Tooth orthodontic treatment scheme planning system

Abstract

The invention relates to the technical field of oral medical treatment, in particular to a tooth orthodontic treatment scheme planning system, which comprises a database module, a processing module and a planning module, the data acquisition module is used for acquiring initial oral data of a patient before treatment and stage monitoring data in a treatment process; the scheme generation module is used for screening out a complete treatment scheme with the highest similarity in the database through feature similarity matching and generating an initial treatment scheme; the risk assessment module is used for extracting risk factors such as periodontal support force and calculating a risk level; the dynamic adjustment module is used for setting a tooth movement distance deviation threshold value according to the risk level, and comparing an actual tooth movement distance deviation value to generate an adjustment treatment scheme; and the doctor rechecking module is used for the doctor to adjust and recheck the initial treatment scheme and the adjusted treatment scheme according to the individual parameters of the patient. According to the scheme, intelligent planning, dynamic adjustment and scientific review of an orthodontic scheme can be realized, and the treatment accuracy and safety are improved.

Description

Technical Field

[0001] This invention relates to the field of oral medical technology, specifically to a system for planning orthodontic treatment plans. Background Technology

[0002] Orthodontic treatment, as an important branch of oral healthcare, plays a crucial role in restoring oral function, improving facial aesthetics, and preventing periodontal disease by adjusting tooth alignment and improving occlusion. With the rapid development of digital technology, the field of orthodontics has gradually shifted from a traditional experience-based model to data-driven precision treatment. The integration of technologies such as oral scanning, 3D modeling, and case databases has propelled orthodontic treatment planning into an intelligent stage.

[0003] In the prior art, for example, Chinese invention patent CN118866386B discloses a deep learning-based auxiliary diagnostic system for orthodontic diseases, including: an image processing module that detects image quality reference coefficients and completes image segmentation to provide standardized data for subsequent analysis; a data processing module that compares the target patient with the object information and medical record information of historical archives to determine a preliminary archive set, and classifies the feature states based on feature position difference values ​​(such as the dispersion of abnormal tooth distance reference values) and feature morphology difference values ​​(such as the deviation of abnormal tooth morphology reference values); a data filtering module that selects a matching strategy according to the feature states, wherein for the first preset feature state with significant feature differences, the matching method is adapted according to the patient category (image similarity matching is used for new patients, and a matching coefficient algorithm that fuses image similarity and correction reference difference values ​​is used for past patients); for the second preset feature state with small feature differences, feature similarity matching or manual intervention is selected according to the abnormal tooth state (whether it contains abnormally worn teeth); and finally, a solution suggestion is generated through a report recommendation module. While this technology has made breakthroughs in the automation of case matching, it still has the following shortcomings: the feature state classification dimension is singular, focusing only on differences in tooth position and shape, resulting in insufficient matching accuracy for high-risk cases (such as patients with severe periodontitis); dynamic adaptability is lacking, the matching of the plan does not associate with the characteristics of the treatment stage (such as the difference in targets between the initial alignment period and the fine adjustment period), and there is a lack of response mechanism to dynamic factors such as changes in tooth movement rate and fluctuations in periodontal status during treatment.

[0004] Therefore, there is an urgent need for a dental orthodontic treatment planning system that can integrate historical case matching and dynamic adjustment of the treatment plan to solve the problems of insufficient adaptability to complex cases and dynamic treatment responsiveness in existing technologies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention aims to provide a dental orthodontic treatment planning system that enables intelligent planning of orthodontic treatment plans, dynamic adjustments during the treatment process, and scientific review, thereby improving treatment accuracy and safety.

[0006] The basic solution provided by this invention: a dental orthodontic treatment planning system, comprising: The database module is used to store the initial oral data of historical patients and the corresponding complete treatment plan, which includes the initial treatment plan, the adjusted treatment plan, and the medical records. The data acquisition module includes an initial data acquisition unit and a phased monitoring unit. The initial data acquisition unit is used to acquire the patient's initial oral data before treatment, and the phased monitoring unit is used to acquire the patient's phased monitoring data during treatment. The treatment plan generation module is used to perform feature similarity matching between the initial oral data of the current patient and the initial oral data of historical patients in the database module, filter out the complete treatment plan corresponding to the historical patient with the highest similarity from the database module, and generate the initial treatment plan for the current patient based on the selected complete treatment plan. The risk assessment module is used to extract risk factors and calculate risk levels based on the patient's initial oral data and interim monitoring data. The risk levels include high risk, medium risk and low risk. The dynamic adjustment module includes a threshold management unit and a treatment plan optimization unit. The threshold management unit is used to dynamically set the tooth movement distance deviation threshold according to the risk level. The treatment plan optimization unit is used to compare the phased monitoring data with the expected treatment data of the initial treatment plan, calculate the actual tooth movement distance deviation value, and generate an adjusted treatment plan based on the comparison result between the actual tooth movement distance deviation value and the tooth movement distance deviation threshold. The doctor review module is used by doctors to review and adjust the initial treatment plan and the adjusted treatment plan based on the patient's individual parameters, and generate a complete treatment plan based on the medical records and store it in the database module.

[0007] The principle of this invention lies in the following: A standardized historical case library is constructed through a database module, linking and storing the initial oral data of historical patients (such as 3D dental arch features, imaging data, etc.) with complete treatment plans, forming a reusable knowledge base. The data acquisition module collects data in stages: initial oral data provides a benchmark for plan generation, and staged monitoring data captures dynamic changes during the treatment process; both together constitute the objective basis for plan optimization. The plan generation module breaks away from traditional experience-based reliance, selecting the most suitable historical plan from the case library through feature similarity matching, achieving standardized generation of the initial treatment plan. The risk assessment module determines the risk level by quantifying risk factors (such as periodontal status, bone mineral density parameters, etc.), providing a quantitative basis for plan adjustment. The dynamic adjustment module dynamically sets a tooth movement distance deviation threshold based on the risk level, compares the staged monitoring data with the expected treatment data of the initial treatment plan, calculates the actual tooth movement distance deviation value, and generates an adjusted treatment plan when the actual tooth movement distance deviation value exceeds the tooth movement distance deviation threshold. The doctor review module serves as the final decision-making stage, confirming the treatment plan and forming a closed loop of data-driven and human decision-making. It also feeds new cases back into the database, continuously enriching the case library.

[0008] The beneficial effects of this invention are as follows: it generates initial treatment plans through multi-dimensional feature matching, reducing plan deviations caused by subjective experience differences and improving the degree of standardization; it introduces a dynamic adjustment mechanism, enabling the plan to be optimized based on phased monitoring data during the treatment process, adapting to the dynamic changes of teeth and periodontium; it quantifies the deep correlation between risk assessment and plan adjustment, reducing the risk of complications such as root resorption and periodontal damage; and it combines doctor review with database feedback mechanisms, which retains the flexibility of human decision-making while continuously improving system performance through case accumulation, making it particularly suitable for the standardized treatment of complex cases.

[0009] Furthermore, the initial oral data includes a three-dimensional dental arch model, oral imaging data, and basic periodontal parameters. The phased monitoring data includes three-dimensional scan data after tooth movement, bracket position change data, and periodontal dynamic health data. The combination of the three-dimensional dental arch model and oral imaging data can accurately reconstruct the anatomical structure of teeth and skeletal relationships; the combination of basic periodontal parameters and dynamic health data can capture periodontal condition fluctuations in real time, providing continuous variable basis for subsequent risk assessment.

[0010] Furthermore, the treatment record includes tooth movement steps, bracket type, and expected treatment parameters. Detailed records of tooth movement steps can assist the treatment plan generation module in accurately matching the treatment logic of similar cases; the associated storage of bracket type and treatment parameters provides a reference for optimizing treatment plans for different malocclusion types, while also providing traceable quantitative indicators for subsequent efficacy evaluation. Standardizing the treatment record facilitates doctors' quick access to key treatment milestones and provides structured data for historical case matching, improving the accuracy of case reuse.

[0011] Furthermore, in the scheme generation module, the feature similarity matching step includes extracting tooth crowding, overbite, overjet, and bone density parameters from the initial oral data as key features, and calculating similarity through feature vector comparison. In this scheme, similarity calculation focuses on the key parameters that have the greatest impact on the orthodontic plan. Tooth crowding determines the difficulty of alignment and the necessity of tooth extraction; overbite and overjet are related to occlusal relationship adjustment strategies; and bone density parameters affect tooth movement efficiency and anchorage design. Quantitative matching is achieved through feature vector comparison, reducing interference from irrelevant features and improving the accuracy of historical case matching.

[0012] Furthermore, the risk factors in the risk assessment module include periodontal support force, root length, and bone mineral density parameters. The risk level is calculated by weighted summation of these risk factors. Core risk factors closely related to orthodontic safety are selected to form a multi-dimensional assessment system: periodontal support force directly relates to the stability during tooth movement; root length affects the root resorption risk threshold; and bone mineral density determines the alveolar bone's tolerance to orthodontic forces. Weighted summation achieves a quantitative classification of risk levels (high, medium, low), avoiding subjective judgment bias. The weight allocation is based on training with historical risk event data, making the risk assessment results more objective and comparable, and providing a scientific risk-oriented basis for dynamically adjusting the module.

[0013] Furthermore, the threshold values ​​for tooth movement distance deviation set by the threshold management unit include: 0.5 mm for high-risk level; 1.0 mm for medium-risk level; and 1.5 mm for low-risk level.

[0014] By setting tooth movement distance deviation thresholds based on risk level differences, individualized early warning systems are implemented: a stricter threshold (0.5mm) is used for high-risk patients (e.g., those with insufficient periodontal support), allowing for early detection and timely adjustments to reduce the risk of periodontal damage; while the threshold is appropriately relaxed (1.5mm) for low-risk patients, reducing unnecessary changes in treatment plans and balancing treatment efficiency and safety. This dynamic correlation between the tooth movement distance deviation threshold and risk level solves the problem of insufficient adaptability to individual differences with traditional fixed thresholds.

[0015] Furthermore, the treatment adjustment plan generated by the optimization unit includes correcting the tooth movement angle and adjusting the force application cycle. The tooth movement angle correction is a 5° increase for every 0.5mm increase in the actual tooth movement distance deviation. The force application cycle is shortened by 20% for high-risk patients, by 5% for medium-risk patients, and remains unchanged for low-risk patients. A quantitative correlation is established between the deviation value and the adjustment strategy to ensure the accuracy and operability of the adjustment: the tooth movement angle correction uses a linear correlation rule, creating a traceable correspondence between the angle adjustment and the actual tooth movement distance deviation; the force application cycle is adjusted differently according to the risk level: high-risk patients have their cycle shortened (20%) and monitoring frequency increased, medium-risk patients have it fine-tuned (5%) to balance efficacy and safety, and low-risk patients maintain their cycle to ensure treatment efficiency. This mechanism avoids treatment fluctuations caused by over-adjustment and achieves personalized adaptation of the adjustment strategy through risk grading.

[0016] Furthermore, the doctor review module provides a 3D visualization interface to simultaneously display 3D models of the initial treatment plan, adjusted treatment plan, and the patient's current dental condition. It also links and displays complete treatment plans from historical patients with the highest similarity in the database for reference. The 3D visualization interface enhances the intuitiveness of the treatment plan, allowing doctors to quickly understand differences and adjustment logic. The linked display of historical cases provides doctors with efficacy references for similar cases, supporting more scientific human decision-making. Attached Figure Description

[0017] Figure 1 This is a system module diagram of an embodiment of the orthodontic treatment planning system of the present invention. Detailed Implementation

[0018] The following detailed description illustrates the specific implementation method: The basic implementation examples are as follows: Figure 1 As shown, the orthodontic treatment planning system includes a database module, a data acquisition module, a plan generation module, a risk assessment module, a dynamic adjustment module, and a doctor review module.

[0019] The database module is used to store the initial oral data of historical patients and the corresponding complete treatment plan, which includes the initial treatment plan, the adjusted treatment plan, and the medical records.

[0020] The data acquisition module includes an initial data acquisition unit and a phased monitoring unit. The initial data acquisition unit is used to acquire the patient's initial oral data before treatment, and the phased monitoring unit is used to acquire the patient's phased monitoring data during treatment.

[0021] The initial oral data is acquired through the initial data acquisition unit, including: a three-dimensional dental arch model is obtained by laser scanning technology to acquire surface point cloud data of the dental arch, which is then denoised and registered to generate a triangular mesh model, preserving tooth morphological details (such as crown tilt angle and proximal contact relationship); oral imaging data is acquired using CBCT equipment to acquire the three-dimensional structure of alveolar bone, and periapical radiographs are acquired simultaneously for root length measurement; curved tomographic radiographs are acquired for overall bone density parameter assessment; and basic periodontal parameters are acquired by electronic periodontal probes to collect periodontal pocket depths at 6 sites, combined with the gingival bleeding index to record the initial periodontal state.

[0022] Data is collected every 6-8 weeks during treatment using a phased monitoring unit. This includes 3D scan data (to compare tooth movement distance), bracket position changes (using image recognition technology based on the OpenCV library to detect bracket offset), and periodontal dynamic health data (to check periodontal pocket depth, probing bleeding, and assess inflammation changes).

[0023] The treatment plan generation module is used to perform feature similarity matching between the initial oral data of the current patient and the initial oral data of historical patients in the database module, select the complete treatment plan corresponding to the historical patient with the highest similarity from the database module, and generate the initial treatment plan for the current patient based on the selected complete treatment plan.

[0024] The feature extraction part includes: extracting four key features from the current patient's initial oral data: tooth crowding: calculating the difference between the actual length and the expected length of the dental arch (in mm); overbite: measuring the vertical distance between the incisal edge of the upper incisor and the labial surface of the lower incisor (in mm); overjet: measuring the horizontal distance between the incisal edge of the upper incisor and the labial surface of the lower incisor (in mm); bone mineral density parameters: selecting specific areas of alveolar bone (such as the buccal bone plate of the first molar) from the oral images acquired by CBCT, and using grayscale analysis to quantify the bone mineral density parameters into values ​​from 0 to 100, with higher values ​​indicating greater bone mineral density parameters.

[0025] The feature similarity matching part includes: using a feature vector comparison algorithm, standardizing the above four types of key features into vectors (e.g., [5,2,3,70]), and calculating the cosine similarity with the feature vectors of historical cases in the database. The calculation formula is as follows:

[0026] in For the current patient feature vector, This is a feature vector of historical patients. The complete treatment plan corresponding to the historical patient with the highest similarity is selected, and an initial treatment plan for the current patient is generated based on this. For example, if the tooth movement angle in the historical plan is 10°, the initial treatment plan can be set to the same angle, and then adjusted according to the actual situation.

[0027] The risk assessment module is used to extract risk factors and calculate risk levels based on the patient's initial oral data and interim monitoring data. The risk levels include high risk, medium risk, and low risk.

[0028] The following risk factors were extracted from the current patients' initial oral data and interim monitoring data: Periodontal support: Calculated based on periodontal pocket depth and attachment loss, specifically the difference between normal attachment level and attachment loss, and the ratio to normal attachment level, converted into percentage data. For example, if the normal attachment level is 2mm and the attachment loss is 0.5mm, then the periodontal support is 75%.

[0029] Root length: The actual length of the tooth root is measured from the oral images acquired by CBCT, in mm.

[0030] Bone mineral density parameter: The method for obtaining bone mineral density is the same as that in the scheme generation module. A specific area of ​​alveolar bone (such as the buccal bone plate of the first molar) is selected from the oral images acquired by CBCT. The bone mineral density parameter is quantified into a value of 0-100 using the gray value analysis method. The higher the value, the greater the bone mineral density parameter.

[0031] The risk level is calculated using a weighted summation method to determine the risk value. The formula is:

[0032] in, For periodontal support, The length of the tooth root. For bone mineral density parameters, the weights of each item are derived from training based on historical risk event data:

[0033] Based on risk value Classification by risk level: High-risk level corresponds to Medium-risk level corresponds to Low-risk level corresponds to For example, the patient's periodontal support 75%, root length 15.0mm, bone mineral density parameters Its risk value is 70. for

[0034] Based on the corresponding range of the risk levels, it belongs to the low-risk level.

[0035] The dynamic adjustment module includes a threshold management unit and a treatment plan optimization unit. The threshold management unit is used to dynamically set a tooth movement distance deviation threshold according to the risk level. The treatment plan optimization unit is used to compare the phased monitoring data with the expected treatment data of the initial treatment plan, calculate the actual tooth movement distance deviation value, and generate an adjusted treatment plan based on the comparison result between the actual tooth movement distance deviation value and the tooth movement distance deviation threshold.

[0036] The threshold management unit dynamically sets the tooth movement distance deviation thresholds based on the risk level obtained from the risk assessment module. These thresholds include: 0.5mm for high-risk level; 1.0mm for medium-risk level; and 1.5mm for low-risk level.

[0037] The treatment plan optimization unit compares the tooth movement distance in the phased monitoring data with the expected treatment data of the initial treatment plan. The difference is the actual tooth movement distance deviation value. For example, if the expected movement is 2.0 mm and the actual movement is 2.3 mm, the actual deviation value is 0.3 mm. The treatment plan generated by the optimization unit includes correcting the tooth movement angle and adjusting the force application cycle. The tooth movement angle correction is 5° for every 0.5 mm increase in the actual tooth movement distance deviation value. The force application cycle is shortened by 20% for high-risk levels, shortened by 5% for medium-risk levels, and remains unchanged for low-risk levels. For example, in this embodiment, if the actual tooth movement distance deviation value is 1.0 mm, the angle correction is 10°, that is, the angle is adjusted from the initial angle of 10° to 20°. The force application cycle remains unchanged for low-risk levels (e.g., 4 weeks).

[0038] The doctor review module is used by doctors to review and adjust the initial and adjusted treatment plans based on the patient's individual parameters (age, oral hygiene habits, etc.), and generate a complete treatment plan by combining the treatment records, which is then stored in the database module. The treatment records include tooth movement steps (sorted by timestamp), bracket replacement type, and expected treatment duration parameters (such as total treatment months, follow-up visit intervals, etc.). The doctor review module provides a 3D visualization interface to simultaneously display a 3D model of the initial treatment plan, the adjusted treatment plan, and the patient's current tooth condition, and also links to and displays the complete treatment plans of the most similar historical patients in the database for reference.

[0039] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A dental orthodontic treatment planning system, characterized in that, include: The database module is used to store the initial oral data of historical patients and the corresponding complete treatment plan, which includes the initial treatment plan, the adjusted treatment plan, and the medical records. The data acquisition module includes an initial data acquisition unit and a phased monitoring unit. The initial data acquisition unit is used to acquire the patient's initial oral data before treatment, and the phased monitoring unit is used to acquire the patient's phased monitoring data during treatment. The treatment plan generation module is used to perform feature similarity matching between the initial oral data of the current patient and the initial oral data of historical patients in the database module, filter out the complete treatment plan corresponding to the historical patient with the highest similarity from the database module, and generate the initial treatment plan for the current patient based on the selected complete treatment plan. The risk assessment module is used to extract risk factors and calculate risk levels based on the patient's initial oral data and interim monitoring data. The risk levels include high risk, medium risk and low risk. The dynamic adjustment module includes a threshold management unit and a treatment plan optimization unit. The threshold management unit is used to dynamically set the tooth movement distance deviation threshold according to the risk level. The treatment plan optimization unit is used to compare the phased monitoring data with the expected treatment data of the initial treatment plan, calculate the actual tooth movement distance deviation value, and generate an adjusted treatment plan based on the comparison result between the actual tooth movement distance deviation value and the tooth movement distance deviation threshold. The doctor review module is used by doctors to review and adjust the initial treatment plan and the adjusted treatment plan based on the patient's individual parameters, and generate a complete treatment plan based on the medical records and store it in the database module.

2. The orthodontic treatment planning system according to claim 1, characterized in that, The initial oral data includes a three-dimensional dental arch model, oral imaging data, and basic periodontal parameters. The phased monitoring data includes three-dimensional scan data after tooth movement, bracket position change data, and periodontal dynamic health data.

3. The orthodontic treatment planning system according to claim 1, characterized in that, The treatment record includes the tooth movement steps, bracket type, and expected treatment parameters.

4. The orthodontic treatment planning system according to claim 1, characterized in that, In the scheme generation module, the feature similarity matching step includes extracting tooth crowding, overbite, coverage and bone density parameters from the initial oral data as key features, and calculating similarity by comparing feature vectors.

5. The orthodontic treatment planning system according to claim 1, characterized in that, The risk factors in the risk assessment module include periodontal support force, root length, and bone mineral density parameters. The risk level is calculated by weighted summation of the risk factors.

6. The orthodontic treatment planning system according to claim 1, characterized in that, The thresholds for tooth movement distance deviation set by the threshold management unit include: 0.5 mm for high-risk level; 1.0 mm for medium-risk level; and 1.5 mm for low-risk level.

7. The orthodontic treatment planning system according to claim 1, characterized in that, The treatment plan generated by the optimization unit includes correcting the tooth movement angle and adjusting the force application cycle. The tooth movement angle correction is 5° for every 0.5mm increase in the actual tooth movement distance deviation. The force application cycle is shortened by 20% for high-risk levels, shortened by 5% for medium-risk levels, and remains unchanged for low-risk levels.

8. The orthodontic treatment planning system according to claim 1, characterized in that, The doctor review module provides a three-dimensional visualization interactive interface, which is used to simultaneously display the three-dimensional model of the initial treatment plan, the adjusted treatment plan, and the patient's current dental condition, and to associate and display the complete treatment plan of the most similar historical patient in the database as a reference.

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