Intelligent management and control system for standardized diagnosis and treatment process of primary orthodontic clinic

Abstract

The invention discloses an intelligent management and control system for a standardized diagnosis and treatment process of a basic-level orthodontic clinic, and relates to the technical field of intelligent management and control of basic-level orthodontic diagnosis and treatment. The standardized scheme generation module generates and archives a scheme based on the database; the dynamic monitoring prediction module calculates risks, deviations and the like and generates pre-intervention suggestions; the precise adhesion removing module completes laser and ultrasonic cleaning operation and records the laser and ultrasonic cleaning operation; the data iterative optimization module collects a data optimization database and coefficients, and pushes results to a related module; universe data are integrated, an adaptive diagnosis and treatment scheme is generated, a management and control process is standardized, and an avoidance problem is dynamically monitored and predicted; bonding is accurately removed, and the ending effect is guaranteed; the data iterative optimization forms a virtuous cycle, continuously adapts to clinical scenes, reduces the basic level operation threshold, reduces human differences, provides support for basic level orthodontic diagnosis and treatment standardized development, and improves the medical service quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent management and control technology for orthodontic treatment at the grassroots level, specifically to an intelligent management and control system for standardized treatment processes in grassroots orthodontic clinics. Background Technology

[0002] With the general increase in oral health awareness, the demand for orthodontics is showing a continuous growth trend. Primary-level orthodontic clinics, due to their convenience and accessibility, have become the core carriers for serving a large number of patients. The quality of their treatment directly affects the orthodontic outcome and oral health safety of patients. Currently, the field of orthodontic treatment is accelerating its transformation towards standardization, precision, and intelligence. The increasing maturity of technologies such as 3D morphological acquisition, sensor detection, cloud storage, and data analysis provides solid technical support for the systematic optimization of the treatment process. Primary-level orthodontic treatment involves multiple key links, including clinic-side examination and diagnosis, patient-side appliance fitting, and treatment completion procedures. Effective integration, standardized processing, and full-process control of data from each link are core requirements for improving treatment consistency and reducing potential risks. At the same time, significant differences exist in the individual oral characteristics of patients, placing higher demands on the adaptability and precision of treatment plans. Traditional experience-based treatment models are no longer able to match industry development trends and patients' core needs. There is an urgent need to build a standardized full-process control system through intelligent means to promote the overall improvement of the quality of primary-level orthodontic treatment.

[0003] Traditional primary care orthodontic treatment models face numerous unresolved issues. Firstly, the lack of a unified, standardized treatment system means that treatment plans heavily rely on individual doctors' clinical experience. Significant differences in treatment approaches and operational procedures among doctors lead to inconsistent quality of care even within the same region or clinic, making it difficult to guarantee consistent treatment outcomes for patients. Secondly, data collection is fragmented and disjointed. Data from clinics, patients, and post-treatment procedures is not fully integrated. Inconsistent data formats and a lack of standardized backup mechanisms not only affect data security and traceability but also fail to provide comprehensive data for optimizing treatment plans. Furthermore, the lack of an effective dynamic monitoring and prediction mechanism makes it impossible to grasp the patient's orthodontic compliance and actual tooth movement in real time, making it difficult to identify potential risks in advance. Intervention is often only carried out after the deviation has accumulated to a certain extent, resulting in a longer treatment cycle and unsatisfactory results. In addition, the debonding operation at the end of orthodontic treatment relies on manual judgment and operation, which lacks the precision to control the tooth surface condition and is prone to problems such as adhesive residue or enamel damage. Moreover, the traditional model lacks the ability to continuously iterate and optimize, and the parameters and operation standards of the plan cannot be dynamically adjusted according to clinical practice data, making it difficult to adapt to diverse clinical scenarios and individual needs. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a standardized intelligent management and control system for orthodontic treatment processes in primary care clinics. This system integrates data from the clinic, patient, and post-treatment procedures, ensuring data security and traceability through unified processing and dual backup. Based on a standardized database specifically for primary care orthodontics, it generates tailored treatment plans by combining individual patient oral characteristics. Multi-dimensional algorithms quantify compliance risks, tooth movement deviations, and their correlation, providing targeted pre-intervention suggestions for both doctors and patients. A precise debonding module enables dynamic analysis of tooth surface conditions and refined operational processing. An iterative data optimization module continuously improves the database and algorithm coefficients. The system constructs an intelligent management and control system covering the entire process of data collection, generation, monitoring, and optimization, lowering the operational threshold for primary care clinics, reducing the impact of human error, improving treatment consistency and accuracy, ensuring treatment effectiveness and tooth health, and providing technical support for the standardized development of orthodontic treatment at the primary care level.

[0005] To solve the above-mentioned technical problems, this invention provides the following technical solution: a standardized treatment process intelligent management and control system for primary-level orthodontic clinics, the system comprising: Full-domain data acquisition module: used to collect raw orthodontic data from clinic, patient, and treatment completion operations, and after format standardization, perform dual backups in the cloud and locally; Standardized treatment plan generation module: Receives raw three-dimensional morphological data of the patient's oral cavity, relies on the cloud-based standardized database for primary orthodontic treatment, and generates a standardized orthodontic treatment plan based on the patient's individual oral characteristics. It outputs standard tooth movement trajectory, wearing specifications, and basic parameters for laser debonding, and archives them in a structured manner. Dynamic monitoring and prediction module: Receives raw orthodontic data and standardized orthodontic treatment plans, runs a dynamic compliance risk index algorithm to calculate the patient's treatment compliance risk index, runs a risk-coupled tooth movement deviation algorithm to calculate the actual tooth movement deviation, runs a clinically calibrated risk-deviation correlation algorithm to calculate the correlation coefficient between risk and deviation, makes a judgment to generate pre-intervention suggestions, and records the entire process data of the dynamic monitoring and prediction module. Precision debonding module: Receives raw tooth surface imaging data and basic parameters for laser debonding, completes the initial settings of the laser equipment and ultrasonic cleaning unit, analyzes the tooth surface condition and dynamically adjusts the laser parameters to form a closed-loop control, stops the laser operation and triggers ultrasonic cleaning after the target is met, and records the operation data. Data Iteration and Optimization Module: Collects the full-process data from the first four modules, cleans and organizes it, and performs multi-dimensional clinical statistical analysis. It optimizes the cloud-based standardized database for primary care orthodontic treatment and the basic coefficients of the algorithm, and pushes the optimization results to the standardized plan generation module and the dynamic monitoring and prediction module.

[0006] Furthermore, the acquisition method for raw orthodontic data from the clinic, patient, and orthodontic finishing operation ends in the full-domain data acquisition module is as follows: At the clinic, three-dimensional morphological data of the patient's dentition, alveolar bone, and jawbone, as well as the occlusal relationship of the upper and lower jaws and basic dental and periodontal conditions are acquired through an intraoral scanning device; At the patient, the contact pressure between the orthodontic appliance and the tooth surface, oral motion acceleration, acoustic signals generated by chewing and molars, and intraoral temperature data are continuously acquired through sensors worn in the oral cavity, while simultaneously acquiring three-dimensional displacement and angular change data of single teeth and the entire row of teeth; At the orthodontic finishing operation end, image data of the enamel surface and adhesive layer in the laser-affected area are acquired through an imaging device during the debonding operation. After all data acquisition is completed, it is processed in a standardized format and backed up both in the cloud and locally.

[0007] Furthermore, the standardized treatment plan generation module, relying on the cloud-based standardized database for basic orthodontic treatment, generates a standardized orthodontic treatment plan based on the patient's individual oral characteristics. The specific content includes: extracting individual oral characteristics from the received raw three-dimensional morphological data of the patient's mouth, including malocclusion type, alveolar bone thickness and metabolic status, dental and periodontal health conditions, maxillary and mandibular occlusion relationship, and tooth density; matching the corresponding basic treatment plan template based on the cloud-based standardized database for basic orthodontic treatment; adjusting the template parameters based on the extracted individual oral characteristics to generate a standardized orthodontic treatment plan. Specifically, the standard trajectory for tooth movement is set according to the malocclusion type and alveolar bone metabolic capacity, defining the displacement path and angle of each tooth at each stage; the wearing specifications parameters are set according to the difficulty of tooth movement and the fit of the aligner, defining the daily wearing time, allowable interruption time, and replacement cycle; and the basic parameters for laser debonding are set according to the enamel thickness and adhesive adhesion, defining the initial power, pulse frequency, and spot path. After the plan and all parameters are determined, a structured archive is created.

[0008] Furthermore, the standardized treatment plan generation module contains a standardized database for primary-level orthodontic treatment, which includes various types of data specifically for primary-level orthodontic treatment. These include basic orthodontic treatment plan templates categorized by malocclusion type, reference data on alveolar bone metabolism rate and tooth movement rate stratified by age, anatomical feature data for different tooth positions, benchmark data on tooth movement stages, benchmark data on wearing duration and replacement cycle of various orthodontic appliances, initial parameters for laser debonding corresponding to different enamel conditions and adhesive types, and standardized operational procedures for primary-level orthodontic treatment. All of these data are constructed based on primary-level clinical practice cases, providing a basis for matching basic plan templates and adjusting parameters to generate standardized orthodontic treatment plans.

[0009] Furthermore, the mathematical expression for the dynamic compliance risk index algorithm run by the dynamic monitoring and prediction module is: ;in, This is a dynamic compliance risk index. These correspond to four categories of behavioral data: wearing time, wearing force, abnormal occlusion / teeth grinding, and appliance status. Let be the dynamic weight coefficients of the i-th type of behavioral data at time t, satisfying , This refers to the start time of treatment. For the standardized quantification value of the corresponding behavioral data, The time decay coefficient, For the current treatment period, This is a correction factor for the risk of orthodontic appliance breakage. The calculation process involves four types of behavioral data: wearing time, wearing force, abnormal occlusion or bruxism, and orthodontic appliance status. Each type of data is standardized to obtain a uniformly quantified value. A weighting coefficient is assigned to each type of data, dynamically adjusted over time. The weights for different treatment stages are then corrected using a time decay factor. The results of the four types of data after weight and time correction are summed to obtain a baseline risk value. Finally, the correction coefficient for orthodontic appliance breakage risk is combined with the quantified value of the breakage feature signal to obtain a dynamic risk index ranging from 0 to 100. This index provides input data for the risk-coupled tooth movement deviation algorithm, provides a quantitative basis for the clinically calibrated risk-deviation association algorithm, and serves as a quantitative indicator for determining whether to generate pre-intervention recommendations. The calculated index and all process data are synchronized to the data iteration and optimization module.

[0010] Furthermore, the mathematical expression for the risk-coupled tooth movement deviation algorithm run by the dynamic monitoring and prediction module is: ;in, Let be the actual movement deviation of the j-th tooth at time t. For the current treatment period, Corresponding to all teeth, These are the three-dimensional coordinates of the actual tooth movement. The standard three-dimensional coordinate values ​​for tooth movement. This is the risk index output by the dynamic compliance risk index algorithm. The risk index is set to its maximum value of 100. The compliance risk coupling coefficient, The coefficients are anatomical feature coefficients for teeth. The calculation process is as follows: First, the actual movement position of a single tooth and the preset standard movement position are obtained, and the spatial distance between the two is calculated to obtain the basic deviation. Then, the previously obtained dynamic compliance risk index is introduced, and combined with the anatomical feature coefficients of the tooth itself and the risk coupling coefficient, the risk coupling correction factor is calculated. The basic deviation is multiplied by the risk coupling correction factor to obtain the actual tooth movement deviation, which is used to provide deviation quantification data for running the clinically calibrated risk-deviation association algorithm, and to provide the basis for determining the deviation dimension for deciding whether to generate pre-intervention recommendations. The calculated deviation and the entire process data are synchronized to the data iteration and optimization module.

[0011] Furthermore, the mathematical expression for the clinically calibrated risk-bias association algorithm run by the dynamic monitoring and prediction module is: ;in, Let be the risk-bias correlation coefficient of the j-th tooth at time t. For the current treatment time, Cov The covariance between the risk index and the amount of tooth movement deviation. This is a dynamic compliance risk index. Let be the actual movement deviation of the j-th tooth at time t. For dynamic compliance risk index variance The actual movement deviation of the j-th tooth variance The clinical calibration factor is used for the following calculation process: First, the degree of synergistic change between the dynamic compliance risk index and the actual tooth movement deviation is calculated. Then, the fluctuation degree of each of these two indicators is calculated separately. The degree of synergistic change is divided by the normalized value of the combination of the two fluctuation degrees to obtain the initial correlation coefficient. The initial correlation coefficient is then adjusted using the clinical calibration factor to obtain the correlation coefficient in the range of -1 to 1. This correlation coefficient is used to determine the degree of correlation between the patient's orthodontic compliance risk index and the actual tooth movement deviation, providing a basis for determining the correlation dimension for generating pre-intervention recommendations. The calculated correlation coefficient and the entire process data are synchronized to the data iteration and optimization module.

[0012] Furthermore, the specific content of the pre-intervention suggestions generated in the dynamic monitoring and prediction module is as follows: the judgment condition is a dynamic compliance risk index ≥80, or an absolute value of the correlation coefficient output by the clinically calibrated risk-bias correlation algorithm ≥0.7; the patient-side pre-intervention suggestions include prompts for adjusting wearing time, prompts for abnormal occlusion and teeth grinding behavior, and prompts for checking the status of the orthodontic appliance; the doctor-side pre-intervention suggestions include marking of tooth movement deviation sites, guidance on adjusting the force points of the orthodontic appliance, and guidance on adjusting the treatment cycle.

[0013] Furthermore, the precise debonding module analyzes the tooth surface state using a high-frequency scanning method with a scanning frequency of 100Hz and a scanning accuracy of 0.5μm. The scanning range covers the laser-acting area and the surrounding 1mm range. Real-time imaging data is stored in the form of frame sequences, and each frame image contains quantitative annotations of adhesive thickness and enamel surface state. The dynamic adjustment step of laser parameters is such that the power adjustment increment does not exceed 0.1W, the pulse frequency adjustment increment does not exceed 2Hz, and the spot movement path adjustment accuracy is not less than 0.1mm. The adjustment interval is synchronized with the scanning frequency.

[0014] Furthermore, the ultrasonic cleaning power of the precision adhesive removal module is 20-40kHz, and the cleaning time is dynamically set according to the adhesive residue, with a minimum of 30 seconds and a maximum of 2 minutes. The ultrasonic vibration amplitude is monitored in real time during the cleaning process, and a tooth surface condition inspection report is automatically generated after the cleaning is completed. The enamel surface integrity is judged by the absence of microcracks with a depth exceeding 5μm, a surface roughness Ra≤0.2μm, and the absence of adhesive residue visible to the naked eye or at the microscopic level.

[0015] Beneficial effects Compared with existing technologies, this intelligent management and control system for standardized treatment processes in primary orthodontic clinics has the following beneficial effects: I. This invention integrates data from clinics, patients, and the final stages of orthodontic treatment. Through unified processing and dual backup, data security and traceability are ensured. Relying on a standardized database covering various types of primary care orthodontic data, it generates personalized treatment plans based on individual patient oral characteristics. This achieves standardized management of the treatment process. A dynamic monitoring and prediction mechanism quantifies compliance risks and tooth movement deviations through multi-dimensional algorithms, establishing a correlation between risk and deviation. It provides targeted pre-intervention suggestions for patients and doctors in advance, effectively avoiding potential problems during treatment and reducing the impact of accumulated deviations on the final outcome. Structured archiving makes plan execution and subsequent traceability more efficient, helping primary care clinics break free from reliance on experience, improving the consistency and reliability of treatment services. Patients receive orthodontic services more tailored to their individual circumstances through standardized management throughout the entire process, significantly reducing treatment risks caused by non-standardized procedures.

[0016] Second, this invention achieves refined processing of orthodontic finishing operations through the dynamic analysis of tooth surface condition and closed-loop adjustment of laser parameters by a precise debonding module, combined with the synergistic operation of ultrasonic cleaning. This ensures that both tooth surface health and debonding effectiveness are achieved. The data iteration and optimization module collects data from the entire process, cleans and organizes it, and conducts multi-dimensional clinical statistical analysis to continuously optimize the database and algorithm's basic coefficients. This promotes the dynamic improvement of treatment plans, monitoring models, and operational parameters, forming a virtuous cycle of data collection, plan generation, dynamic monitoring, and optimization iteration. This cyclical mechanism allows the system to continuously adapt to more clinical scenarios, constantly improving its adaptability and accuracy, lowering the operational threshold for primary care clinics, reducing the impact of human error, and improving treatment efficiency and effectiveness. At the same time, it provides solid data support and technical guarantee for the standardized development of primary care orthodontic treatment, promoting the overall improvement of the quality of primary care orthodontic medical services.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart for an intelligent management and control system for standardized treatment processes in primary orthodontic clinics; Figure 2 This diagram illustrates the connection relationships between the modules of an intelligent management and control system for standardized treatment processes in primary orthodontic clinics. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1: Orthodontic treatment scenario for mild to moderate crowding of teeth in adolescents.

[0022] At the clinic, intraoral scanning equipment comprehensively collects three-dimensional morphological data of the patient's dentition, alveolar bone, and jawbone, as well as data on the occlusal relationship of the upper and lower jaws and the basic condition of the teeth and periodontium. This data provides comprehensive and accurate anatomical and health support for the subsequent treatment plan, ensuring that the plan is tailored to the characteristics of adolescent dental development. At the patient's end, intraoral sensing elements are worn to continuously collect data on the contact pressure between the orthodontic appliance and the tooth surface, oral motion acceleration, acoustic signals generated by chewing and molars, and intraoral temperature. Simultaneously, three-dimensional displacement and angular change data of individual teeth and the entire row of teeth are collected, capturing data in real time. The dynamic movement of teeth and the patient's wearing behavior during orthodontic treatment provide continuous and detailed data for subsequent dynamic monitoring. An imaging device interface is reserved at the end of the treatment procedure. During subsequent bond removal, this device will collect image data of the enamel surface and adhesive layer in the laser-treated area, providing a direct reference for precise operation and effect evaluation in the bond removal process. After all data is collected, it undergoes standardized formatting and is backed up both in the cloud and locally to prevent data loss or corruption, ensuring that the entire treatment process is traceable and accessible, providing complete data support for subsequent review and optimization. Figure 1 As shown.

[0023] From the patient's raw three-dimensional oral morphology data, individual oral characteristics were extracted, including mild to moderate dental crowding, alveolar bone thickness and metabolic status (normal metabolism in adolescents), absence of periodontal disease, maxillary and mandibular occlusion, and tooth density. This clarified the core points and individual differences in adolescent orthodontic treatment, providing a clear basis for adjusting the treatment plan. Utilizing a cloud-based standardized database for primary care orthodontic treatment, a basic treatment plan template corresponding to mild to moderate dental crowding was matched, ensuring the plan conforms to primary care standards and clinical experience, reducing the difficulty of treatment plan design for primary care physicians. Based on the extracted individual characteristics, template parameters were adjusted to generate a standardized orthodontic treatment plan. The standard tooth movement trajectory, based on the type of mild to moderate dental crowding and the adolescent's alveolar bone metabolic capacity, sets the displacement path and angle of each tooth at each stage. For example, the anterior tooth retraction path is planned at 0.5mm per month to ensure that tooth movement is scientific and orderly and conforms to the growth and development patterns of adolescents. Wearing specifications are based on the difficulty of tooth movement and the fit of the orthodontic appliance, specifying a daily wearing time of 22 hours, a permissible interruption time of 1 hour, and a replacement cycle of 4 weeks. This helps patients wear the appliance correctly to ensure treatment effectiveness and adapts to the daily study and life rhythm of adolescents. Laser bonding removal parameters are based on the patient's enamel thickness and adhesive adhesion, setting an initial power of 1.2W, a pulse frequency of 20Hz, and a circular scanning spot path to provide a safety benchmark for subsequent bonding removal operations and to suit the physiological characteristics of thicker enamel in adolescents. After the plan and all parameters are determined, they are structured and archived for easy retrieval and comparison in subsequent treatment stages, improving the continuity of the treatment process.

[0024] It receives raw orthodontic data from the full-domain data acquisition module and standardized orthodontic treatment plans output by the standardized plan generation module, providing complete and accurate input data for multi-dimensional algorithm calculations; The dynamic adherence risk index algorithm is used to calculate the patient's treatment adherence risk index. The mathematical expression for the dynamic adherence risk index algorithm is as follows: ;in, This is a dynamic compliance risk index. These correspond to four categories of behavioral data: wearing time, wearing force, abnormal occlusion / teeth grinding, and appliance status. Let be the dynamic weight coefficients of the i-th type of behavioral data at time t, satisfying , This refers to the start time of treatment. For the standardized quantification value of the corresponding behavioral data, The time decay coefficient, For the current treatment period, This is a correction factor for the risk of orthodontic appliance breakage. This represents the quantized value of the orthodontic appliance damage characteristic signal. The risk-coupled tooth movement deviation algorithm is used to calculate the actual tooth movement deviation. The mathematical expression of the risk-coupled tooth movement deviation algorithm is: ;in, Let be the actual movement deviation of the j-th tooth at time t. For the current treatment period, Corresponding to all teeth, These are the three-dimensional coordinates of the actual tooth movement. The standard three-dimensional coordinate values ​​for tooth movement. This is the risk index output by the dynamic compliance risk index algorithm. The risk index is set to its maximum value of 100. The compliance risk coupling coefficient, These are the coefficients representing the anatomical features of the teeth. The clinically calibrated risk-bias association algorithm is used to calculate the correlation coefficient between risk and bias. The mathematical expression for the clinically calibrated risk-bias association algorithm is: ;in, Let be the risk-bias correlation coefficient of the j-th tooth at time t. For the current treatment time, Cov The covariance between the risk index and the amount of tooth movement deviation. This is a dynamic compliance risk index. Let be the actual movement deviation of the j-th tooth at time t. For dynamic compliance risk index variance The actual movement deviation of the j-th tooth variance For clinical calibration factors; By using multi-dimensional quantitative analysis, potential problems in the orthodontic process can be accurately controlled, avoiding poor treatment results due to fluctuations in adolescent compliance or deviations in tooth movement. The patient's dynamic compliance risk index was determined to be 65, with an absolute correlation coefficient of 0.5, both below the warning threshold. Pre-intervention suggestions were generated: patients received reminders of the required wearing time, indications of no abnormal occlusion or bruxism, and confirmation of normal appliance status, helping adolescents maintain good orthodontic progress and reinforcing their awareness of proper appliance wearing. Doctors received guidance indicating no tooth movement deviation points, no need to adjust appliance force points or treatment cycles, allowing doctors to clearly understand treatment progress without additional intervention, saving treatment time. Simultaneously, the entire process of calculation data and judgment results of this module were fully recorded and synchronized to the data iteration and optimization module, accumulating real clinical data on mild to moderate crowding cases in adolescents for system optimization.

[0025] The system receives raw tooth surface imaging data and basic parameters for laser adhesive removal, completing the initial setup of the laser equipment and ultrasonic cleaning unit. This ensures the equipment is in an initial working state suitable for adolescent patients, avoiding operational risks caused by improper initial parameters. A high-frequency scanning method is used to analyze the tooth surface condition at a scanning frequency of 100Hz, with a scanning accuracy of 0.5μm. The scanning range covers the laser-affected area and a 1mm perimeter. Real-time imaging data is stored in frame sequence format, with each frame containing quantitative annotations of adhesive thickness and enamel surface condition, making the tooth surface condition visible and facilitating precise adjustments. It allows for real-time monitoring of adhesive residue and enamel condition. Based on the scanning results, laser parameters are dynamically adjusted: power from an initial 1.2W to 1.0W in 0.1W increments, and pulse frequency from 20Hz to 18Hz in 0.1W increments. The laser beam movement path is adjusted with a frequency of 2Hz and a precision of 0.1mm. The adjustment interval is synchronized with the scanning frequency to ensure precise laser action without damaging the enamel of adolescents, achieving gentle and efficient bond removal. Once the tooth surface condition meets the requirements, the laser operation is stopped and ultrasonic cleaning is triggered. The ultrasonic cleaning power is set to 30kHz, and the cleaning time is dynamically set to 45 seconds based on the amount of adhesive residue. This efficiently removes residue while avoiding over-cleaning, protecting the dental health of adolescents. The ultrasonic vibration amplitude is monitored in real time during the cleaning process to prevent excessive vibration from damaging the teeth. After cleaning, a tooth surface condition inspection report is automatically generated, confirming that there are no microcracks deeper than 5μm on the enamel surface, the surface roughness Ra is 0.15μm, and there is no visible or microscopic adhesive residue, ensuring that the bond removal operation is both safe and thorough, and protecting the subsequent healthy development of adolescents' teeth.

[0026] This process aggregates data from four modules: comprehensive data collection, standardized treatment plan generation, dynamic monitoring and prediction, and precise debonding. This includes raw 3D morphological data, sensor data, generated treatment plan parameters, algorithm calculation results, pre-intervention suggestions, operational parameters, and test reports, constructing a complete data chain for adolescent cases of mild to moderate dental crowding. After cleaning and organizing the data to remove redundant information, multi-dimensional clinical statistical analysis is conducted to uncover treatment patterns and optimization directions for these cases, such as the impact of different wearing durations on treatment outcomes and the correlation between laser parameters and debonding effects. Based on the analysis results, the cloud-based standardized database for primary care orthodontic treatment is optimized, supplementing it with effective data for these adolescent cases of mild to moderate dental crowding, enriching the parameter dimensions of corresponding basic treatment plan templates, and adjusting relevant algorithm coefficients to better suit the treatment characteristics and physiological features of adolescents. The optimized database and algorithm coefficients are then pushed to the standardized treatment plan generation module and the dynamic monitoring and prediction module to improve the accuracy of treatment plans and the reliability of algorithm calculations for subsequent similar adolescent cases, continuously optimizing the effectiveness of primary care orthodontic treatment.

[0027] In summary, for orthodontic treatment of mild to moderate crowding in adolescents, the system acquires comprehensive data from both the clinic and patient sides through a full-domain data acquisition module, with double backups ensuring data integrity. The standardized treatment plan generation module relies on a cloud database to customize personalized plans based on the alveolar bone metabolism characteristics of adolescents, clearly defining tooth movement trajectories, wearing guidelines, and laser bonding removal parameters. The dynamic monitoring and prediction module runs three core algorithms to accurately assess the treatment status and provide pre-intervention suggestions tailored to adolescents. The precise bonding removal module achieves safe and efficient operation through high-frequency scanning and closed-loop control, protecting the enamel of adolescents. The data iteration and optimization module collects data from the entire process, continuously improving the database and algorithms to ensure more accurate and standardized diagnosis and treatment of similar adolescent cases, fully adapting to the physiological characteristics and orthodontic needs of adolescents.

[0028] Example 2: Orthodontic treatment for moderate malocclusion in adults.

[0029] At the clinic, intraoral scanning equipment comprehensively collects three-dimensional morphological data of the patient's dentition, alveolar bone, and jawbone, as well as data on the occlusal relationship of the upper and lower jaws and the basic condition of the teeth and periodontium. It focuses on capturing jawbone developmental characteristics and details of tooth misalignment, providing precise anatomical and health data for designing treatment plans for adult skeletal malocclusion, adapting to the characteristics of mature adult teeth and stable jawbone morphology. At the patient's end, intraoral sensors continuously collect data on the contact pressure between the orthodontic appliance and the tooth surface, oral motor acceleration, acoustic signals generated by chewing and molars, and intraoral temperature. Simultaneously, it collects three-dimensional displacement and angular change data of individual teeth and the entire row of teeth, capturing real-time data on tooth movement during adult orthodontic treatment. Details of tooth movement and wearing behavior, such as potential interruptions during work, provide data support for dynamic monitoring that aligns with the individual's daily routine. At the end of the orthodontic treatment, imaging equipment captures real-time images of the enamel surface and adhesive layer in the laser-treated area during the debonding process, ensuring safe monitoring of adult enamel (which is more fragile than that of adolescents) during debonding and promptly identifying potential damage risks. All collected data undergoes standardized formatting and is backed up both in the cloud and locally to ensure data integrity and security for complex adult orthodontic procedures, providing comprehensive support for subsequent traceability and review, and supporting the optimization of diagnosis and treatment for complex cases, such as... Figure 2 As shown.

[0030] From the patient's raw three-dimensional oral morphology data, individual oral characteristics were extracted, including moderate skeletal malocclusion, alveolar bone thickness and metabolic status (normal adult metabolism), good periodontal health, maxillary and mandibular occlusal relationship, and tooth density. This clarified the core orthodontic points and individual differences in adult skeletal malocclusion, such as jawbone limitations on tooth movement and tooth stress tolerance. Utilizing a cloud-based standardized database for primary care orthodontic treatment, a basic treatment plan template for moderate skeletal malocclusion was matched to ensure the plan conforms to clinical norms and safety standards for adult orthodontics at the primary care level, reducing the difficulty for primary care physicians in handling complex skeletal cases. The template parameters were adjusted based on the extracted individual oral characteristics to generate a personalized standardized orthodontic treatment plan: the standard tooth movement trajectory was set according to the moderate skeletal malocclusion type and adult alveolar bone metabolic capacity, specifying the displacement path and angle of each tooth at each stage, with a focus on… The plan optimizes the movement of teeth corresponding to the jawbone, such as planning the anterior tooth retraction path at 0.3mm per month, balancing treatment effectiveness with adult tooth tolerance. Wearing specifications are set based on the patient's tooth movement difficulty and appliance fit, with a daily wearing time of 20 hours, a permissible interruption time of 2 hours, and a replacement cycle of 6 weeks, adapting to the daily work and life rhythm of adults to improve compliance and reduce treatment interruptions due to wearing inconvenience. Laser bonding removal parameters are set based on the patient's enamel thickness and adhesive adhesion, with an initial power of 1.0W, a pulse frequency of 18Hz, and a spiral scanning spot path, adapting to the characteristics of adult enamel to ensure safe bonding removal and avoid damage to adult enamel due to improper parameters. Once the plan and parameters are determined, a structured archive is created, providing a clear basis for subsequent plan adjustments and progress tracking during the treatment process, allowing doctors to clearly understand the key points of each treatment step.

[0031] It receives raw orthodontic data and standardized orthodontic treatment plans, providing complete and accurate input data for multidimensional monitoring of moderate skeletal malocclusion in adults, ensuring the reliability of monitoring results; The dynamic adherence risk index algorithm is used to calculate the patient's treatment adherence risk index. The mathematical expression for the dynamic adherence risk index algorithm is as follows: ;in, This is a dynamic compliance risk index. These correspond to four categories of behavioral data: wearing time, wearing force, abnormal occlusion / teeth grinding, and appliance status. Let be the dynamic weight coefficients of the i-th type of behavioral data at time t, satisfying , This refers to the start time of treatment. For the standardized quantification value of the corresponding behavioral data, The time decay coefficient, For the current treatment period, This is a correction factor for the risk of orthodontic appliance breakage. This represents the quantized value of the orthodontic appliance damage characteristic signal. The risk-coupled tooth movement deviation algorithm is used to calculate the actual tooth movement deviation. The mathematical expression of the risk-coupled tooth movement deviation algorithm is: ;in, Let be the actual movement deviation of the j-th tooth at time t. For the current treatment period, Corresponding to all teeth, These are the three-dimensional coordinates of the actual tooth movement. The standard three-dimensional coordinate values ​​for tooth movement. This is the risk index output by the dynamic compliance risk index algorithm. The risk index is set to its maximum value of 100. The compliance risk coupling coefficient, These are the coefficients representing the anatomical features of the teeth. The clinically calibrated risk-bias association algorithm is used to calculate the correlation coefficient between risk and bias. The mathematical expression for the clinically calibrated risk-bias association algorithm is: ;in, Let be the risk-bias correlation coefficient of the j-th tooth at time t. For the current treatment time, Cov The covariance between the risk index and the amount of tooth movement deviation. This is a dynamic compliance risk index. Let be the actual movement deviation of the j-th tooth at time t. For dynamic compliance risk index variance The actual movement deviation of the j-th tooth variance For clinical calibration factors; It accurately identifies potential problems in adult orthodontic treatment caused by fluctuations in compliance or limitations due to skeletal structure, such as insufficient wearing time due to busy work schedules or deviations in tooth movement caused by skeletal malocclusion. Calculations show that the patient's dynamic compliance risk index is 85, and the absolute value of the correlation coefficient is 0.75, both reaching the warning threshold. Targeted pre-intervention suggestions are generated: patients receive a notification to increase wearing time by 1 hour, reminders of abnormal occlusal behavior, and timely check notifications of the appliance's status, helping adult patients adjust their wearing habits, compensate for insufficient compliance caused by work and other factors, and reduce risks. The doctor receives precise markings of tooth movement deviation sites (0.8mm deviation in the anterior tooth region), specific guidance on adjusting the force points of the orthodontic appliances (0.2mm shift of the force point of the anterior bracket towards the lingual side), and a suggestion to slightly extend the treatment period by one month. This provides the doctor with clear intervention directions and operational basis, enabling precise correction of tooth movement deviations. The entire process data of this module is fully recorded, including algorithm input parameters, calculation process data, judgment results, and pre-intervention suggestions, and synchronized to the data iteration and optimization module, accumulating key data for the optimization of diagnosis and treatment of adult skeletal malocclusion cases.

[0032] The system receives raw tooth surface imaging data and basic parameters for laser adhesive removal, completing the initial setup of the laser equipment and ultrasonic cleaning unit. It is adapted to the tooth surface and adhesive conditions of adult patients with moderate skeletal malocclusion, with initial parameters prioritizing gentleness to avoid damage to adult enamel. A high-frequency scanning method is used to analyze the tooth surface condition at a scanning frequency of 100Hz, with a scanning accuracy of 0.5μm. The scanning range covers the laser-affected area and a 1mm perimeter. Real-time imaging data is stored in frame sequence format, with each frame containing quantitative annotations of adhesive thickness and enamel surface condition, accurately capturing details of the adult tooth surface and focusing on monitoring for the presence of minute cracks in the enamel, providing precise guidance for subsequent operations. Based on scan feedback, laser parameters are dynamically adjusted: power from an initial 1.0W to 0.9W in 0.1W increments, and pulse frequency from 18Hz to 16Hz in 2W increments. The laser beam movement path is adjusted with a precision of 0.1 mm, and the adjustment interval is synchronized with the scanning frequency to ensure precise laser action without damaging the relatively fragile enamel of adults, achieving minimally invasive adhesive removal. Once the tooth surface condition meets the standards, the laser operation is stopped and ultrasonic cleaning is triggered. The ultrasonic cleaning power is set to 35 kHz, and the cleaning time is dynamically set to 1.5 minutes based on the amount of adhesive residue, thoroughly removing residue while avoiding over-operation and protecting adult dental health. During the cleaning process, the ultrasonic vibration amplitude is monitored in real time to prevent excessive vibration from causing additional damage to adult teeth. After cleaning, a tooth surface condition inspection report is automatically generated, confirming that there are no microcracks deeper than 5 μm on the enamel surface, the surface roughness Ra is 0.18 μm, and there is no visible or microscopic adhesive residue, ensuring the safety and cleanliness of the adhesive removal procedure for adult patients and protecting their subsequent oral health.

[0033] The entire process data from the first four modules is collected, including raw jawbone and tooth data, sensor data, treatment plan parameters, algorithm calculation data, pre-intervention suggestions, operation execution parameters, and test reports, to construct a complete data archive for adult cases of moderate skeletal malocclusion, covering the entire treatment process details of complex skeletal cases. The data is cleaned and organized, and invalid data is removed before multi-dimensional clinical statistical analysis is conducted to uncover key patterns and optimization points in the treatment of adult skeletal malocclusion, such as the influence of skeletal structure on tooth movement and the balance between laser parameters and adult enamel protection. Based on the analysis results, standardized data for cloud-based primary care orthodontic treatment is optimized. The database includes treatment plan templates and reference data related to moderate skeletal malocclusion, enriching the parameters for tooth movement trajectory, wearing guidelines, and laser debonding for skeletal malocclusion. It also adjusts the base coefficients of the dynamic compliance risk index algorithm, the risk-coupled tooth movement deviation algorithm, and the clinically calibrated risk-deviation correlation algorithm to better suit the orthodontic characteristics and compliance features of adult skeletal malocclusion. The optimized results are then pushed to the standardized treatment plan generation module and the dynamic monitoring and prediction module, providing more accurate and clinically relevant support for the diagnosis and treatment of subsequent adult cases of moderate malocclusion, and helping primary care physicians better handle complex adult skeletal orthodontic cases.

[0034] In summary, for adults with moderate skeletal malocclusion, the system employs comprehensive data acquisition tailored to the individual's lifestyle, capturing key information about the jawbone and teeth. The standardized treatment plan generation module matches a basic template for skeletal malocclusion, optimizing tooth movement planning and fitting parameters to suit adult enamel characteristics. The dynamic monitoring and prediction module uses three types of algorithms to identify compliance risks and movement deviations, generating targeted intervention suggestions to precisely correct adult orthodontic problems. The precise debonding module reduces the risk of enamel damage in adults with gentle parameters and closed-loop control. The data iteration and optimization module integrates data from the entire process, optimizing skeletal malocclusion-related plans and algorithm coefficients, assisting primary care physicians in efficiently handling complex adult cases and achieving safe, accurate, and adult-appropriate orthodontic treatment.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A standardized treatment process intelligent management and control system for primary-level orthodontic clinics, characterized in that: The system includes: Full-domain data acquisition module: used to collect raw orthodontic data from clinic, patient, and treatment completion operations, and after format standardization, perform dual backups in the cloud and locally; Standardized treatment plan generation module: Receives raw three-dimensional morphological data of the patient's oral cavity, relies on the cloud-based standardized database for primary orthodontic treatment, and generates a standardized orthodontic treatment plan based on the patient's individual oral characteristics. It outputs standard tooth movement trajectory, wearing specifications, and basic parameters for laser debonding, and archives them in a structured manner. Dynamic monitoring and prediction module: Receives raw orthodontic data and standardized orthodontic treatment plans, runs a dynamic compliance risk index algorithm to calculate the patient's treatment compliance risk index, runs a risk-coupled tooth movement deviation algorithm to calculate the actual tooth movement deviation, runs a clinically calibrated risk-deviation correlation algorithm to calculate the correlation coefficient between risk and deviation, makes a judgment to generate pre-intervention suggestions, and records the entire process data of the dynamic monitoring and prediction module. Precision debonding module: Receives raw tooth surface imaging data and basic parameters for laser debonding, completes the initial settings of the laser equipment and ultrasonic cleaning unit, analyzes the tooth surface condition and dynamically adjusts the laser parameters to form a closed-loop control, stops the laser operation and triggers ultrasonic cleaning after the target is met, and records the operation data. Data Iteration and Optimization Module: Collects the full-process data from the first four modules, cleans and organizes it, and performs multi-dimensional clinical statistical analysis. It optimizes the cloud-based standardized database for primary care orthodontic treatment and the basic coefficients of the algorithm, and pushes the optimization results to the standardized plan generation module and the dynamic monitoring and prediction module.

2. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The method for collecting orthodontic raw data from the clinic, patient, and orthodontic treatment completion operation in the full-domain data acquisition module is as follows: The clinic collects three-dimensional morphological data of the patient's dentition, alveolar bone, and jawbone, as well as the occlusal relationship of the upper and lower jaws and the basic condition of the teeth and periodontium through an intraoral scanning device. The patient-side device continuously collects data on the contact pressure between the orthodontic appliance and the tooth surface, oral motion acceleration, acoustic signals generated by chewing and molars, and intraoral temperature data through sensors worn in the oral cavity. It also collects three-dimensional displacement and angular change data of individual teeth and the entire row of teeth. The orthodontic finishing operation device uses imaging equipment to collect image data of the enamel surface and adhesive layer in the laser-treated area during the debonding operation. After all data is collected, it is processed in a standardized format and backed up in both the cloud and local environments.

3. The standardized treatment process intelligent management and control system for primary-level orthodontic clinics according to claim 1, characterized in that, The standardized treatment plan generation module, relying on the cloud-based standardized database for basic orthodontic treatment, generates a standardized orthodontic treatment plan based on the patient's individual oral characteristics. Specifically, it extracts individual oral characteristics from the received raw three-dimensional morphological data of the patient's mouth, including malocclusion type, alveolar bone thickness and metabolic status, dental and periodontal health conditions, maxillary and mandibular occlusion relationship, and tooth density. It then matches a corresponding basic treatment plan template to the cloud-based standardized database, adjusts the template parameters based on the extracted individual oral characteristics, and generates a standardized orthodontic treatment plan. The standard tooth movement trajectory sets the displacement path and angle of each tooth at each stage according to the malocclusion type and alveolar bone metabolic capacity. The wearing specifications parameters set the daily wearing time, allowable interruption time, and replacement cycle based on the difficulty of tooth movement and the appliance fit. The laser debonding basic parameters set the initial power, pulse frequency, and spot path based on the enamel thickness and adhesive adhesion. After the plan and all parameters are determined, they are archived in a structured manner.

4. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The standardized treatment plan generation module contains a standardized database for primary-level orthodontic treatment, which includes various types of data specific to primary-level orthodontic treatment. Specifically, it covers basic orthodontic treatment plan templates categorized by malocclusion type, reference data on alveolar bone metabolism rate and tooth movement rate stratified by age, anatomical feature data for different tooth positions, benchmark data on stage displacement and angle of tooth movement, benchmark data on wearing duration and replacement cycle of various orthodontic appliances, initial parameters for laser debonding corresponding to different enamel conditions and adhesive types, and standardized operational procedures for primary-level orthodontic treatment. All types of data are constructed based on primary-level clinical practice cases, providing a basis for matching basic plan templates and adjusting parameters to generate standardized orthodontic treatment plans.

5. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The mathematical expression for the dynamic compliance risk index algorithm run by the dynamic monitoring and prediction module is: ;in, This is a dynamic compliance risk index. These correspond to four categories of behavioral data: wearing time, wearing force, abnormal occlusion / teeth grinding, and appliance status. Let be the dynamic weight coefficients of the i-th type of behavioral data at time t, satisfying , This refers to the start time of treatment. For the standardized quantification value of the corresponding behavioral data, The time decay coefficient, For the current treatment period, This is a correction factor for the risk of orthodontic appliance breakage. This is the quantized value of the characteristic signal of orthodontic appliance damage.

6. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The mathematical expression for the risk-coupled tooth movement deviation algorithm run by the dynamic monitoring and prediction module is: ;in, Let be the actual movement deviation of the j-th tooth at time t. For the current treatment period, Corresponding to all teeth, These are the three-dimensional coordinates of the actual tooth movement. The standard three-dimensional coordinate values ​​for tooth movement. This is the risk index output by the dynamic compliance risk index algorithm. The risk index is set to its maximum value of 100. The compliance risk coupling coefficient, The coefficient represents the anatomical characteristics of the teeth.

7. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The mathematical expression for the clinically calibrated risk-bias association algorithm run by the dynamic monitoring and prediction module is: ;in, Let be the risk-bias correlation coefficient of the j-th tooth at time t. For the current treatment time, Cov The covariance between the risk index and the amount of tooth movement deviation. This is a dynamic compliance risk index. Let be the actual movement deviation of the j-th tooth at time t. For dynamic compliance risk index variance The actual movement deviation of the j-th tooth variance For clinical calibration factors.

8. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The specific content of the pre-intervention suggestions generated in the dynamic monitoring and prediction module is as follows: the judgment condition is a dynamic compliance risk index ≥80, or an absolute value of the correlation coefficient output by the clinically calibrated risk-bias correlation algorithm ≥0.7; the patient-side pre-intervention suggestions include prompts for adjusting wearing time, prompts for abnormal occlusion and bruxism, and prompts for checking the status of the orthodontic appliance; the doctor-side pre-intervention suggestions include marking of tooth movement deviation sites, guidance on adjusting the force points of the orthodontic appliance, and guidance on adjusting the treatment cycle.

9. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The precise debonding module analyzes the tooth surface condition using a high-frequency scanning method with a scanning frequency of 100Hz and a scanning accuracy of 0.5μm. The scanning range covers the laser-acting area and the surrounding 1mm range. Real-time imaging data is stored in the form of frame sequences, and each frame image contains quantitative annotations of adhesive thickness and enamel surface condition. The dynamic adjustment step of laser parameters is that the power adjustment increment does not exceed 0.1W, the pulse frequency adjustment increment does not exceed 2Hz, and the spot movement path adjustment accuracy is not less than 0.1mm. The adjustment interval is synchronized with the scanning frequency.

10. The intelligent management and control system for standardized diagnosis and treatment processes in primary orthodontic clinics according to claim 1, characterized in that, The precise adhesive removal module operates at an ultrasonic cleaning power of 20-40kHz, and the cleaning time is dynamically set according to the adhesive residue, with a minimum of 30 seconds and a maximum of 2 minutes. The ultrasonic vibration amplitude is monitored in real time during the cleaning process, and a tooth surface condition inspection report is automatically generated after the cleaning is completed. The enamel surface integrity is judged by the absence of microcracks with a depth exceeding 5μm, a surface roughness Ra≤0.2μm, and the absence of adhesive residue visible to the naked eye or at the microscopic level.

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