A method and system for generating dental treatment plans based on user image data

Abstract

The application discloses a dental diagnosis and treatment scheme generation method and system based on user image data, and relates to the technical field of dental treatment. The application solves the technical problem that the historical data of patients and the comprehensive conditions of various diseases are not fully combined, and the system and accuracy are lacking in disease identification. The application can obtain more detailed and accurate disease characteristics by segmenting the pretreated image according to tooth partition, adopting an edge detection algorithm to identify morphological characteristics of the combined area, and judging the change of the user historical data. Whether there is a new disease is determined by analyzing the historical data. The relevance of the new disease and the original disease is analyzed in depth based on the image characteristics. The diagnosis and treatment scheme is finely screened according to the relevance of the new disease and the original disease and abnormal parameters, so that the final generated diagnosis scheme information can comprehensively consider various diseases, and the comprehensiveness and effectiveness of treatment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dental treatment, in particular to a dental diagnosis and treatment scheme generation method and system based on user image data. BACKGROUND

[0002] With the enhancement of people's oral health awareness and the improvement of the demand for tooth aesthetics, the demand for oral treatment / dental correction is increasing, but despite this, patients going to treatment still have great doubts about oral treatment.

[0003] Patent application with publication number CN108877897B discloses a dental diagnosis and treatment scheme generation method, device and system, which comprises: acquiring oral image information; sending the oral image information to two or more remote diagnosis and treatment addresses for analysis; receiving the analysis results returned by the corresponding remote diagnosis and treatment addresses to obtain a comprehensive analysis result; according to the comprehensive analysis result, performing image processing on the oral image information to form a dynamic image, the dynamic image at least including an initial dental film simulation image and a diagnosis and treatment result simulation image; and displaying the dynamic image.

[0004] However, part of the existing diagnosis scheme generation method stops at a relatively basic level when using image data processing, and in the generation of diagnosis and treatment schemes, the historical data of patients and the comprehensive situation of multiple diseases are not fully combined, and in the identification of diseases, the system and precision are lacking. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a dental diagnosis and treatment scheme generation method and system based on user image data, which solves the problem of not fully combining the historical data of patients and the comprehensive situation of multiple diseases, and lacks system and precision in disease identification.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme: a dental diagnosis and treatment scheme generation method based on user image data, which specifically comprises the following steps:

[0007] Step 1: acquire the image data of the user and perform image processing to obtain a preprocessed image, and segment the preprocessed image to obtain a segmented preprocessed image;

[0008] Step 2: analyze the segmented preprocessed image obtained, acquire the gray value corresponding to the segmented preprocessed image, determine the abnormal area according to the gray value, and combine the abnormal area to obtain a combined area;

[0009] Step three: analyzing the obtained combined area, identifying the disease category information corresponding to the combined area, and generating diagnosis scheme information according to the single category result in the disease category information, and analyzing the specific scheme generation according to the user historical data, and generating diagnosis scheme information;

[0010] Step four: analyzing the disease category information for multiple category results, analyzing the historical data, and screening the diagnosis and treatment scheme based on the relationship between the new disease and the original disease in the multiple category results, and generating diagnosis scheme information.

[0011] As a further scheme of the present application, the specific way of obtaining the segmented preprocessing image in step one is:

[0012] The image data of the user is obtained by using the oral CBCT equipment, then the obtained image data is smoothed and denoised to obtain a preprocessing image, and then the preprocessing image is segmented according to the tooth partition to obtain a segmented preprocessing image.

[0013] As a further scheme of the present application, the specific way of analyzing the segmented preprocessing image in step two is:

[0014] The segmented preprocessing image is obtained and labeled as i, and i=1, 2, …, j, wherein j represents the number of segmented preprocessing images, then the gray value corresponding to the segmented preprocessing image i is recorded as Hi, and the gray value Hi is compared with the threshold value, if the gray value Hi corresponding to the segmented preprocessing image i does not match the threshold value, it indicates that there is an abnormality in the corresponding segmented preprocessing image, and it is marked as an abnormal image, if the gray value Hi corresponding to the segmented preprocessing image i matches the threshold value, it indicates that the corresponding segmented preprocessing image is normal, and it is marked as a normal image.

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

[0016] As a further scheme of the present application, the specific way of analyzing the combined area in step three is:

[0017] The combined area and the corresponding morphological features are obtained, then the disease category corresponding to the combined area is identified to generate a single category result and a multiple category result, and the specific identification method is to judge the gray value corresponding to the combined area, if there are multiple groups of gray values, a multiple category result is generated, otherwise if there is only one group of gray values, a single category result is generated, and the generated single category result is analyzed.

[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 signal is generated. Then, the new symptom signal and the no-new 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 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 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 two: analyzing the obtained segmented pre-processing image, acquiring the gray value corresponding to the segmented pre-processing image, determining the abnormal area according to the gray value, and combining the abnormal areas to obtain a combined area.

[0046] The segmented pre-processing image is obtained and labeled as i, and i = 1, 2, …, j, where j represents the number of segmented pre-processing images. Then the gray value corresponding to the segmented pre-processing image i is labeled as Hi, and the gray value is acquired by a special medical image reading software, such as a DICOM (Digital Imaging and Communications in Medicine) image viewer. The gray value Hi is compared with a threshold value, and the specific value of the threshold value is set by an operator, which specifically represents 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), and the corresponding threshold value can be set to 1000-2000HU. If the gray value Hi corresponding to the segmented pre-processing image i does not match the threshold value, it indicates that there is an abnormality in the corresponding segmented pre-processing image, and it is marked as an abnormal image. If the gray value Hi corresponding to the segmented pre-processing image i matches the threshold value, it indicates that the corresponding segmented pre-processing image is normal, and it is marked as a normal image. The judgment of matching and not matching is based on whether the gray value exists in the threshold value interval. If it exists, it means matching, and if it does not exist, it means not matching.

[0047] All abnormal images are obtained, and then the gray values of the abnormal images are acquired. The abnormal images corresponding to the same dimension gray value are obtained and combined to obtain a combined area, and the same dimension here means that the gray value is in the same threshold interval. The morphological features of the combined area are obtained by corresponding morphological feature recognition, and the specific morphological feature recognition is obtained by an edge detection algorithm, such as the Canny edge detection algorithm.

[0048] Step three: analyzing the obtained combined area, identifying the disease type information by identifying the disease type corresponding to the combined area, and generating a diagnosis scheme information by acquiring and analyzing the abnormal parameters corresponding to the combined area for a single type result in the disease type information, combining user historical data to generate a specific scheme analysis, and generating a diagnosis scheme information.

[0049] Obtaining the combined region and the corresponding morphological features, then identifying the disease category corresponding to the combined region, generating single category results and multi-category results, and the specific identification method is to judge the gray value corresponding to the combined region, if there are multiple groups of gray values, specifically, there are multiple groups of threshold intervals corresponding to the gray value, for example, in an oral CBCT image, we observe that a certain tooth region has a lower gray value, which is in the threshold interval representing caries (such as 800-1000HU), while the adjacent part has a higher gray value, which falls in the threshold interval representing the inflammation region of the dental pulp (such as 1200-1500HU), then generate multi-category results, otherwise if there is only one group of gray values, for example, the gray value of a certain tooth region is always stable in the threshold interval representing the periodontal lesion region (such as 1000-1300HU), then generate single category results, and analyze the generated single category results;

[0050] Obtaining the generated single category results, and obtaining the abnormal parameters corresponding to the combined region, and the abnormal parameters include the area and the perimeter in the morphological features, then obtaining the historical data corresponding to the user, and judging the change of the abnormal parameters based on the historical data, and the change here specifically represents the change value of the abnormal parameters, such as the area change value and the perimeter change value, and the diagnosis and treatment scheme is selected according to the change of the abnormal parameters, and the diagnosis and treatment scheme is selected as the pre-selected diagnosis and treatment scheme, and the diagnosis and treatment scheme here represents all diagnosis and treatment schemes for the current combined region;

[0051] Then judge the historical treatment in the user historical data, if the user has historical treatment, mark the user as a treated user, otherwise if the user has no historical treatment, mark the user as an untreated user, and generate the corresponding scheme for the two types of users respectively;

[0052] The method for generating diagnosis scheme for the treated user is to obtain the pre-selected diagnosis and treatment scheme same as the treated user, and generate diagnosis scheme information based on the pre-selected diagnosis and treatment scheme obtained by screening;

[0053] The method for generating diagnosis scheme for the untreated user is to obtain the pre-selected diagnosis and treatment scheme, and to select the diagnosis scheme based on the current gray value, and to generate the diagnosis scheme information, specifically, to obtain the gray value corresponding to the pre-selected diagnosis and treatment scheme, and to select the pre-selected diagnosis and treatment scheme corresponding to the minimum difference value of the current gray value as the standard.

[0054] Step four: analyze the disease category information for multi-category results, select the diagnosis and treatment scheme based on the relationship between the new disease and the original disease in the multi-category results, and generate the diagnosis scheme information.

[0055] The generated multi-class results are obtained, and the historical data corresponding to the user is obtained. The historical data is analyzed. If the multi-class results do not exist in the historical data, it indicates that the user has a new disease state, and a new disease state signal is generated. Otherwise, if the multi-class results exist in the historical data, it indicates that the user does not have a new disease state, and a non-new signal is generated. Here, the new indicates the existence of a different type of disease state from the historical data, and the non-new indicates that the type of disease state in the historical data is the same as the current type of disease state. Then, the new disease state signal and the non-new signal are analyzed respectively.

[0056] The new disease state signal is analyzed to obtain the corresponding new disease state in the multi-class results. Then, the relevance of the new disease state and the original disease state is judged. Here, the judgment is based on image features for analysis. For example, if a low-density shadow of periapical periodontitis is found adjacent to the caries area in the CBCT image, it indicates that caries may be the cause of periapical periodontitis. Because caries is not treated in time, bacteria can infect the periapical tissue through the dental pulp cavity, causing periapical periodontitis. If the new disease state and the original disease state have relevance, a correlation analysis result is generated. Otherwise, if the new disease state and the original disease state have no relevance, a non-correlation analysis result is generated.

[0057] The generated correlation analysis result is obtained. Then, all diagnosis and treatment schemes are obtained. Similar cases in the diagnosis and treatment schemes are obtained and recorded as to-be-analyzed diagnosis and treatment schemes. Here, the similar case indicates the diagnosis and treatment scheme corresponding to the relevance between the new disease state and the original disease state. The to-be-analyzed diagnosis and treatment scheme is selected based on the abnormal parameters corresponding to the new disease state and the original disease state to obtain a diagnosis scheme. Diagnosis scheme information is generated. Specifically, the abnormal parameters in the to-be-analyzed diagnosis and treatment scheme are compared with the current abnormal parameters, and the to-be-analyzed diagnosis and treatment scheme corresponding to the minimum difference value is selected as the standard and recorded as the diagnosis scheme.

[0058] The generated non-correlation analysis result is obtained. The to-be-analyzed diagnosis and treatment scheme is selected based on the abnormal parameters corresponding to the new disease state and the original disease state to obtain a diagnosis scheme. Diagnosis scheme information is generated.

[0059] The non-new signal is analyzed to obtain the multi-type disease state corresponding to the multi-class results. The diagnosis and treatment scheme is selected based on the abnormal parameters corresponding to the multi-type disease state to obtain a screened diagnosis and treatment scheme. Then, the abnormal parameter change value corresponding to the multi-type disease state is obtained. Here, the abnormal parameter change value is the change value within a time t. The specific value of the time t is set by an operator. The screened diagnosis and treatment scheme is matched based on the generated abnormal parameter change value to obtain a diagnosis scheme. Specifically, the screened diagnosis and treatment scheme corresponding to the same abnormal parameter change value is obtained and recorded as the diagnosis scheme. Diagnosis scheme information is generated.

[0060] Embodiment two, 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] Meanwhile, the contents not described in detail in this specification belong to the prior art 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 symptom type corresponding to the combined area to obtain symptom type information, obtain and analyze the abnormal parameters corresponding to the combined area for a single type of symptom in the symptom type information, combine the user's historical data to generate a specific solution analysis, and generate diagnostic solution information. The specific processing method is as follows: Obtain 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. If there are multiple groups of grayscale values, then generate multi-category results. Conversely, if there is only one group of grayscale values, then generate a single-category result, and analyze the generated single-category results. 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. 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, record the grayscale value corresponding to the segmented preprocessed image i as Hi, and compare the grayscale value Hi with the threshold. 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. 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 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.

5. 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 signal is generated. Then, the new symptom signal and the no-new signal are analyzed separately.

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 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.

7. 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 6, 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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