, and the diagnosis and treatment plans here are represented as all diagnosis and treatment plans corresponding to the current combined area;

[0051] Then, the historical treatment status in the user's historical data is judged. If the user has historical treatment, the user is marked as a treated user. Otherwise, if the user has no historical treatment, the user is marked as an untreated user. At the same time, corresponding plans are generated for the two types of users respectively.

[0052] The method of generating a diagnosis plan for a treated user is as follows: obtaining the same pre-selected diagnosis and treatment plan as that of the treated user, and generating diagnosis plan information based on the pre-selected diagnosis and treatment plan obtained by screening;

[0053] The method for generating a diagnostic plan for untreated users is to obtain a pre-selected treatment plan, and at the same time, use the current grayscale value as the standard to screen the diagnostic plan, and generate diagnostic plan information. Specifically, the corresponding grayscale value in the pre-selected treatment plan is obtained, and the pre-selected diagnostic plan corresponding to the smallest difference with the current grayscale value is obtained as the standard.

[0054] Step 4: Analyze the symptom type information into multiple categories of results, analyze historical data, and screen the diagnosis and treatment plans based on the relationship between the newly added symptoms and the original symptoms in the multiple categories of results to generate diagnosis plan information.

[0055] Obtain the generated multi-category results and the corresponding historical data of the user at the same time, and analyze the historical data. If the multi-category results do not exist in the historical data, it indicates that the user has a new condition, and a new condition signal is generated. Conversely, if the multi-category results exist in the historical data, it indicates that the user has no new condition, and a no-new condition signal is generated. Here, "new" indicates that there is a condition of a different type than that in the historical data, and "no-new" indicates that the condition type in the historical data is the same as the current condition type. Then, the new condition signal and the no-new condition signal are analyzed separately;

[0056] Analyze the newly added pathology signals to obtain the corresponding newly added pathology in multiple categories of results. Then, determine the correlation between the newly added pathology and the original pathology. The judgment method here is based on image feature analysis. For example, if a low-density shadow of apical periodontitis is found adjacent to a caries area in a CBCT image, it indicates that caries may be the cause of the apical periodontitis. If caries is not treated in time, bacteria can infect the periapical tissue through the pulp cavity, leading to apical periodontitis. If the newly added pathology is correlated with the original pathology, a correlation analysis result is generated. Conversely, if the newly added pathology is not correlated with the original pathology, a non-correlation analysis result is generated.

[0057] Obtain the generated correlation analysis results, then obtain all diagnosis and treatment plans, and at the same time obtain similar situations in the diagnosis and treatment plans and record them as the diagnosis and treatment plans to be analyzed, and the similar situations here are represented as the diagnosis and treatment plans corresponding to the newly added symptoms and the original symptoms. Based on the abnormal parameters corresponding to the newly added symptoms and the original symptoms, the diagnosis and treatment plans to be analyzed are screened to obtain the diagnosis plans, and the diagnosis and treatment plan information is generated. Specifically, the corresponding abnormal parameters in the diagnosis and treatment plans to be analyzed are compared with the current abnormal parameters, and the diagnosis and treatment plan to be analyzed corresponding to the smallest difference is selected as the standard and recorded as the diagnosis plan;

[0058] The generated non-correlated analysis results are obtained, and the diagnosis and treatment plans to be analyzed are screened based on the abnormal parameters corresponding to the newly added symptoms and the original symptoms to obtain a diagnosis plan, and the diagnosis plan information is generated at the same time.

[0059] Analyze the new signals that have not been added, obtain multiple types of symptoms corresponding to multiple types of results, and at the same time, use the abnormal parameters corresponding to the multiple types of symptoms as standards to screen the treatment plans to obtain screening treatment plans, then obtain the abnormal parameter change values corresponding to the multiple types of symptoms, and the abnormal parameter change values here are the change values within time t. The specific value of time t is set by the operator, and the screening treatment plans are matched with the generated abnormal parameter change value standards to obtain a diagnostic plan. The specific matching method is to obtain the screening treatment plans corresponding to the same abnormal parameter change values, record them as diagnostic plans, and generate diagnostic plan information at the same time.

[0060] For example 2, please refer to Figure 2The present application provides a dental treatment plan generation system based on user image data, comprising: an image information acquisition unit, an image preprocessing and analysis unit, a disease type analysis unit and an information output unit, and in combination with Figure 2 It can be known that the functional units are electrically connected in a unidirectional manner.

[0061] An image information acquisition unit, which acquires the user's image data and transmits it to the image preprocessing and analysis unit;

[0062] An image preprocessing and analysis unit processes the acquired image to obtain a preprocessed image, and segments the preprocessed image to obtain a segmented preprocessed image. The specific processing method is the same as the processing process of step 1 in Example 1. Grayscale values corresponding to the segmented preprocessed image are obtained, and abnormal regions are determined based on the grayscale values. The abnormal regions are combined to obtain a combined region. The specific processing method is the same as the processing process of step 2 in Example 1. Then, the disease type corresponding to the combined region is identified to obtain disease type information, and the disease type information is transmitted to the disease type information analysis unit. The specific processing method is the same as the processing process of step 3 in Example 1.

[0063] a symptom type analysis unit, which obtains and analyzes abnormal parameters corresponding to the combined area for a single type of result in the symptom type information, performs specific solution generation analysis based on the user's historical data, and generates diagnostic solution information. The specific processing method is similar to the processing process of step three in embodiment one. The symptom type information is analyzed for multiple types of results. By analyzing the historical data and screening the diagnosis and treatment solutions based on the relationship between the newly added symptom in the multiple type results and the original symptom, the diagnostic solution information is generated. The specific processing method is similar to the processing process of step four in embodiment one. The generated diagnostic solution information is transmitted to the information output unit.

[0064] An information output unit is used to display the acquired diagnostic solution information to the corresponding operator.

[0065] At the same time, the contents not described in detail in this specification belong to the existing technology well known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for generating a dental treatment plan based on user image data, characterized in that: The method specifically comprises the following steps: Step 1: Acquire the user's image data, perform image processing to obtain a pre-processed image, and segment the pre-processed image to obtain a segmented pre-processed image; Step 2: Analyze the obtained segmentation preprocessing image, obtain the grayscale value corresponding to the segmentation preprocessing image, determine the abnormal area according to the grayscale value, and combine the abnormal areas to obtain the combined area; Step 3: Analyze the obtained combined area, identify the disease type corresponding to the combined area to obtain disease type information, obtain and analyze the abnormal parameters corresponding to the combined area for a single type of disease in the disease type information, combine the user's historical data to perform specific solution generation and analysis, and generate diagnostic solution information; Step 4: Analyze the symptom type information into multiple categories of results, analyze historical data, and screen the diagnosis and treatment plans based on the relationship between the newly added symptoms and the original symptoms in the multiple categories of results to generate diagnosis plan information.

2. A method for generating a dental treatment plan based on user image data according to claim 1, characterized in that: The specific method of obtaining the segmentation pre-processed image in step 1 is: The user's image data is obtained by using an oral CBCT device, and then the obtained image data is smoothed and denoised to obtain a preprocessed image. The preprocessed image is then segmented according to tooth partitions to obtain a segmented preprocessed image.

3. The method for generating a dental treatment plan based on user image data according to claim 1, characterized in that: The specific method of analyzing the segmentation pre-processed image in step 2 is: Obtain a segmented preprocessed image and label it as i, where i = 1, 2, ..., j, where j represents the number of segmented preprocessed images. Then, the grayscale value corresponding to the segmented preprocessed image i is recorded as Hi, and the grayscale value Hi is compared with the threshold. If the grayscale value Hi corresponding to the segmented preprocessed image i does not match the threshold, it indicates that the corresponding segmented preprocessed image is abnormal and is marked as an abnormal image. If the grayscale value Hi corresponding to the segmented preprocessed image i matches the threshold, it indicates that the corresponding segmented preprocessed image is normal and is marked as a normal image. All abnormal images are obtained, and then the grayscale values of the abnormal images are obtained. At the same time, the abnormal images corresponding to the grayscale values of the same dimension are obtained and combined to obtain the combined area, and the corresponding morphological features of the combined area are identified to obtain the morphological features of the combined area.

4. The method for generating a dental treatment plan based on user image data according to claim 1, characterized in that: The specific method of analyzing the combined area in step 3 is: The combined area and the corresponding morphological features are obtained, and then the pathological type corresponding to the combined area is identified to generate single-category results and multi-category results. If there are multiple groups of grayscale values, multi-category results are generated. Conversely, if there is only one group of grayscale values, a single-category result is generated and the generated single-category results are analyzed at the same time.

5. The method for generating a dental treatment plan based on user image data according to claim 4, characterized in that: The specific method of analyzing the results of a single type in step 3 is: Obtain the abnormal parameters corresponding to the combined area, then obtain the historical data corresponding to the user, and judge the changes in the abnormal parameters based on the historical data. At the same time, screen the diagnosis and treatment plans according to the changes in the abnormal parameters, and obtain the diagnosis and treatment plans with the same changes in the abnormal parameters as the pre-selected diagnosis and treatment plans; Then, the historical treatment status in the user's historical data is judged. If the user has historical treatment, the user is marked as a treated user. Otherwise, if the user has no historical treatment, the user is marked as an untreated user. At the same time, corresponding plans are generated for the two types of users respectively.

6. The method for generating a dental treatment plan based on user image data according to claim 5, characterized in that: The specific method of generating corresponding solutions for the two types of users in step 3 is as follows: Generate diagnostic plans for treated users, obtain pre-selected diagnostic plans that are the same as those for treated users, and generate diagnostic plan information based on the pre-selected diagnostic plans obtained; Generate diagnostic plans for untreated users, obtain pre-selected diagnostic and treatment plans, and screen the diagnostic plans based on the current grayscale value and generate diagnostic plan information.

7. The method for generating a dental treatment plan based on user image data according to claim 1, characterized in that: The specific method of analyzing multiple types of results in step 4 is: Obtain the generated multiple-category results, and at the same time obtain the historical data corresponding to the user, and analyze the historical data. If the multiple-category results do not exist in the historical data, it means that the user has a new symptom, and a new symptom signal is generated. Conversely, if the multiple-category results exist in the historical data, it means that the user does not have a new symptom, and a no-new symptom signal is generated. Then, the new symptom signal and the no-new symptom signal are analyzed separately.

8. The method for generating a dental treatment plan based on user image data according to claim 7, characterized in that: The specific method of analyzing the newly added pathological signals and the non-newly added signals in step 4 is: Analyze the newly added symptom signal to obtain the corresponding newly added symptom from multiple categories of results, and then judge the correlation between the newly added symptom and the original symptom. If the newly added symptom is correlated with the original symptom, generate a correlation analysis result; otherwise, if the newly added symptom is not correlated with the original symptom, generate a non-correlation analysis result. Obtain the generated association analysis results, then obtain all diagnosis and treatment plans, and at the same time obtain similar situations in the diagnosis and treatment plans and record them as the diagnosis and treatment plans to be analyzed, and screen the diagnosis and treatment plans to be analyzed based on the abnormal parameters corresponding to the newly added symptoms and the original symptoms to obtain the diagnosis plan, and generate diagnosis plan information; Obtain the generated non-correlated analysis results, and use the abnormal parameters corresponding to the newly added symptoms and the original symptoms as the standard to screen the diagnosis and treatment plans to obtain a diagnosis plan, and generate diagnosis plan information at the same time; Analyze the new signals that have not been added, obtain multiple types of symptoms corresponding to multiple types of results, and at the same time, use the abnormal parameters corresponding to the multiple types of symptoms as the standard to screen the treatment plans to obtain the screening treatment plans. Then, obtain the abnormal parameter change values corresponding to the multiple types of symptoms, and use the generated abnormal parameter change values as the standard to match the screening treatment plans to obtain the diagnosis plan, and generate the diagnosis plan information at the same time.

9. A dental treatment plan generation system based on user image data, used to execute a dental treatment plan generation method based on user image data according to any one of claims 1 to 8, characterized in that: include: An image information acquisition unit, which acquires the user's image data and transmits it to the image preprocessing and analysis unit; an image preprocessing and analysis unit, which processes the acquired image to obtain a preprocessed image, and segments the preprocessed image to obtain a segmented preprocessed image, obtains grayscale values corresponding to the segmented preprocessed image, determines abnormal regions based on the grayscale values, combines the abnormal regions to obtain combined regions, and then identifies the pathology types corresponding to the combined regions to obtain pathology type information, and transmits the pathology type information to the pathology type information analysis unit; a symptom type analysis unit, which obtains and analyzes abnormal parameters corresponding to the combined area for a single type of result in the symptom type information, performs specific plan generation and analysis in combination with the user's historical data, and generates diagnostic plan information; analyzes symptom type information with multiple types of results, analyzes historical data, and screens treatment plans based on the relationship between the newly added symptom in the multiple type results and the original symptom, generates diagnostic plan information, and transmits the generated diagnostic plan information to the information output unit; An information output unit is used to display the acquired diagnostic solution information to the corresponding operator.

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