Children oral cavity cleaning scoring method and system

Abstract

The invention discloses a children oral cleaning scoring method and system, and relates to the technical field of oral cleaning, and the method comprises the following steps: obtaining the basic identity information and cleaning behavior information of children participating in oral cleaning scoring; marking the current oral cavity development period of the child through information identification and behavior analysis, and preliminarily evaluating the cleaning behavior score of the child according to the behavior habit of the oral cavity development period; generating oral evaluation indexes through data processing and feature extraction of oral examination data, inputting the oral evaluation indexes into the constructed multi-instance scoring model, and evaluating oral health scores; and integrating the cleaning behavior score and the oral health score, and comprehensively outputting an oral cleaning score. According to the method, the cleaning behavior can be individually evaluated according to the oral cavity development period of the child, the problem that a traditional scoring method is insufficient in applicability to the child in different development stages is avoided, the overall oral cavity cleaning level of the child is more comprehensively reflected, and the practicability and pertinence of an evaluation report are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oral cavity cleaning, and particularly relates to a method and system for scoring children's oral cavity cleaning. Background Art

[0002] Oral health is an important part of overall health. Good oral health not only concerns the state of teeth and gums, but also affects digestion, nutrient absorption, and mental health. The key to maintaining oral health lies in regular oral examinations, cleaning, and developing good oral hygiene habits. Oral cavity cleaning mainly includes brushing teeth, using dental floss, etc., aiming to remove food residues and dental plaque on the tooth surface and between tooth gaps, and prevent dental caries and gum diseases. Daily cleaning should include brushing teeth at least twice a day, using fluoride toothpaste, and regularly replacing toothbrushes to maintain the cleaning effect.

[0003] Children's oral cavity cleaning is particularly important because their teeth and gums are more vulnerable and susceptible to bacterial invasion. When children are about two years old, parents should start helping them with oral cavity cleaning and teach them the correct toothbrushing method. In children's oral cavity cleaning, using soft toothbrushes and low-fluoride toothpaste specifically for children is a recommended choice. Since children's toothbrushing skills are not yet mature, parents should accompany and guide them to ensure that every part can be adequately cleaned. In addition, regular dental examinations and professional cleaning cannot be ignored, which can promptly detect potential problems and intervene.

[0004] Current oral cavity cleaning detection and scoring technologies mainly focus on imaging analysis, manual inspection, sensor technology, etc. Common oral cavity cleaning detection means include dental plaque staining detection, oral endoscope imaging analysis, periodontal probe detection, optical detection, and using image recognition technology to evaluate the cleanliness and health status of teeth. In addition, some intelligent toothbrushes and cleaning devices are equipped with sensors that can real-time feedback data such as brushing strength and duration, and give cleaning scores through supporting software. These technologies have high accuracy and operability when applied to the adult population.

[0005] However, there are certain particularities and challenges in children's oral cavity cleaning detection and scoring. First of all, children are in the mixed dentition stage, with deciduous teeth and permanent teeth coexisting, and the teeth are not yet fully developed, so the difficulty of tooth cleaning is relatively large. Secondly, children's toothbrushing habits are often not standardized, and it is easy to miss cleaning areas, making the traditional scoring method ineffective for children. And although the dental plaque staining detection for children is intuitive, concerns about the impact of the staining agent on tooth health and psychology limit the application of this technology. Summary of the Invention

[0006] Based on this, it is necessary to provide a method and system for scoring children's oral cavity cleaning in view of the above technical problems.

[0007] In a first aspect, the present invention provides a method for scoring children's oral hygiene, the method comprising the following steps: S1. Obtain the basic identity information and cleaning behavior information of children participating in oral hygiene scoring, and detect the oral examination data of the children through an oral health detection device.

[0008] S2. Mark the current oral development stage of the children through information recognition and behavior analysis, and preliminarily evaluate the cleaning behavior score of the children according to the behavior habits in the current oral development stage.

[0009] S3. Generate oral evaluation indicators through data processing and feature extraction of the oral examination data, input them into a constructed multi-instance scoring model, and evaluate the oral health score.

[0010] S4. Integrate the cleaning behavior score and the oral health score, comprehensively output the oral hygiene score, and generate a scoring evaluation report according to the scoring results, display the oral hygiene score of the children through a data visualization tool, and provide personalized cleaning improvement suggestions.

[0011] Generating oral evaluation indicators through data processing and feature extraction of the oral examination data, inputting them into a constructed multi-instance scoring model, and evaluating the oral health score includes the following steps: S31. Divide the oral examination data into image data and numerical data according to the detection method of the oral examination data, and perform data preprocessing by adopting corresponding data processing methods according to the data types.

[0012] S32. Extract the effective features of each type of oral examination data to generate oral evaluation indicators.

[0013] S33. Set the index value range and convert the oral evaluation indicators into multiple groups of binary inputs.

[0014] S34. Establish a multi-instance scoring model by defining packages and examples, calculate the individual scores of each example respectively, and then integrate and calculate the overall score of the package as the oral health score.

[0015] Further, marking the current oral development stage of the children through information recognition and behavior analysis, and preliminarily evaluating the cleaning behavior score of the children according to the behavior habits in the current oral development stage includes the following steps: S21. Mark the current oral development stage of the children by extracting and recognizing the age information, gender and tooth growth record in the basic identity information, and the oral development stage includes the primary tooth eruption period, the primary tooth occlusion completion period, the mixed dentition period and the permanent dentition period.

[0016] S22. By identifying the cleaning behavior information, extract the cleaning frequency, cleaning duration, cleaning tools and eating habits of the child, and match the corresponding cleaning scoring criteria according to the current oral development stage.

[0017] S23. According to the cleaning scoring criteria, evaluate the actual scores of the data in the cleaning behavior information, and use the method of cumulative summation to take the calculation result as the cleaning behavior score of the preliminary evaluation.

[0018] Furthermore, extracting the effective features of various types of oral examination data and generating oral evaluation indicators includes the following steps: S321. Extract the image data after data preprocessing, use a deep learning model to automatically segment the teeth, identify the position and shape of each tooth, and use a classification model for identification according to the current oral development stage to mark the type and number of each tooth.

[0019] S322. Detect the health status of each tooth, extract the cleaning effect features by analyzing the tooth surface image, and use the image analysis method to quantify the cleaning effect to form feature indicators.

[0020] S323. Extract the numerical data after data preprocessing, match each type of numerical data with the tooth position in the image data, and map it to the corresponding tooth number.

[0021] S324. According to the tooth position distribution, integrate the feature indicators extracted from the image data and the numerical data, and combine with the tooth numbers to generate oral evaluation indicators.

[0022] Furthermore, setting the index value range and converting the oral evaluation indicators into multiple groups of binary inputs includes the following steps: S331. Obtain the quantified oral evaluation indicators, divide the value range of each oral evaluation indicator into (1, C n -1) according to the nine-point system, and set the classification threshold. For each classification threshold, generate a binary input, and C n represents the total score, and the classification threshold ∈ (1, C n -1).

[0023] S332. For the divided classification thresholds, construct binary classifiers. Each type of oral evaluation indicator corresponds to a binary classifier. Take the corresponding oral evaluation indicator of each tooth as the input of the binary classifier and output the corresponding binary result.

[0024] S333. Integrate the binary results corresponding to each tooth to form multiple groups of binary inputs.

[0025] Further, by defining packages and examples, a multi-instance scoring model is established. The individual scores of each example are calculated separately, and then the overall score of the package is calculated through integration, which is used as the oral health score and includes the following steps: S341. Define the entire oral cavity of a child as a package, which includes all oral evaluation indicators. Then, define each tooth as an example, and each example includes the oral evaluation indicators under the corresponding number.

[0026] S342. Integrate the binary classifier and the Gaussian model, adjust the weight values of different binary classifiers to form a combined classifier, and calculate the individual score of each example.

[0027] S343. Integrate the individual scores of all examples, combine the distribution of pathological individuals, construct a multi-instance scoring model, and output the oral health score of the entire oral cavity through model calculation.

[0028] Further, integrating the binary classifier and the Gaussian model, adjusting the weight values of different binary classifiers to form a combined classifier, and calculating the individual score of each example includes the following steps: S3421. For each example, use the oral evaluation indicators of each tooth as input features.

[0029] S3422. According to the deviation degree of the position of the binary classifier and the intermediate score, use the Gaussian model to assign weight values to each binary classifier, adjust the contribution degree of each binary classifier to the individual score, and form a combined classifier by integrating all binary classifiers.

[0030] S3423. Input the input features into the combined classifier and calculate the individual score of each example.

[0031] Further, the calculation formula for the individual score of each example is: ; In the formula, S i represents the individual score of the i th example; j represents the serial number of the binary classifier; M represents the number of binary classifiers; g (·) represents the weight function; C i represents the historical average score value of the i th example; C n represents the total score value; f ij represents the score of the i th binary classifier in the j th example.

[0032] Furthermore, integrate the individual scores of all examples, combine the distribution of pathological individuals, and construct a multi-example scoring model. Through model calculation, the oral health score of the entire oral cavity is output, including the following steps: S3431. Obtain the individual scores of all examples in the oral cavity. At the same time, sort out the types of teeth corresponding to each example and assign individual weights to the examples.

[0033] S3432. According to the examination results of the oral cavity and dental health status, count the types and quantities of pathological teeth in the oral cavity, and set a penalty factor to represent the non-linear negative impact.

[0034] S3433. Integrate the scores of all examples and the penalties for pathological individuals to construct a multi-example scoring model. Through model calculation, obtain the oral health score of the child's oral cavity.

[0035] Furthermore, the expression of the multi-example scoring model is: ; In the formula, Q represents the oral health score; S i represents the i th individual score of the example; α i represents the individual weight of the β ith example; N represents the penalty factor; U represents the number of examples;

[0036] In the second aspect, the present invention also provides a children's oral cavity cleaning scoring system, which includes: A data acquisition module for acquiring the basic identity information and cleaning behavior information of children participating in oral cavity cleaning scoring, and detecting the oral examination data of the children through an oral health detection device.

[0037] An identification and analysis module for marking the current oral development stage of children through information identification and behavior analysis, and preliminarily evaluating the cleaning behavior score of children according to the behavior habits in the oral development stage.

[0038] A health assessment module for generating oral assessment indicators through data processing and feature extraction of oral examination data, inputting them into the constructed multi-example scoring model, and evaluating the oral health score.

[0039] A scoring display module for integrating the cleaning behavior score and the oral health score, comprehensively outputting the oral cavity cleaning score, generating a scoring evaluation report according to the scoring result, displaying the oral cavity cleaning score of children through a data visualization tool, and providing personalized cleaning improvement suggestions.

[0040] Among them, the data acquisition module, the recognition and analysis module, the health assessment module, and the scoring and display module are connected in sequence.

[0041] The beneficial effects of the present invention are as follows: 1. Through targeted information recognition and behavior analysis, it is possible to individually evaluate the cleaning behavior according to the oral development stage of children, avoiding the insufficient applicability of traditional scoring methods to children in different development stages; by processing oral examination data through a multi-instance scoring model, ensuring that the health status of each tooth is carefully evaluated, and then integrating the cleaning behavior score and the oral health score, it can more comprehensively reflect the overall oral cleaning level of children; in addition, a dual scoring mechanism combining behavior data and health data, and using data visualization tools to provide personalized cleaning improvement suggestions, greatly improves the practicality and pertinence of the evaluation report, is more suitable for the special oral development characteristics and behavior habits of children, and can provide more accurate and operable suggestions for parents and doctors.

[0042] 2. By generating oral evaluation indicators through data processing and feature extraction of oral examination data, and combining a multi-instance scoring model for oral health scoring, it is possible to perform targeted processing on different types of oral data, ensuring the flexibility and accuracy of the data processing process; secondly, through effective feature extraction, the comprehensiveness and accuracy of oral evaluation indicators are guaranteed; using a multi-instance scoring model, it is possible to independently score each tooth, and then comprehensively calculate the overall oral health score, avoiding the problem of ignoring individual differences in traditional evaluation methods. By introducing a multi-instance model with data type division and binary input, higher scoring accuracy and individual differential analysis are provided; at the same time, by integrating multiple scores, a more comprehensive oral health assessment result is provided for doctors and patients, thus forming a more accurate diagnosis and personalized care suggestions for oral problems, with stronger practicality and guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flowchart of a method for scoring children's oral cleaning according to an embodiment of the present invention; Figure 2 is a schematic block diagram of a system for scoring children's oral cleaning according to an embodiment of the present invention.

[0044] Reference numerals in the drawings: 1. Data acquisition module; 2. Recognition and analysis module; 3. Health assessment module; 4. Scoring and display module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] Please refer to Figure 1 , and a method for scoring children's oral cleaning is provided. The method includes the following steps: S1. Obtain the basic identity information and cleaning behavior information of children participating in oral cleaning scoring, and detect the oral examination data of the children through an oral health detection device.

[0047] Specifically, an electronic health record system can be used to automatically collect children's basic identity information, such as name, age, gender, oral health history, etc. Standardized questionnaires or behavior record forms filled in by parents are used to collect cleaning behavior information. For example, the brushing frequency (times per day), brushing duration (minutes per time), types of toothpaste and toothbrush used, and eating habits (frequency of sweet food intake, etc.).

[0048] Through an oral health detection device, obtain the oral examination data of the children, including image or numerical data such as the health status, cleanliness, caries or dental calculus conditions of the teeth. Numerical and image-based oral data are obtained through conventional oral examination equipment in oral hospitals.

[0049] Among them, the oral health detection device includes related devices such as an oral scanner, a dental X-ray machine, an oral endoscope, a periodontal probe, a laser caries detector, an oral CT, and a digital dental camera, which are used to obtain images or other detection parameters inside the oral cavity, and understand information such as the internal structure of the oral cavity, tooth position, tooth shape, tooth surface information, caries, periodontitis, and gingival status.

[0050] S2. Through information recognition and behavior analysis, mark the current oral development stage of the child, and preliminarily evaluate the cleaning behavior score of the child according to the behavior habits in the oral development stage.

[0051] In the description of the present invention, through information recognition and behavior analysis, marking the current oral development stage of the child, and preliminarily evaluating the cleaning behavior score of the child according to the behavior habits in the oral development stage includes the following steps: S21. Mark the current oral development stage of the child by extracting and recognizing the age information, gender, and tooth growth records in the basic identity information, and the oral development stage includes the primary tooth eruption stage, the primary tooth occlusion completion stage, the mixed dentition stage, and the permanent dentition stage.

[0052] Specifically, according to the collected identity and behavior information, judge the current oral development stage of the child: 1. Primary tooth eruption period (0 - 3 years old): The main event is the eruption of primary teeth, and attention is paid to the number and health status of primary teeth.

[0053] 2. Complete primary tooth occlusion period (3 - 6 years old): The occlusion of primary teeth is gradually completed. In this stage, attention is paid to whether children's oral hygiene habits are guided by their parents.

[0054] 3. Mixed dentition period (6 - 12 years old): Primary teeth are gradually replaced and permanent teeth begin to erupt. In this stage, attention needs to be paid to children's self - cleaning ability and the process of tooth replacement.

[0055] 4. Permanent dentition period (12 years old and above): Mainly composed of permanent teeth, attention is paid to children's independent cleaning ability and the cultivation of good oral hygiene habits.

[0056] S22. By identifying cleaning behavior information, extract children's cleaning frequency, cleaning duration, cleaning tools and eating habits, and match the corresponding cleaning scoring criteria according to the current oral development stage.

[0057] Specifically, according to the characteristics of different oral development stages, set the corresponding scoring criteria. The set scoring items and score values are shown in the following examples.

[0058] 1. Primary tooth eruption period: Mainly evaluate the parent's participation degree, whether there is a habit of helping children brush their teeth, and give a score. The scoring criteria can be set as follows: 1.1. Brushing frequency: Once a day (2 points), twice a day (4 points); 1.2. Parent participation: Yes (3 points), No (0 points).

[0059] 2. Complete primary tooth occlusion period: Pay attention to whether children can brush their teeth independently under the guidance of their parents, and evaluate children's understanding of cleaning behaviors. The scoring criteria can be set as follows: 2.1. Brushing frequency: Once a day (2 points), twice a day (4 points); 2.2. Brushing time: < 2 minutes (1 point), 2 - 3 minutes (3 points), > 3 minutes (5 points); 2.3. Parent guidance: Yes (3 points), No (0 points); 2.4. Brushing tools: Using a special toothbrush or toothpaste (3 points), not using a special toothbrush or toothpaste (0 points).

[0060] 3. Mixed dentition period: Evaluate children's independent brushing ability and brushing skills, such as whether they can use a toothbrush correctly and whether the cleaning is comprehensive. The scoring criteria can be set as follows: 3.1. Brushing frequency: Once a day (2 points), twice a day (4 points); 3.2. Brushing time: < 2 minutes (1 point), 2 - 3 minutes (3 points), > 3 minutes (5 points); 3.3. Toothbrushing skills (e.g., whether all teeth can be cleaned): Yes (5 points), No (0 points).

[0061] 4. Permanent dentition stage: Emphasize the self-management ability of children, and pay attention to whether they can brush their teeth and use dental floss every day. The scoring criteria can be set as follows: 4.1. Toothbrushing frequency: once a day (2 points), twice a day (4 points); 4.2. Brushing time: <2 minutes (1 point), 2-3 minutes (3 points), >3 minutes (5 points); 4.3. Use of dental floss: Yes (5 points), No (0 points).

[0062] S23. According to the cleaning scoring standard, the actual scores of each data in the cleaning behavior information are evaluated, and the calculation results are used as the cleaning behavior scores of the preliminary evaluation by cumulative summation.

[0063] Specifically, the questionnaire results can be quantified to generate a cleaning behavior score. Specific scoring criteria need to be set for each question, and finally the scores are added up to get a comprehensive score. The behavior score is then weighted and adjusted according to the child's developmental stage to ensure the fairness and effectiveness of the score.

[0064] For example, suppose a child in the replacement dentition period brushes twice a day (score 4 points), brushes for 2-3 minutes (score 3 points), and can clean all teeth (score 5 points). And the scoring standard can be set according to the highest score in each oral development stage: for example, for the replacement dentition period, the full score is 20 points, and 12 points can be considered "good", but still needs improvement.

[0065] Ultimately, the specific profiles and scores for each developmental period can be combined to provide a more in-depth oral health analysis and identify areas for improvement in a child's cleaning habits.

[0066] S3. Generate oral assessment indicators through data processing and feature extraction of oral examination data, input them into the constructed multi-instance scoring model, and evaluate the oral health score.

[0067] Specifically, the present invention uses a multi-instance scoring model built with a multi-instance learning algorithm to evaluate children's oral health scores. Multi-instance learning (MIL) is a special machine learning framework that is suitable for processing data with incomplete or unclear labels. In traditional supervised learning, each training sample has a clear label, while in multi-instance learning, data is organized in the form of bags, each bag contains multiple instances, and each bag has only one label, rather than a label for a single instance.

[0068] In other words, a bag is labeled as positive or negative, but it is not clear whether each example within the bag is positive or negative. Multi-instance learning is a powerful tool that can effectively handle complex and ambiguously labeled data. By constructing a multi-instance scoring model, information from multiple examples can be integrated to output meaningful scoring results. Therefore, it also plays a crucial role in the oral cleaning scoring scenario of the present invention.

[0069] In the description of the present invention, by processing and extracting features from oral examination data, oral evaluation metrics are generated and input into the constructed multi-instance scoring model. The steps for evaluating oral health scores include the following: S31: According to the detection methods of oral examination data, it is divided into image data and numerical data, and corresponding data processing methods are adopted according to the data type to perform data preprocessing.

[0070] Specifically, the image data includes images of the internal oral structures, such as dental X-rays, intraoral camera images, tooth surface images, etc. These data are mainly used for visual recognition and image analysis.

[0071] The preprocessing of the image data includes: 1. Denoising processing: Applying filters (such as Gaussian filtering, mean filtering, etc.) to remove noise in the image to improve the image quality. 2. Image enhancement: Using methods such as contrast enhancement and histogram equalization to improve the visibility of the image, making the teeth and other structures more obvious. 3. Standardization processing: Unifying the image size, for example, adjusting all images to the same resolution (such as 256×256 pixels) for subsequent analysis. 4. Image segmentation: Using deep learning models (such as U-Net, FCN, etc.) for image segmentation to accurately identify and extract teeth and other related structures.

[0072] The numerical data includes quantitative data, such as the depth of teeth, cleaning frequency, oral health scores of teeth, age and gender of children, etc. These data are used for statistical analysis and feature extraction.

[0073] The preprocessing of the numerical data includes: 1. Data cleaning: Checking for missing values, outliers, and duplicate data in the dataset and taking corresponding measures (such as filling missing values, deleting outliers, etc.) to ensure the integrity and accuracy of the data. 2. Standardization and normalization: Performing standardization processing (such as Z-score standardization) or normalization processing (scaling the data to the [0, 1] interval) on the numerical data to eliminate the influence of different dimensions on the analysis results. 3. Feature selection: According to correlation analysis and statistical tests (such as analysis of variance, correlation coefficient, etc.), selecting features related to oral health for further analysis to reduce redundant data.

[0074] S32. Extract the effective features of various types of oral examination data to generate oral evaluation indicators.

[0075] In the description of the present invention, extracting the effective features of various types of oral examination data to generate oral evaluation indicators includes the following steps: S321. Extract the image data after data preprocessing, use a deep learning model to automatically segment the teeth, identify the position and shape of each tooth, and according to the current oral development stage, use a classification model for identification to mark the type and number of each tooth.

[0076] S322. Detect the health status of each tooth, and by analyzing the tooth surface image, extract the cleaning effect features, use image analysis methods to quantify the cleaning effect, and form feature indicators.

[0077] Specifically, detect health problems such as tooth defects, dental caries, and discoloration through image analysis, and a classifier (such as a support vector machine or a deep learning model) can be trained to judge the health status of the teeth, such as normal, mild dental caries, moderate dental caries, etc.

[0078] In the process of quantifying the cleaning effect, it is necessary to use image processing techniques (such as edge detection, texture analysis) to extract the cleaning features on the tooth surface. The following features can be considered: 1. Cleanliness index: Analyze the difference between the image after cleaning and the image before cleaning to quantify the effect of removing dirt or plaque.

[0079] 2. Surface smoothness: Evaluate the texture features of the tooth surface, and use texture analysis algorithms (such as LBP, Gabor filter) to calculate the smoothness index.

[0080] 3. Color difference analysis: Use a color space (such as HSV or Lab) to calculate the color change on the tooth surface before and after cleaning to reflect the cleaning effect.

[0081] Quantify the extracted cleaning effect features into numerical indicators (such as cleanliness score, smoothness score, color difference score, etc.), and integrate them according to the set standards to generate a comprehensive cleaning effect feature indicator.

[0082] S323. Extract the numerical data after data preprocessing, match each type of numerical data with the tooth position in the image data, and map it to the corresponding tooth number.

[0083] S324. According to the tooth position distribution, integrate the feature indicators extracted from the image data and the numerical data, and combine with the tooth number to generate oral evaluation indicators.

[0084] Specifically, the oral evaluation indicators may include: 1. Tooth position and shape: Identify the exact position, shape, and type of each tooth, including whether the tooth arrangement is regular, and whether there are problems such as missing teeth or malformations. 2. Health status score: Evaluate the health status of each tooth, usually by observing the appearance, color, wear degree of the tooth, and whether there are diseases such as dental caries and periodontal disease for scoring. 3. Cleaning effect characteristics: Quantify the oral cleaning effect of children, which may include the dirt degree on the tooth surface, the thickness of dental plaque, etc., and this can be achieved through image analysis methods. 4. Hygiene behavior score: Evaluate the oral hygiene behavior of children according to their brushing habits, use of dental floss, eating habits, etc.

[0085] S33. Set the value range of the indicators, and convert the oral evaluation indicators into multiple groups of binary inputs.

[0086] In the description of the present invention, setting the value range of the indicators and converting the oral evaluation indicators into multiple groups of binary inputs includes the following steps: S331. Obtain the quantified oral evaluation indicators. According to the nine-point system, divide the value range of each oral evaluation indicator into (1, C n - 1), and set the classification threshold. For each classification threshold, generate a binary input, and C n represents the total score, and the classification threshold ∈ (1, C n - 1).

[0087] Specifically, in the previous steps, the various oral evaluation indicators have been quantified to obtain the corresponding scoring data. For example, the evaluation indicators may include the cleanliness, health status, caries risk of the teeth, etc. According to the set nine-point scoring system, divide the value range of each evaluation indicator into (1, C n - 1), where C n represents the total score of all indicators (such as 9), and the value range is from 1 to 8. For each evaluation indicator, set the classification threshold according to the actual situation. For example, the threshold can be set to 3, 5, 7, etc., and the specific selection of the threshold should be determined according to clinical experience, literature research, or expert opinions.

[0088] For each classification threshold, generate a binary input, that is, when the oral evaluation indicator is less than or equal to the threshold, the binary result is 0 (indicating not meeting the standard). When the oral evaluation indicator is greater than the threshold, the binary result is 1 (indicating meeting the standard). For example, for an indicator with a score of 6, if the set threshold is 5, the binary result is 1.

[0089] S332. For the divided classification thresholds, construct a binary classifier. Each type of oral evaluation indicator corresponds to a binary classifier. Take the corresponding oral evaluation indicator of each tooth as the input of the binary classifier, and output the corresponding binary result.

[0090] Specifically, the present invention divides the multi - value classification problem into multiple binary classifiers. Specifically, for each example (such as each tooth), the multi - value score of its health status (such as a 9 - point scale score) is divided into several binary classifiers. Each classifier corresponds to a binary decision problem, that is, to judge whether the score of the example is above or below a certain threshold.

[0091] For each type of oral evaluation index (such as cleanliness, health status, etc.), a corresponding binary classifier is constructed respectively. The task of each classifier is to judge the state of the teeth based on the input oral evaluation index. The corresponding oral evaluation index of each tooth is provided as input data to the corresponding binary classifier. For example, if the cleanliness score of a certain tooth is 7, it is used as input and sent to the cleanliness classifier for judgment.

[0092] S333. Integrate the binary results corresponding to each tooth to form multiple groups of binary inputs.

[0093] Specifically, each classifier generates a binary output according to the input score, respectively representing the state of the tooth on a specific oral evaluation index. Finally, a set of binary results will be obtained. For example: Output of the cleanliness classifier: 1 (meeting the standard); Output of the health status classifier: 0 (not meeting the standard); Output of the caries risk classifier: 1 (meeting the standard).

[0094] S34. By defining bags and examples, establish a multi - example scoring model, calculate the individual score of each example respectively, and then calculate the overall score of the package through integration as the oral health score.

[0095] In the description of the present invention, by defining bags and examples, establishing a multi - example scoring model, calculating the individual score of each example respectively, and then calculating the overall score of the package through integration as the oral health score includes the following steps: S341. Define the entire oral cavity of the child as a bag, which contains all oral evaluation indexes, and then define each tooth as an example. Each example contains the oral evaluation indexes under the corresponding number.

[0096] S342. Integrate the binary classifier and the Gaussian model, adjust the weight values of different binary classifiers to form a combined classifier, and calculate the individual score of each example.

[0097] Specifically, in order to assign different weight distributions to different classifiers, a Gaussian model is used to assign weights to each classifier based on the position of the classifier (i.e., the value of the classification threshold) and the degree of deviation from the intermediate score. The core idea of the Gaussian model is to assign higher weights to classifiers close to the intermediate value and lower weights to extreme classifiers at both ends.

[0098] In the description of the present invention, a binary classifier is integrated with a Gaussian model to adjust the weight values of different binary classifiers, form a combined classifier, and calculate the individual score of each example, including the following steps: S3421. For each example, use the oral evaluation index of each tooth as the input feature.

[0099] S3422. According to the position of the binary classifier and the degree of deviation from the intermediate score, use the Gaussian model to assign weight values to each binary classifier, adjust the contribution degree of each binary classifier to the individual score, and form a combined classifier by integrating all binary classifiers.

[0100] S3423. Input the input feature into the combined classifier to calculate the individual score of each example.

[0101] In the description of the present invention, the calculation formula for calculating the individual score of each example is: ; In the formula, S i represents the individual score of the i th example; j represents the binary classifier serial number; M represents the number of binary classifiers; g (·) represents the weight function; C i represents the historical average score value of the i th example; C n represents the total score value; f ij represents the score of the i th example in the j th binary classifier.

[0102] S343. Integrate the individual scores of all examples, combine the distribution of pathological individuals, construct a multi-example scoring model, and through model calculation, output the oral health score of the entire oral cavity.

[0103] In the description of the present invention, integrating the individual scores of all examples, combining the distribution of pathological individuals, constructing a multi-example scoring model, and through model calculation, outputting the oral health score of the entire oral cavity includes the following steps: S3431. Obtain the individual scores of all examples in the oral cavity. Meanwhile, sort out the types of teeth corresponding to each example and assign individual weights to the examples.

[0104] S3432. According to the examination results of the oral cavity and dental health status, count the types and quantities of diseased teeth in the oral cavity, and set a penalty factor to represent the non-linear negative impact.

[0105] S3433. Integrate the scores of all examples and the penalties for diseased individuals to construct a multi-example scoring model. Through model calculation, obtain the oral health score of the child's oral cavity.

[0106] In the description of the present invention, the expression of the multi-example scoring model is: ; In the formula, Q represents the oral health score; S i represents the i th individual score of the example; α i represents the i th individual weight of the example; β represents the penalty factor; N represents the number of examples; U represents the number of diseased teeth in the oral cavity.

[0107] Suppose there is a child's oral health assessment data involving three teeth (A, B, C), and each tooth has different oral assessment indicators and corresponding cleaning behavior scores.

[0108] I. The oral examination data can be shown as: Tooth A: Health status score (8 / 10), cleaning effect score (7 / 10); Tooth B: Health status score (5 / 10), cleaning effect score (6 / 10); Tooth C: Health status score (9 / 10), cleaning effect score (8 / 10); II. For the processing of image data, automatic segmentation of teeth is performed through a deep learning model, and the numbers and positions of teeth A, B, and C are marked. Suppose the image analysis results show that the surfaces of teeth A and C are relatively smooth, while the surface of tooth B has slight dirt. The extracted features include health status and cleaning effect scores, which are mapped to the corresponding tooth numbers.

[0109] III. Generate oral assessment indicators from the processed data: Tooth A: Health status indicator = 8, cleaning effect indicator = 7; Tooth B: Health status indicator = 5, cleaning effect indicator = 6; Tooth C: Health status index = 9, Cleaning effect index = 8; IV. Convert the indicators into multiple groups of binary inputs, set the value range (1 - 10) for each oral assessment indicator and convert it into binary inputs. For example: Health status threshold: Tooth A: 8 (high), Tooth B: 5 (medium), Tooth C: 9 (high); Cleaning effect threshold: Tooth A: 7 (medium), Tooth B: 6 (medium), Tooth C: 8 (high); Converted to: Tooth A: (high, medium), Tooth B: (medium, medium), Tooth C: (high, high) V. Use the combined classifier to calculate the score Input feature preparation: The feature input of Tooth A is (8, 7), Tooth B is (5, 6), and Tooth C is (9, 8).

[0110] Weight assignment and combined classifier construction. Assume that after calculation through the Gaussian model, the weights corresponding to each tooth are obtained: Weight of Tooth A: 0.4, Weight of Tooth B: 0.3, Weight of Tooth C: 0.3. Individual score calculation: Individual score of Tooth A = 0.4×8 + 0.4×7 = 3.2 + 2.8 = 6; Individual score of Tooth B = 0.3×5 + 0.3×6 = 1.5 + 1.8 = 3.3; Individual score of Tooth C = 0.3×9 + 0.3×8 = 2.7 + 2.4 = 5.1.

[0111] VI. Integrate all individual scores to obtain the overall oral health score.

[0112] S4. Integrate the cleaning behavior score and the oral health score, comprehensively output the oral cleaning score, and generate a score evaluation report according to the score result. Display the oral cleaning score of children through a data visualization tool and provide personalized cleaning improvement suggestions.

[0113] Specifically, the comprehensive score can be calculated by the weighted average method. For example, set the same weight (50%) for the cleaning behavior score and the oral health score. Generate a detailed score evaluation report according to the calculation result, and then present the score result in the form of a chart using a data visualization tool.

[0114] Please refer to Figure 2 for a children's oral cleaning scoring system, which includes: Data acquisition module 1, used to acquire the basic identity information and cleaning behavior information of children participating in the oral cleaning score, and detect the oral examination data of the child through an oral health detection device.

[0115] An identification and analysis module 2, which is used to mark the current oral development stage of a child through information identification and behavior analysis, and preliminarily evaluate the cleaning behavior score of the child according to the behavior habits in the oral development stage.

[0116] A health assessment module 3, which is used to generate oral assessment indicators through data processing and feature extraction of oral examination data, input them into the constructed multi-instance scoring model, and evaluate the oral health score.

[0117] A scoring display module 4, which is used to integrate the cleaning behavior score and the oral health score, comprehensively output the oral cleaning score, generate a scoring evaluation report according to the scoring result, display the oral cleaning score of the child through a data visualization tool, and provide personalized cleaning improvement suggestions.

[0118] Among them, the data acquisition module 1, the identification and analysis module 2, the health assessment module 3, and the scoring display module 4 are connected in sequence.

[0119] In summary, by means of the above technical solutions of the present invention, through targeted information identification and behavior analysis, it is possible to individually evaluate the cleaning behavior according to the oral development stage of the child, avoiding the insufficient applicability of traditional scoring methods to children in different development stages; by processing oral examination data through a multi-instance scoring model, ensuring that the health status of each tooth is carefully evaluated, and then integrating the cleaning behavior score and the oral health score, it can more comprehensively reflect the overall oral cleaning level of the child; in addition, a dual scoring mechanism combining behavior data and health data is adopted, and a data visualization tool is used to provide personalized cleaning improvement suggestions, greatly improving the practicality and pertinence of the evaluation report, being more suitable for the special oral development characteristics and behavior habits of children, and being able to provide more accurate and operable suggestions for parents and doctors.

[0120] Generating oral assessment indicators through data processing and feature extraction of oral examination data, and combining with a multi-instance scoring model for oral health scoring, can perform targeted processing on different types of oral data, ensuring the flexibility and accuracy of the data processing process; secondly, through effective feature extraction, the comprehensiveness and accuracy of the oral assessment indicators are guaranteed; using the multi-instance scoring model, it is possible to independently score each tooth, and then comprehensively calculate the health score of the overall oral cavity, avoiding the problem of ignoring individual differences in traditional assessment methods. By introducing a multi-instance model with data type division and binary input, higher scoring accuracy and individual differential analysis are provided; at the same time, by integrating multiple scores, a more comprehensive oral health assessment result is provided for doctors and patients, thus forming a more accurate diagnosis and personalized nursing suggestions for oral problems, with stronger practicality and guidance.

[0121] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially according to the indication of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this document, there is no strict order restriction for the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed and completed at the same moment, but can be executed at different moments, and their execution order is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

Claims

1. A method for scoring children's oral cavity cleaning, characterized in that, Including: S1. Obtain the basic identity information and cleaning behavior information of children participating in oral cleaning score, and detect the oral examination data of the child through an oral health detection device; S2. Mark the current oral development stage of the child through information recognition and behavior analysis, and preliminarily evaluate the cleaning behavior score of the child according to the behavior habits in the current oral development stage; S3. Generate oral evaluation indicators through data processing and feature extraction of oral examination data, input them into the constructed multi-instance scoring model to evaluate the oral health score; S4. Integrate the cleaning behavior score and the oral health score, comprehensively output the oral cleaning score, and generate a score evaluation report according to the score result, display the oral cleaning score of the child through a data visualization tool, and provide personalized cleaning improvement suggestions; Among them, the S3 includes: S32. Extract the effective features of each type of the oral examination data to generate oral evaluation indicators; The S32 includes: S321. Extract the image data after data preprocessing, use a deep learning model to automatically segment the teeth, identify the position and shape of each tooth, and use a classification model for identification according to the current oral development stage to mark the type and number of each tooth; S322. Detect the health status of each tooth, extract the cleaning effect features by analyzing the tooth surface image, and use an image analysis method to quantify the cleaning effect to form feature indicators; S323. Extract the numerical data after data preprocessing, match each type of the numerical data with the tooth positions in the image data, and map them to the corresponding tooth numbers; S324. Integrate the feature indicators extracted from the image data and the numerical data according to the tooth position distribution, combine with the tooth numbers to generate oral evaluation indicators.

2. The method for scoring children's oral cavity cleaning according to claim 1, wherein, The marking of the current oral development stage of the child through information recognition and behavior analysis, and the preliminary evaluation of the cleaning behavior score of the child according to the behavior habits in the current oral development stage include the following steps: S21. Mark the current oral development stage of the child by extracting and identifying the age information, gender and tooth growth record in the basic identity information, and the oral development stage includes the primary tooth eruption stage, the primary tooth occlusion completion stage, the mixed dentition stage and the permanent dentition stage; S22. Identify the cleaning behavior information, extract the cleaning frequency, cleaning duration, cleaning tools and eating habits of the child, and match the corresponding cleaning score standards according to the current oral development stage; S23. Evaluate the actual scores of the data in the cleaning behavior information according to the cleaning score standards, and use the cumulative summation method to take the calculation result as the preliminary evaluated cleaning behavior score.

3. A method for scoring children's oral cavity cleaning according to claim 1, characterized in that Before the extraction of the effective features of each type of the oral examination data to generate oral evaluation indicators, it also includes: S31. Divide the oral examination data into image data and numerical data according to the detection method, and perform data preprocessing according to the data type using the corresponding data processing method; After the extraction of the effective features of each type of the oral examination data to generate oral evaluation indicators, it also includes: S33. Set the index value range, and convert the oral evaluation indicators into multiple groups of binary inputs; S34. Establish a multi-instance scoring model by defining packages and instances, calculate the individual scores of each instance respectively, and then calculate the overall score of the package through integration as the oral health score.

4. A method for scoring children's oral hygiene, according to claim 3, characterized in that, The steps of converting the oral evaluation indicators into multiple groups of binary inputs by setting the value range of the indicators are as follows: S331. Obtain the quantified oral assessment indicators. According to the nine-point system, divide the value range of each of the oral assessment indicators into (1, C n -1), and set a classification threshold. For each of the classification thresholds, generate a binary input, and C n represents the total score, and the classification threshold ∈ (1, C n -1); S332. For the divided classification thresholds, construct binary classifiers. Each type of oral evaluation indicator corresponds to a binary classifier. Take the corresponding oral evaluation indicators of each tooth as the input of the binary classifier and output the corresponding binary results. S333. Integrate the binary results corresponding to each tooth to form multiple groups of binary inputs.

5. The method for scoring children's oral cavity cleaning according to claim 3, wherein The steps of establishing a multi-instance scoring model by defining packages and instances, calculating the individual scores of each instance respectively, and then calculating the overall score of the package through integration as the oral health score are as follows: S341. Define the entire oral cavity of a child as a package, which contains all oral evaluation indicators, and then define each tooth as an instance. Each instance contains the oral evaluation indicators under the corresponding number. S342. Integrate the binary classifier and the Gaussian model, adjust the weight values of different binary classifiers to form a combined classifier, and calculate the individual scores of each instance. S343. Integrate the individual scores of all instances, combine the distribution of pathological individuals, construct a multi-instance scoring model, and calculate through the model to output the oral health score of the entire oral cavity.

6. The method for scoring children's oral cavity cleaning according to claim 5, characterized in that, The steps of integrating the binary classifier and the Gaussian model, adjusting the weight values of different binary classifiers to form a combined classifier, and calculating the individual scores of each instance are as follows: S3421. For each instance, take the oral evaluation indicators of each tooth as input features. S3422. According to the deviation degree of the position of the binary classifier from the middle score, use the Gaussian model to assign weight values to each binary classifier, adjust the contribution degree of each binary classifier to the individual score, and form a combined classifier by integrating all binary classifiers. S3423. Input the input features into the combined classifier and calculate the individual scores of each instance.

7. A method for scoring children's oral cavity cleaning according to claim 6, characterized in that, The calculation formula for the individual score of each instance is: ； In the formula, S i represents the i individual score of the i -th example; j Indicates the serial number of the binary classifier; M Indicates the number of binary classifiers; g (·) represents a weight function; C i Indicates the i historical average rating value of the C n Indicates the total score value; f ij Indicates the i score of the j th binary classifier in the 8. A method for scoring children's oral cavity cleaning according to claim 5, characterized in that, The steps of integrating the individual scores of all instances, combining the distribution of pathological individuals, constructing a multi-instance scoring model, and calculating through the model to output the oral health score of the entire oral cavity are as follows: S3431. Obtain the individual scores of all instances in the oral cavity. At the same time, sort out the types of teeth corresponding to each instance and assign individual weights to the instances. S3432. According to the inspection results of the oral and dental health status, count the types and quantities of pathological teeth in the oral cavity, and set a penalty factor to represent the non-linear negative impact. S3433. Combine the scores of all instances and the penalties of pathological individuals to construct a multi-instance scoring model, and calculate through the model to obtain the oral health score of the child's oral cavity.

9. The method for scoring children's oral cavity cleaning according to claim 8, characterized in that, The expression of the multi-instance scoring model is: ； Wherein, Q represents the oral health score; S i Indicates the i individual score for the α i Represents the individual weight of the i-th example; β denotes the penalty factor; N Indicates the number of examples; U Indicates the number of diseased teeth in the oral cavity.

10. A children's oral cavity cleaning scoring system for implementing the children's oral cavity cleaning scoring method described in any one of claims 1-9, characterized in that, The system includes: A data acquisition module for acquiring the basic identity information and cleaning behavior information of children participating in oral cleaning scoring, and detecting the oral examination data of the child through an oral health detection device. An identification and analysis module, which is used to mark the current oral development stage of a child through information identification and behavior analysis, and preliminarily evaluate the child's cleaning behavior score according to the behavior habits in the current oral development stage; A health assessment module, which is used to generate oral assessment indicators through data processing and feature extraction of oral examination data, input them into a constructed multi-instance scoring model, and evaluate the oral health score; A score display module, which is used to integrate the cleaning behavior score and the oral health score, comprehensively output the oral cleaning score, generate a score evaluation report according to the score result, display the child's oral cleaning score through a data visualization tool, and provide personalized cleaning improvement suggestions; Among them, the data acquisition module, the identification and analysis module, the health assessment module, and the score display module are sequentially connected.

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