Auxiliary diagnosis and treatment system for oral treatment

Abstract

The invention relates to the technical field of auxiliary diagnosis and treatment systems, in particular to an auxiliary diagnosis and treatment system for oral treatment. Comprising an oral cavity data acquisition module, a data preprocessing module, an oral cavity three-dimensional model construction module, an oral cavity tissue health condition quantitative analysis module, an occlusion function evaluation and anomaly recognition module, a preliminary auxiliary diagnosis and treatment scheme making module, an auxiliary diagnosis and treatment scheme simulation module and an auxiliary diagnosis and treatment scheme optimization and adjustment module. According to the method, data such as teeth, periodontium and occlusion relation in the oral cavity of the patient can be comprehensively and accurately obtained through multi-dimensional oral cavity data collection in combination with a data integrity check formula, the problem that in the prior art, the data collection dimension is single and incomplete is solved, and high-quality basic data is provided for subsequent diagnosis and treatment.

Description

Technical Field

[0001] This invention relates to the field of auxiliary diagnostic and treatment systems, and in particular to an auxiliary diagnostic and treatment system for oral treatment. Background Technology

[0002] With increased public awareness of oral health and the growing complexity of oral diseases, the clinical demand for precision, personalization, and safety in oral diagnosis and treatment has significantly increased. On the one hand, diseases such as dental caries, periodontitis, and malocclusion are often accompanied by multi-tissue pathological changes. For example, periodontal disease can lead to alveolar bone resorption and occlusal disorder, making it difficult to diagnose the entire condition with a single symptom. On the other hand, patients have higher expectations for treatment duration, comfort, and long-term effectiveness, and traditional experience-based treatment models can no longer meet the needs of complex cases. However, in the data acquisition stage of existing oral diagnosis and treatment auxiliary systems, traditional technologies mostly use two-dimensional images or single tools for acquisition, which have limited data dimensions and cannot obtain key information such as three-dimensional tooth morphology and dynamic occlusal force, making it difficult to support accurate diagnosis. Summary of the Invention

[0003] In view of the technical problems mentioned in the background art, the present invention provides an auxiliary diagnostic and treatment system for oral treatment.

[0004] The technical solution adopted in this invention is: an auxiliary diagnostic and treatment system for oral treatment, the auxiliary diagnostic and treatment system comprising: The oral cavity data acquisition module collects three-dimensional morphological data of teeth, periodontal tissue data, and occlusal relationship data. The data preprocessing module preprocesses the data acquired by the oral cavity data acquisition module. A three-dimensional oral cavity model construction module, which constructs a three-dimensional model of the patient's oral cavity based on the collected and preprocessed three-dimensional coordinate data of teeth; The oral tissue health status quantitative analysis module is based on preprocessed periodontal tissue data, oral three-dimensional model and oral CT image data to quantitatively analyze the health status of the patient's oral tissues and construct an oral tissue health index. The occlusal function assessment and abnormality identification module assesses the patient's occlusal function based on preprocessed occlusal relationship data and a three-dimensional oral cavity model. The preliminary auxiliary treatment plan formulation module formulates a preliminary oral auxiliary treatment plan based on the quantitative analysis results of oral tissue health status and the occlusal function assessment results, combined with the patient's age, gender, overall health status and treatment needs. The auxiliary treatment plan simulation module uses a three-dimensional oral model and computer simulation technology to simulate the implementation of the initially formulated treatment plan. The auxiliary treatment plan optimization and adjustment module adjusts the initial auxiliary treatment plan based on the results of the auxiliary treatment plan simulation module to form the final auxiliary treatment plan.

[0005] In one embodiment, the oral data acquisition module acquires three-dimensional tooth morphology data, periodontal tissue data, and occlusal relationship data, as follows: Three-dimensional morphological data acquisition of teeth: The teeth in the patient's mouth are scanned using an oral 3D scanner to obtain the three-dimensional coordinate data of the teeth; Let the three-dimensional coordinates of any point on the tooth surface obtained from the scan be... ,in This represents the total number of scan points; using this coordinate data, a three-dimensional geometric model of the tooth is constructed. Periodontal tissue data acquisition: Periodontal pocket depth, attachment level, and gingival bleeding index were measured using a periodontal probe; Let the measured value of periodontal pocket depth be... The adhesion level measurement value is The gingival bleeding index is Meanwhile, tomographic images of periodontal tissues are obtained using oral CT equipment; Occlusal data acquisition: Occlusal force measurement was used to record the occlusal force distribution data of the patient under different occlusal states, including centric occlusion, lateral occlusion, and protruding occlusion; the force value at the tooth contact point under centric occlusion was set as follows. The coordinates of the biting contact point are Record the patient's dynamic occlusal parameters, such as occlusal time and occlusal frequency. Let the occlusal time be... The bite frequency is Introduce the data integrity verification formula: ; in, This is the data integrity coefficient; The number of valid data; This represents the total number of data collected.

[0006] In one embodiment, the data preprocessing module preprocesses the data acquired by the oral cavity data acquisition module, and the specific method is as follows: Noise removal: Noise in tooth 3D coordinate data, periodontal tissue measurement data, and occlusal force data is processed using the moving average filtering method; The Z-score standardization method is used, and the formula is: ; in, The data is standardized. This is the original data; This is the mean of this type of data; This represents the standard deviation of this type of data.

[0007] In one embodiment, the oral cavity 3D model construction module constructs a 3D model of the patient's oral cavity based on the collected and preprocessed 3D tooth coordinate data using the following specific method: Oral 3D model construction: The triangulation algorithm is used to connect the discrete 3D coordinate points on the tooth surface into continuous triangular patches, thereby constructing a 3D mesh model of the tooth. On this basis, combined with the periodontal tissue tomographic image data obtained by oral CT, the periodontal tissue model and the tooth 3D model are fused through image registration to form an oral 3D model. Suppose that in the constructed 3D oral cavity model, the set of vertices of the tooth model is... The set of triangular facets is The periodontal tissue model is registered with the tooth model using a registration matrix. To achieve integration with the dental model; Model accuracy evaluation: Introducing the model accuracy evaluation formula: ; in, The average error of the model; The first part of the constructed oral cavity three-dimensional model The coordinates of the vertices; These are the reference coordinates of the corresponding vertex obtained through a high-precision reference measurement method; The number of vertices used for accuracy evaluation.

[0008] In one embodiment, the oral tissue health status quantitative analysis module performs quantitative analysis on the patient's oral tissue health status based on preprocessed periodontal tissue data, oral three-dimensional model, and oral CT imaging data. The specific method for constructing the oral tissue health index is as follows: Periodontal health analysis: A periodontal health scoring formula was constructed by comprehensively considering periodontal pocket depth, attachment level, gingival bleeding index, and alveolar bone height. ; in, Assess periodontal health; , , , These are the weighting coefficients for periodontal pocket depth, attachment level, gingival bleeding index, and alveolar bone height, respectively. This represents the maximum normal periodontal pocket depth. This represents the maximum value of the normal adhesion level. This is a reference value for normal alveolar bone height; Dental health status analysis: Combining 3D models of teeth and oral CT images, this study analyzes the degree of tooth decay, defects, and pulp health, and constructs a dental health scoring formula. ; in, A health score for a single tooth; For the degree of caries, For the degree of damage, Weighting coefficients for pulp health status; Grades of caries severity; The degree of tooth damage is graded; The level of dental pulp health status; By calculating each tooth in the patient's mouth separately This allows us to obtain the distribution of the overall health status of all teeth. Oral tissue health index calculation: The oral tissue health index formula is constructed by combining periodontal health score and overall dental health score. ; in, Oral tissue health index; The total number of teeth in the patient's mouth; For the first Health score of each tooth.

[0009] In one embodiment, the occlusal function assessment and abnormality identification module assesses the patient's occlusal function based on preprocessed occlusal relationship data and a three-dimensional oral cavity model using the following specific method: Analysis of the uniformity of bite force distribution: Introducing the formula for the uniformity coefficient of bite force distribution: ; in, The coefficient for uniformity of bite force distribution. For the first The force value at each biting contact point; It is the average value of the force at all biting contact points, i.e. This refers to the number of occlusal contact points; Occlusal interference identification: Using a three-dimensional oral model to simulate the contact of teeth in different occlusal states, and combining occlusal force data, occlusal interference is identified; Introducing the formula for the occlusal interference index: ; in, This refers to the occlusal interference index. The number of contact points where there is interlocking interference; This represents the total number of occlusal contact points in this occlusal state. Comprehensive Occlusal Function Score: A comprehensive occlusal function score formula is constructed by combining the uniformity coefficient of occlusal force distribution and the occlusal interference index. ; in, A comprehensive score for occlusal function.

[0010] In one embodiment, the preliminary auxiliary treatment plan formulation module formulates a preliminary oral auxiliary treatment plan based on the quantitative analysis results of oral tissue health status and the occlusal function assessment results, combined with the patient's age, gender, overall health status, and treatment needs. The specific method is as follows: Treatment goals were determined based on a comprehensive score of oral tissue health index and occlusal function. Determine the main goals of diagnosis and treatment; Solution fit evaluation: Introducing the solution fit evaluation formula: ; in, For solution adaptability; The number of treatment items; For the first Weighting coefficients for each treatment item; For the first The degree of match between each treatment program and the patient's condition.

[0011] In one embodiment, the auxiliary treatment plan simulation module utilizes a three-dimensional oral cavity model and computer simulation technology to simulate the implementation of a preliminary treatment plan. The specific method is as follows: Simulation of auxiliary treatment process: Simulation of different treatment procedures on a three-dimensional oral model; Expected Outcome Assessment: Based on data obtained from simulations of the adjunctive treatment process, the expected oral tissue health index after treatment is calculated. Comprehensive score of expected occlusal function Compare with the target values ​​set in the treatment objectives; Introducing the simulation error formula: ; ; in, This represents the simulation error of the oral tissue health index; The target value for the oral tissue health index set in the diagnosis and treatment goals; The simulation error for the comprehensive scoring of occlusal function; The target value for the comprehensive occlusal function score set in the diagnosis and treatment objectives; Treatment risk assessment: During the simulation and validation process, the potential risks during treatment are simultaneously assessed, and a treatment risk assessment formula is introduced: ; in, The number and types of potential risks; For the first Weighting coefficients for various risks; For the first The probability of such risks occurring.

[0012] In one embodiment, the auxiliary treatment plan optimization and adjustment module adjusts the auxiliary treatment plan based on the results of the auxiliary treatment plan simulation module to form the final auxiliary treatment plan. The specific method is as follows: Treatment parameter optimization: For treatment items that are found to have poor treatment effects or pose risks in the simulation verification, optimize their treatment parameters; Treatment sequence adjustment: The treatment sequence is adjusted based on the interaction between treatment items and the urgency of the patient's condition; Final treatment plan determined: After optimizing treatment parameters and adjusting the treatment sequence, the treatment plan was simulated and verified again. If the simulation error... , and treatment risks All requirements were met, and the treatment sequence was rated as reasonable. If the set level is reached, the plan will be determined as the final auxiliary treatment plan.

[0013] The beneficial effects of this invention are as follows: Compared with the prior art, this invention, through multi-dimensional oral data collection and combined with data integrity verification formulas, can comprehensively and accurately obtain data on teeth, periodontal tissues, and occlusal relationships within the patient's oral cavity, solving the problem of single and incomplete data collection dimensions in the prior art, and providing high-quality basic data for subsequent diagnosis and treatment; secondly, through the data preprocessing module, data noise is effectively removed, data format and numerical range are unified, avoiding diagnostic bias caused by doctors' subjective interpretation of data, and improving the usability of data and the accuracy of analysis results; thirdly, by constructing a three-dimensional oral model, the actual structure of the patient's oral cavity can be accurately reflected. Combined with quantitative analysis of oral tissue health status and occlusal function assessment, during the formulation of auxiliary treatment plans, through simulation verification and optimization adjustments, a highly feasible auxiliary treatment plan that closely matches the patient's condition can be developed. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0015] In the description of this invention, it should be noted that the terms "front", "up", "down", "left", "right", "vertical", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0016] refer to Figure 1 To address the problems existing in the background art, this application proposes the following technical solution: an auxiliary diagnostic and treatment system for oral treatment, the auxiliary diagnostic and treatment system comprising: The oral cavity data acquisition module collects three-dimensional morphological data of teeth, periodontal tissue data, and occlusal relationship data. The oral cavity data acquisition module includes the following specific methods for acquiring three-dimensional tooth morphology data, periodontal tissue data, and occlusal relationship data: Three-dimensional morphological data acquisition of teeth: The teeth in the patient's mouth are scanned using an oral 3D scanner to obtain the three-dimensional coordinate data of the teeth; Let the three-dimensional coordinates of any point on the tooth surface obtained from the scan be... ,in This represents the total number of scan points. Using these coordinate data, a three-dimensional geometric model of the tooth can be constructed, which can clearly present detailed features such as the crown shape, occlusal surface shape, and proximal contact relationship of the tooth. Periodontal tissue data acquisition: Periodontal pocket depth, attachment level, and gingival bleeding index were measured using a periodontal probe; Let the measured value of periodontal pocket depth be... ( , (Number of measurement sites), attachment level measurement value The gingival bleeding index is (Values ​​range from 0 to 4, where 0 indicates no bleeding and 4 indicates severe bleeding). Simultaneously, tomographic images of periodontal tissues are obtained using an oral CT scanner to further understand alveolar bone resorption, bone density, and other information. The alveolar bone height measurement value is set as follows: , (Number of measurement sites for alveolar bone); Occlusal data acquisition: Occlusal force measurement was used to record the occlusal force distribution data of the patient under different occlusal states, including centric occlusion, lateral occlusion, and protruding occlusion; the force value at the tooth contact point under centric occlusion was set as follows. ( , (Number of occlusal contact points), coordinates of the occlusal contact points are Record the patient's dynamic occlusal parameters, such as occlusal time and occlusal frequency. Let the occlusal time be... (Unit: s), bite frequency is (Unit: times / min); To ensure the integrity and validity of the collected data, a data integrity verification formula is introduced: ; in, This is the data integrity coefficient, with a value ranging from 0 to 100%. The number of valid data (i.e., the number of data that meets the preset data quality standards, such as measurements being within a reasonable range and without obvious abnormal fluctuations); For the total number of data collected, when If the data collection is deemed successful, the process can proceed to the next step; otherwise... If the data is not up to standard, it needs to be collected again until the data integrity coefficient meets the requirements.

[0017] The above technical solution is explained as follows: By using multiple devices to collaboratively collect three-dimensional morphological data of teeth, periodontal tissues, and occlusal relationships, it breaks through the limitations of traditional two-dimensional acquisition and comprehensively covers complex oral cavity structural information.

[0018] The data preprocessing module preprocesses the data acquired by the oral cavity data acquisition module. The data preprocessing module preprocesses the data acquired by the oral cavity data acquisition module, and the specific method is as follows: Because the raw oral data collected may contain noise (such as systematic errors of measuring equipment, random errors caused by slight movements of the patient during data collection) and inconsistent data formats, it is necessary to preprocess and standardize the data to improve its quality and usability.

[0019] Noise removal: Noise in tooth 3D coordinate data, periodontal tissue measurement data, and occlusal force data is processed using the moving average filtering method; Taking bite force data as an example, the moving average filtering formula is: ; in, The filtered first The force value at each biting contact point; The width of the sliding window (determined based on the data sampling frequency and noise characteristics, generally an odd number between 3 and 7); For the first time before filtering The formula calculates the occlusal force value at each sampling point. Its purpose is to smooth the data curve by averaging the force values ​​from adjacent sampling points, reducing the impact of random noise and making the processed occlusal force data more accurately reflect the actual occlusion. For three-dimensional tooth coordinate data and periodontal tissue measurement data, a similar moving average filtering method can be used, adjusting the sliding window width according to the characteristics of different data.

[0020] Data standardization: Since different types of oral data have different dimensions and numerical ranges (e.g., periodontal pocket depth is measured in mm, occlusal force in N, and gingival bleeding index is dimensionless), data standardization is necessary to facilitate subsequent multi-dimensional data analysis, transforming the data into a unified numerical range. The Z-score standardization method is used, with the formula: ; in, The data is standardized. This is the original data; This is the mean of this type of data; This represents the standard deviation of this type of data.

[0021] Taking periodontal pocket depth data as an example, first calculate all periodontal pocket depth measurements. mean and standard deviation Then each Substituting into the above formula, we obtain the standardized periodontal pocket depth data. .

[0022] The above technical solution is explained as follows: the moving average filtering formula is used to remove noise and smooth the data curve, making the data such as bite force and tooth coordinates closer to the real situation; the Z-score standardization formula is used to eliminate the differences in the dimensions of different data and unify all types of data into the same numerical range.

[0023] A three-dimensional oral cavity model construction module, which constructs a three-dimensional model of the patient's oral cavity based on the collected and preprocessed three-dimensional coordinate data of teeth; The oral cavity 3D model construction module constructs a 3D model of the patient's oral cavity based on the collected and preprocessed 3D tooth coordinate data. The specific method is as follows: Oral 3D model construction: The triangulation algorithm is used to connect the discrete 3D coordinate points on the tooth surface into continuous triangular patches, thereby constructing a 3D mesh model of the tooth. On this basis, combined with the periodontal tissue tomographic image data obtained by oral CT, the periodontal tissue model and the tooth 3D model are fused through image registration technology to form a complete oral 3D model. Suppose that in the constructed 3D oral cavity model, the set of vertices of the tooth model is... The set of triangular facets is (Each triangular facet consists of three vertices), the periodontal tissue model is registered with the tooth model using a registration matrix. (A 3×4 transformation matrix, containing translation, rotation, and scaling parameters) is used to achieve fusion with the tooth model.

[0024] Model accuracy evaluation: To verify the accuracy of the constructed 3D oral cavity model, a model accuracy evaluation formula is introduced: ; in, The average error of the model (unit: mm); The first part of the constructed oral cavity three-dimensional model The coordinates of the vertices; The reference coordinates of the corresponding vertices are obtained through high-precision reference measurement methods (such as scanning the oral cavity model with an industrial-grade 3D scanner); The number of vertices used for accuracy evaluation; when When the model accuracy meets the requirements, it is determined that the accuracy of the model is satisfactory; if If so, the model construction parameters (such as the parameters of the triangulation algorithm, the accuracy of image registration, etc.) need to be re-optimized until the model accuracy meets the standard.

[0025] The above technical solution is explained as follows: A complete 3D model of the oral cavity is constructed using triangulation algorithms and image registration technology, clearly presenting the fused structure of teeth and periodontal tissues. The model's accuracy is quantitatively verified to ensure it truly reflects the oral cavity structure. Compared to existing 2D images, this model can intuitively display oral cavity details.

[0026] The oral tissue health status quantitative analysis module is based on preprocessed periodontal tissue data, oral three-dimensional model and oral CT image data to quantitatively analyze the health status of the patient's oral tissues and construct an oral tissue health index. The above technical solution is explained as follows: a complete three-dimensional model of the oral cavity is constructed using triangulation algorithm and image registration technology, which clearly presents the fusion structure of teeth and periodontal tissues.

[0027] The oral tissue health status quantitative analysis module, based on preprocessed periodontal tissue data, oral three-dimensional model, and oral CT imaging data, quantitatively analyzes the health status of the patient's oral tissues and constructs an oral tissue health index using the following specific method: Periodontal health analysis: A periodontal health scoring formula was constructed by comprehensively considering periodontal pocket depth, attachment level, gingival bleeding index, and alveolar bone height. ; in, Periodontal health is scored on a scale of 0-5, with a higher score indicating better periodontal health. , , , The weighting coefficients for periodontal pocket depth, attachment level, gingival bleeding index, and alveolar bone height are respectively determined through expert scoring and statistical analysis based on the impact of each indicator on periodontal health. The value is generally taken as , , , ); This is the maximum normal periodontal pocket depth (generally taken as 3mm; exceeding this value is considered to indicate an abnormal periodontal pocket). This is the maximum value of the normal adhesion level (generally taken as 2 mm; exceeding this value is considered to indicate adhesion loss). This is a reference value for normal alveolar bone height (determined based on factors such as the patient's age and tooth position). The purpose of this formula is to comprehensively quantify multiple periodontal tissue indicators to form a single periodontal health score, enabling dentists to quickly and intuitively understand the health status of the patient's periodontal tissues and avoid the bias caused by judging based on a single indicator.

[0028] Dental health status analysis: Combining 3D models of teeth and oral CT images, this study analyzes the degree of tooth decay, defects, and pulp health, and constructs a dental health scoring formula. ; in, A health score is given for each individual tooth, ranging from 0 to 5 points. , , The weighting coefficients for the degree of caries, the degree of damage, and the health status of the dental pulp are respectively. The value is generally taken as , , ); The degree of caries is graded (values ​​range from 0 to 4, where 0 indicates no caries, 1 indicates superficial caries, 2 indicates moderate caries, 3 indicates deep caries, and 4 indicates caries penetrating the pulp). The degree of tooth loss is graded (values ​​range from 0 to 4, where 0 indicates no loss, 1 indicates small loss, 2 indicates moderate loss, 3 indicates large loss, and 4 indicates most of the tooth is missing). The pulp health status is graded (values ​​range from 0-4, where 0 indicates healthy pulp, 1 indicates pulp congestion, 2 indicates pulpitis, 3 indicates pulp necrosis, and 4 indicates periapical periodontitis). This is calculated for each tooth in the patient's oral cavity individually. This allows us to obtain the distribution of the overall health status of all teeth, providing detailed dental health information for subsequent diagnosis and treatment planning.

[0029] Oral tissue health index calculation: The oral tissue health index formula is constructed by combining periodontal health score and overall dental health score. ; in, This is an oral tissue health index, with a value range of 0-5; The total number of teeth in the patient's mouth (usually 28-32). For the first A health score for each tooth. The index categorizes oral tissue health into five levels: 5.0-4.0 for healthy, 3.9-3.0 for mild abnormality, 2.9-2.0 for moderate abnormality, 1.9-1.0 for severe abnormality, and below 1.0 for extremely severe abnormality. This index quantifies the overall health of a patient's oral tissues, providing doctors with a clear and consistent basis for diagnosis and assessment of disease severity.

[0030] The above technical solution is explained as follows: By constructing formulas such as periodontal health score, dental health score and oral tissue health index, multiple indicators such as periodontal pocket depth and degree of caries are comprehensively quantified to form a scoring system of 0-5 points.

[0031] The occlusal function assessment and abnormality identification module assesses the patient's occlusal function based on preprocessed occlusal relationship data and a three-dimensional oral cavity model. The occlusal function assessment and abnormality identification module assesses the patient's occlusal function based on preprocessed occlusal relationship data and a three-dimensional oral cavity model using the following specific method: Analysis of the uniformity of bite force distribution: Introducing the formula for the uniformity coefficient of bite force distribution: ; in, This is the uniformity coefficient of bite force distribution, with a value ranging from 0 to 1. The closer it is to 1, the more uniform the bite force distribution. For the first The force value at each biting contact point (after filtering); It is the average value of the force at all biting contact points, i.e. This represents the number of contact points.

[0032] when When the bite force is evenly distributed, it is determined that the bite force is evenly distributed; if Bei believes there is an uneven distribution of occlusal force, requiring further analysis to determine the cause. The purpose of this formula is to objectively assess the uniformity of occlusal force distribution by quantifying the differences in occlusal force distribution, thus providing a basis for identifying abnormal occlusal force distribution. Occlusal interference identification: Using a three-dimensional oral model to simulate the contact of teeth in different occlusal states (centric occlusion, lateral occlusion, and protruding occlusion), combined with occlusal force data, occlusal interference is identified.

[0033] Introducing the formula for the occlusal interference index: ; in, The occlusal interference index ranges from 0% to 100%. The number of contact points where occlusal interference exists (occlusal interference is defined as the non-working side teeth contacting the working side teeth before the working side teeth during a specific occlusal movement, or the contact force being too great and exceeding the normal range). This represents the total number of occlusal contact points in this occlusal state. When At that time, it was determined that there was obvious occlusal interference; if If the occlusal interference is within an acceptable range, then the specific tooth location and occlusal movement stage of the occlusal interference are determined by combining the temporal data of tooth contact (obtained through the dynamic acquisition function of the occlusal force measuring instrument), providing accurate positioning information for the subsequent occlusal adjustment plan.

[0034] Comprehensive Occlusal Function Score: A comprehensive occlusal function score formula is constructed by combining the uniformity coefficient of occlusal force distribution and the occlusal interference index. in, The comprehensive score for occlusal function ranges from 0 to 5 points, with higher scores indicating better occlusal function. 0.6 and 0.4 are the weighting coefficients for the evenness of occlusal force distribution and the occlusal interference index, respectively, and are determined based on their respective influence on occlusal function. When the bite is normal, the bite function is good; 3.0-3.9 indicates mild abnormality; 2.0-2.9 indicates moderate abnormality; and below 2.0 indicates severe abnormality.

[0035] The above technical solution is explained as follows: By using the uniformity coefficient of occlusal force distribution, the occlusal interference index, and the comprehensive scoring formula for occlusal function, the distribution of occlusal force and interference are quantitatively evaluated to form a functional score of 0-5.

[0036] The preliminary auxiliary treatment plan formulation module formulates a preliminary oral auxiliary treatment plan based on the quantitative analysis results of oral tissue health status and the occlusal function assessment results, combined with the patient's age, gender, overall health status and treatment needs. The preliminary auxiliary treatment plan development module, based on the quantitative analysis results of oral tissue health status and the assessment results of occlusal function, combined with the patient's age, gender, overall health status, and treatment needs, develops a preliminary oral auxiliary treatment plan using the following specific methods: Treatment goals were determined based on a comprehensive score of oral tissue health index and occlusal function. Determine the main goals of diagnosis and treatment; For example, if a patient's HI is 2.5 (moderately abnormal), If the HI is 2.8 (moderately abnormal), the main treatment goals include improving periodontal health (raising the HI to above 3.5), repairing tooth defects or treating caries, and adjusting the occlusal relationship (to make the HI higher). (Upgraded to 3.5 or higher), etc.

[0037] Treatment program selection: Based on the health score of each tooth in the oral tissue health status analysis ( ) and periodontal health score ( Based on the results of the occlusal function assessment, appropriate treatment programs are selected. For example, for For teeth with deep caries, filling treatment is recommended; for For patients with moderate periodontal abnormalities, basic periodontal treatments such as scaling and root planing are selected; for patients with uneven occlusal force distribution and significant occlusal interference, occlusal adjustment treatments or restorative treatments (such as porcelain crowns or dental implants to adjust the occlusal relationship) are selected.

[0038] Protocol fit assessment: To ensure that the developed primary and adjuvant treatment plan matches the patient's specific condition, a protocol fit assessment formula is introduced: ; in, The value for the protocol fit is 0-1, with the value closer to 1 indicating a better match between the protocol and the patient's condition. The number of treatment items; For the first The weighting coefficients for each treatment item (determined based on the importance of each treatment item in achieving the treatment goals); For the first The matching degree between a treatment program and the patient's condition (calculated by an expert system based on the patient's specific condition parameters and the applicable conditions of the treatment program, with a value ranging from 0 to 1). For example, for a patient requiring basic periodontal treatment, if the patient's average periodontal pocket depth is 4.5 mm and the average attachment level is 3 mm, meeting the applicable conditions for basic periodontal treatment, then... If the patient also has serious systemic diseases (such as uncontrolled diabetes), it may affect the effectiveness of periodontal treatment. Reduce appropriately to 0.7. When At that time, the initial auxiliary diagnosis and treatment plan showed good fit; if If so, the treatment program or parameters need to be readjusted until the suitability of the treatment plan meets the requirements.

[0039] The above technical solution is explained as follows: the treatment goals are determined by combining the oral tissue health index, occlusal function score and individual patient condition, appropriate treatment items are selected, and the matching degree between the solution and the condition is quantitatively verified by the solution matching degree evaluation formula (0.8 is the passing line).

[0040] The auxiliary treatment plan simulation module uses a three-dimensional oral model and computer simulation technology to simulate the implementation of the initially formulated treatment plan. The auxiliary treatment plan simulation module utilizes a three-dimensional oral cavity model and computer simulation technology to simulate the implementation of the initially formulated treatment plan. The specific method is as follows: Simulation of auxiliary treatment process: Simulation of different treatment procedures on a three-dimensional oral model.

[0041] For example, for dental filling treatments, the simulation covers the process of removing decayed tissue, preparing the cavity, and filling with restorative material; for basic periodontal treatments, it simulates the process of scaling and root planing to remove tartar and plaque; and for occlusal adjustment treatments, it simulates the process of grinding down excessively high cusps or marginal ridges. During the simulation, data on the impact of the treatment procedures on tooth morphology, periodontal tissues, and occlusal relationships are recorded, such as changes in the three-dimensional morphology of the teeth after treatment, the expected change in periodontal pocket depth, and changes in the location and force distribution of occlusal contact points.

[0042] Expected Outcome Assessment: Based on data obtained from simulations of the adjunctive treatment process, the expected oral tissue health index after treatment is calculated. Comprehensive score of expected occlusal function Compare with the target values ​​set in the treatment objectives; Introducing the simulation error formula: ; ; in, This represents the simulation error of the oral tissue health index; The target value for the oral tissue health index set in the diagnosis and treatment goals; The simulation error for the comprehensive scoring of occlusal function; The target value for the comprehensive occlusal function score set in the diagnostic and treatment goals. and At that time, it was determined that the expected effect of the primary auxiliary treatment plan met the treatment objectives; if or If so, it is necessary to analyze the causes of simulation errors, such as inappropriate selection of treatment items or unreasonable setting of treatment parameters, and adjust the primary and auxiliary diagnosis and treatment plan, and re-perform simulation verification until the simulation error meets the requirements.

[0043] Treatment risk assessment: During the simulation and validation process, potential risks during treatment are simultaneously assessed, such as the risk of tooth fracture, periodontal tissue damage, and further deterioration of the occlusal relationship. A treatment risk assessment formula is introduced: ; in, The number and types of potential risks; For the first The weighting coefficients for each risk (determined based on the degree of impact on the patient's oral health after the risk occurs); For the first The probability of this risk occurring (calculated by an expert system based on the characteristics of the treatment, the patient's condition parameters, and historical treatment data, with a value ranging from 0 to 1).

[0044] when At a certain level, the treatment risk is low; 0.2-0.4 indicates moderate risk; and above 0.4 indicates high risk. For high-risk treatment plans, the treatment plan needs to be redesigned, and risk control measures (such as adjusting the treatment sequence and improving treatment methods) need to be implemented to reduce the treatment risk. For moderate-risk plans, monitoring and protective measures need to be strengthened during the treatment process.

[0045] The above technical solution is explained as follows: The treatment process is simulated on a three-dimensional model, and the impact of the treatment on the oral structure is recorded.

[0046] The auxiliary treatment plan optimization and adjustment module adjusts the initial auxiliary treatment plan based on the results of the auxiliary treatment plan simulation module to form the final auxiliary treatment plan.

[0047] The auxiliary treatment plan optimization and adjustment module adjusts the initial auxiliary treatment plan based on the results of the auxiliary treatment plan simulation module to form the final auxiliary treatment plan. The specific method is as follows: Treatment parameter optimization: For treatment programs that are found to have poor treatment effects or pose risks during simulation verification, optimize their treatment parameters.

[0048] For example, in the case of caries filling treatment, if the simulation shows that the filling material does not fit well with the tooth edge, the size and angle parameters of the cavity preparation can be adjusted; in the case of occlusion adjustment treatment, if the simulation shows that the distribution of occlusal force is still uneven after adjustment, the location and amount of tooth grinding can be optimized. Introducing treatment parameters to optimize the objective function: ; in, To optimize the objective function value, a smaller value indicates a better solution; , γ and γ are the weighting coefficients for the simulation error of the oral tissue health index simulation and the simulation error of the comprehensive occlusal function score, respectively, and the treatment risk. The priority of treatment is determined based on the objective of improving health status. (The value is relatively large).

[0049] By adjusting the treatment parameters, the objective function The goal is to minimize these parameters, thereby optimizing the treatment outcomes. This objective function comprehensively considers both treatment effectiveness and risk, guiding the optimization of treatment parameters and ensuring that the optimized treatment plan maintains therapeutic efficacy while minimizing treatment risk.

[0050] Treatment sequence adjustment: The treatment sequence should be adjusted based on the interaction between treatment procedures and the urgency of the patient's condition. For example, for patients with both acute pulpitis and periodontitis, emergency treatment for pulpitis (such as pulpotomy and drainage) should be performed first to relieve the patient's pain symptoms, followed by basic periodontal treatment. For patients requiring dental implant restoration and with occlusal abnormalities, occlusal adjustment treatment should be performed first, followed by implant surgery to ensure normal occlusal relationships of the implants. A formula for evaluating the rationality of the treatment sequence is introduced: in, The treatment sequence is scored to indicate its rationality, with a range of 0-100 points. The higher the score, the more rational the treatment sequence. The number of treatment items; For the first The treatment program and the first The weighting coefficients of the mutual influence between treatment items (determined based on clinical experience and treatment principles; for example, the greater the influence of a preceding treatment on a subsequent treatment, the greater the weighting coefficient). For the first The treatment program in the A rationality coefficient (ranging from 0 to 1, determined based on the logical relationship between treatments and the urgency of the condition) is calculated before each treatment sequence. Value, selection The treatment sequence with the highest value is considered the optimal treatment sequence. This formula quantifies the rationality of the treatment sequence, helping doctors determine the best treatment order and improve treatment efficiency and effectiveness.

[0051] The final plan was determined as follows: After optimizing treatment parameters and adjusting the treatment sequence, the treatment plan was simulated and verified again. If the simulation error... , ) and treatment risks ( All requirements were met, and the treatment sequence rationality score was ( ) reach a high level (e.g.) If the score is 0.5, then the plan is determined as the final treatment plan; if it still does not meet the requirements, the optimization and adjustment process is repeated until the plan meets all the criteria.

[0052] The above technical solution is explained as follows: guided by minimizing the objective function, the treatment parameters are optimized by comprehensively considering simulation errors and treatment risks; In summary, by collecting multi-dimensional oral data and combining it with data integrity verification formulas, we can comprehensively and accurately obtain data on the teeth, periodontal tissues, and occlusal relationships within the patient's oral cavity. This solves the problem of single and incomplete data collection dimensions in existing technologies, providing high-quality basic data for subsequent diagnosis and treatment. Secondly, the introduction of moving average filtering formulas and Z-score standardization formulas effectively removes data noise, unifies data format and numerical range, avoids diagnostic bias caused by doctors' subjective interpretation of data, and improves data usability and the accuracy of analysis results. Thirdly, by constructing a three-dimensional oral model and introducing a model accuracy evaluation formula, we can accurately reflect the true structure of the patient's oral cavity. Combined with quantitative analysis of oral tissue health status and occlusal function assessment, and the introduction of a treatment plan fit evaluation formula during the development of auxiliary treatment plans, along with simulation verification and optimization adjustments, we can develop auxiliary treatment plans that are highly matched to the patient's condition, feasible, have good expected results, and low treatment risks.

[0053] Although embodiments of the invention have been shown and described, the scope of the invention will be defined by the appended claims and their equivalents by those skilled in the art.

Claims

1. An auxiliary diagnostic and treatment system for oral treatment, characterized in that, The auxiliary diagnostic and treatment system includes: The oral cavity data acquisition module collects three-dimensional morphological data of teeth, periodontal tissue data, and occlusal relationship data. The data preprocessing module preprocesses the data acquired by the oral cavity data acquisition module. A three-dimensional oral cavity model construction module, which constructs a three-dimensional model of the patient's oral cavity based on the collected and preprocessed three-dimensional coordinate data of teeth; The oral tissue health status quantitative analysis module is based on preprocessed periodontal tissue data, oral three-dimensional model and oral CT image data to quantitatively analyze the health status of the patient's oral tissues and construct an oral tissue health index. The occlusal function assessment and abnormality identification module assesses the patient's occlusal function based on preprocessed occlusal relationship data and a three-dimensional oral cavity model. The preliminary auxiliary treatment plan formulation module formulates a preliminary oral auxiliary treatment plan based on the quantitative analysis results of oral tissue health status and the occlusal function assessment results, combined with the patient's age, gender, overall health status and treatment needs. An auxiliary treatment plan simulation module, which uses a three-dimensional oral model and computer simulation technology to simulate the implementation of a preliminary auxiliary treatment plan; The auxiliary treatment plan optimization and adjustment module adjusts the initial auxiliary treatment plan based on the results of the auxiliary treatment plan simulation module to form the final auxiliary treatment plan.

2. The auxiliary diagnostic and treatment system for oral treatment according to claim 1, characterized in that, The specific methods by which the oral cavity data acquisition module collects three-dimensional tooth morphology data, periodontal tissue data, and occlusal relationship data are as follows: Three-dimensional morphological data acquisition of teeth: The teeth in the patient's mouth are scanned using an oral 3D scanner to obtain the three-dimensional coordinate data of the teeth; Let the three-dimensional coordinates of any point on the tooth surface obtained from the scan be... ,in This represents the total number of scan points; using this coordinate data, a three-dimensional geometric model of the tooth is constructed. Periodontal tissue data acquisition: Periodontal pocket depth, attachment level, and gingival bleeding index were measured using a periodontal probe; Let the measured value of periodontal pocket depth be... The adhesion level measurement value is The gingival bleeding index is Meanwhile, tomographic images of periodontal tissues are obtained using oral CT equipment; Occlusal data acquisition: Occlusal force measurement was used to record the occlusal force distribution data of the patient under different occlusal states, including centric occlusion, lateral occlusion, and protruding occlusion; the force value at the tooth contact point under centric occlusion was set as follows. The coordinates of the biting contact point are Record the patient's dynamic occlusal parameters, such as occlusal time and occlusal frequency. Let the occlusal time be... The bite frequency is Introduce the data integrity verification formula: ； in, This is the data integrity coefficient; The number of valid data; This represents the total number of data collected.

3. The auxiliary diagnostic and treatment system for oral treatment according to claim 2, characterized in that, The data preprocessing module preprocesses the data acquired by the oral cavity data acquisition module, and the specific method is as follows: Noise removal: Noise in tooth 3D coordinate data, periodontal tissue measurement data, and occlusal force data is processed using the moving average filtering method; The Z-score standardization method is used, and the formula is: ； in, The data is standardized. This is the original data; This is the mean of this type of data; This represents the standard deviation of this type of data.

4. The auxiliary diagnostic and treatment system for oral treatment according to claim 3, characterized in that, The oral cavity 3D model construction module constructs a 3D model of the patient's oral cavity based on the collected and preprocessed 3D tooth coordinate data. The specific method is as follows: Oral 3D model construction: The triangulation algorithm is used to connect the discrete 3D coordinate points on the tooth surface into continuous triangular patches, thereby constructing a 3D mesh model of the tooth. On this basis, combined with the periodontal tissue tomographic image data obtained by oral CT, the periodontal tissue model and the tooth 3D model are fused through image registration to form an oral 3D model. Suppose that in the constructed 3D oral cavity model, the set of vertices of the tooth model is... The set of triangular facets is The periodontal tissue model is registered with the tooth model using a registration matrix. To achieve integration with the dental model; Model accuracy evaluation: Introducing the model accuracy evaluation formula: ； in, The average error of the model; The first part of the constructed oral cavity three-dimensional model The coordinates of the vertices; These are the reference coordinates of the corresponding vertex obtained through a high-precision reference measurement method; The number of vertices used for accuracy evaluation.

5. The auxiliary diagnostic and treatment system for oral treatment according to claim 4, characterized in that, The oral tissue health status quantitative analysis module, based on preprocessed periodontal tissue data, oral three-dimensional model, and oral CT imaging data, quantitatively analyzes the health status of the patient's oral tissues and constructs an oral tissue health index using the following specific method: Periodontal health analysis: A periodontal health scoring formula was constructed by comprehensively considering periodontal pocket depth, attachment level, gingival bleeding index, and alveolar bone height. ； in, Assess periodontal health; , , , These are the weighting coefficients for periodontal pocket depth, attachment level, gingival bleeding index, and alveolar bone height, respectively. This represents the maximum normal periodontal pocket depth. This represents the maximum value of the normal adhesion level. This is a reference value for normal alveolar bone height; Dental health status analysis: Combining 3D models of teeth and oral CT images, this study analyzes the degree of tooth decay, defects, and pulp health, and constructs a dental health scoring formula. ； in, A health score for a single tooth; For the degree of caries, For the degree of damage, Weighting coefficients for pulp health status; Grades of caries severity; Grades of tooth damage; The level of dental pulp health status; By calculating each tooth in the patient's mouth separately This allows us to obtain the distribution of the overall health status of all teeth. Oral tissue health index calculation: The oral tissue health index formula is constructed by combining periodontal health score and overall dental health score. ； in, Oral tissue health index; The total number of teeth in the patient's mouth; For the first Health score of each tooth.

6. The auxiliary diagnostic and treatment system for oral treatment according to claim 5, characterized in that, The occlusal function assessment and abnormality identification module assesses the patient's occlusal function based on preprocessed occlusal relationship data and a three-dimensional oral cavity model using the following specific method: Analysis of the uniformity of bite force distribution: Introducing the formula for the uniformity coefficient of bite force distribution: ； in, The coefficient for uniformity of bite force distribution. For the first The force value at each biting contact point; It is the average value of the force at all biting contact points, i.e. This refers to the number of occlusal contact points; Occlusal interference identification: Using a three-dimensional oral model to simulate the contact of teeth in different occlusal states, and combining occlusal force data, occlusal interference is identified; Introducing the formula for the occlusal interference index: ； in, This refers to the occlusal interference index. The number of contact points where there is interlocking interference; This represents the total number of occlusal contact points in this occlusal state. Comprehensive Occlusal Function Score: A comprehensive occlusal function score formula is constructed by combining the uniformity coefficient of occlusal force distribution and the occlusal interference index. ； in, A comprehensive score for occlusal function.

7. The auxiliary diagnostic and treatment system for oral treatment according to claim 6, characterized in that, The preliminary auxiliary treatment plan development module, based on the quantitative analysis results of oral tissue health status and the assessment results of occlusal function, combined with the patient's age, gender, overall health status, and treatment needs, develops a preliminary oral auxiliary treatment plan using the following specific methods: Treatment goals were determined based on a comprehensive score of oral tissue health index and occlusal function. Determine the main goals of diagnosis and treatment; Solution fit evaluation: Introducing the solution fit evaluation formula: ； in, For solution adaptability; The number of treatment items; For the first Weighting coefficients for each treatment item; For the first The degree of match between each treatment program and the patient's condition.

8. The auxiliary diagnostic and treatment system for oral treatment according to claim 7, characterized in that, The auxiliary treatment plan simulation module utilizes a three-dimensional oral cavity model and computer simulation technology to simulate the implementation of the initially formulated treatment plan. The specific method is as follows: Simulation of auxiliary treatment process: Simulation of different treatment procedures on a three-dimensional oral model; Expected Outcome Assessment: Based on data obtained from simulations of the adjunctive treatment process, the expected oral tissue health index after treatment is calculated. Comprehensive score of expected occlusal function Compare with the target values ​​set in the treatment objectives; Introducing the simulation error formula: ； ； in, This represents the simulation error of the oral tissue health index; The target value for the oral tissue health index set in the diagnosis and treatment goals; The simulation error for the comprehensive scoring of occlusal function; The target value for the comprehensive occlusal function score set in the diagnosis and treatment objectives; Treatment risk assessment: During the simulation and validation process, the potential risks during treatment are simultaneously assessed, and a treatment risk assessment formula is introduced: ； in, The number and types of potential risks; For the first Weighting coefficients for various risks; For the first The probability of such risks occurring.

9. The auxiliary diagnostic and treatment system for oral treatment according to claim 8, characterized in that, The auxiliary treatment plan optimization and adjustment module adjusts the auxiliary treatment plan based on the results of the auxiliary treatment plan simulation module to form the final auxiliary treatment plan. The specific method is as follows: Treatment parameter optimization: For treatment items that are found to have poor treatment effects or pose risks during simulation verification, adjust their treatment parameters; Treatment sequence adjustment: The treatment sequence is adjusted based on the interaction between treatment items and the urgency of the patient's condition; Final treatment plan determined: After optimizing treatment parameters and adjusting the treatment sequence, the treatment plan was simulated and verified again. If the simulation error... , and treatment risks All requirements were met, and the treatment sequence was rated as reasonable. If the set level is reached, the plan will be determined as the final auxiliary treatment plan.

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