Digitized oral tooth treatment accurate monitoring and analysis method and system

Abstract

The invention discloses a digital oral tooth treatment accurate monitoring and analysis method and system, and relates to the technical field of orthodontic treatment, and the method comprises the steps: receiving oral three-dimensional scanning data of a current treatment stage, extracting a dental arch feature point data set, carrying out the spatial alignment processing of the dental arch feature point data set and a standardized dental arch feature point data set of a historical treatment stage, and obtaining a standard dental arch feature point data set; based on the aligned feature point data set, calculating an inverse geometric difference parameter, generating a comprehensive symmetry index according to the geometric difference parameter, comparing a change trend of the comprehensive symmetry index with a change trend of a target comprehensive symmetry index in a preset treatment path model, judging whether to generate an adjustment instruction, and adjusting the treatment path according to the adjustment instruction. Calculating a treatment parameter adjustment amount, and updating the treatment path model according to the treatment parameter adjustment amount; the method has the advantages that the dental arch symmetry evolution trend is dynamically monitored, a treatment path closed-loop feedback mechanism is established, and individualized treatment parameter adjustment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oral orthodontic treatment, and in particular to a method and system for precise monitoring and analysis of digital oral dental treatment. Background Art

[0002] In modern orthodontic treatment, with the advancement of digital technology and 3D imaging equipment, more and more treatment processes rely on digital modeling and simulation of the patient's oral structure. Especially with the widespread use of invisible braces, doctors typically develop a phased treatment path based on the patient's initial dentition model and treatment goals, using a series of customized appliances to guide the gradual movement of teeth.

[0003] However, in the actual treatment process, patients' biological responses have significant individual differences, and there are problems such as error accumulation and response lag during the wearing of braces, which can easily cause the actual dentition state to deviate from the expected path, thereby affecting the treatment effect and cycle control.

[0004] Although some systems are currently capable of visually analyzing tooth displacement during treatment, the following technical bottlenecks still exist:

[0005] 1) There is a lack of a dynamic symmetry index evaluation system for the treatment process, which makes it impossible to accurately describe the symmetry evolution trend of the dental arch structure at different stages;

[0006] 2) A closed-loop feedback mechanism with the treatment pathway model has not been established, resulting in a lack of automatic control capabilities in response to deviations from the patient's tooth movement targets;

[0007] 3) The adjustment mechanism relies on manual judgment or single rule triggering, lacks an individualized adaptive learning mechanism, and is unable to cope with nonlinear changes in complex treatment processes;

[0008] 4) Existing solutions generally focus on determining the status of a single appliance and lack the ability to continuously monitor and optimize parameters throughout the entire treatment cycle.

[0009] Therefore, a digital oral and dental treatment precise monitoring and analysis method and system are proposed. Summary of the Invention

[0010] In view of the above-mentioned state of the art, the present application is proposed. The embodiments of the present application provide a digital oral dental treatment precision monitoring and analysis method and system, which can dynamically monitor the evolution trend of dental arch symmetry, establish a closed-loop feedback mechanism for the treatment path, and achieve the advantages of individualized treatment parameter adjustment.

[0011] According to one aspect of the present application, a method for precise monitoring and analysis of digital oral dental treatment is provided, comprising: receiving oral 3D scanning data of a current treatment stage; obtaining a standardized dental arch feature point dataset of a historical treatment stage; extracting the dental arch feature point dataset from the oral 3D scanning data of the current treatment stage, and performing spatial alignment processing with the standardized dental arch feature point dataset; calculating a plurality of geometric difference parameters reflecting the symmetry of the dental arch based on the aligned feature point dataset; generating a comprehensive symmetry index based on the geometric difference parameters, and recording it as time series data; and comparing the changing trend of the comprehensive symmetry index in the time series data with the preset treatment path. The target comprehensive symmetry index change trend of the corresponding stage in the treatment pathway model is compared, and it is determined whether to generate an adjustment instruction based on the comparison result; according to the adjustment instruction, the treatment parameter adjustment amount including time interval adjustment and allowable deviation range adjustment is calculated through a mapping table of preset rules or a pre-trained machine learning model; the treatment pathway model is updated according to the treatment parameter adjustment amount to maintain the comprehensive symmetry index within the range of the treatment pathway model; wherein, the preset treatment pathway model includes the target comprehensive symmetry index, time node information and allowable deviation range preset for each treatment stage, which is used as a dynamic benchmark for the trend comparison of the comprehensive symmetry index in the actual treatment process.

[0012] According to another aspect of the present application, a digital oral dental treatment precision monitoring and analysis system is provided, comprising: a first data acquisition module for receiving oral three-dimensional scanning data of the current treatment stage; a second data acquisition module for obtaining a standardized dental arch feature point data set of a historical treatment stage; a data processing module for extracting the dental arch feature point data set from the oral three-dimensional scanning data of the current treatment stage, and performing spatial alignment processing with the standardized dental arch feature point data set; a difference parameter calculation module for calculating a plurality of geometric difference parameters reflecting the symmetry of the dental arch based on the aligned feature point data set; and a comprehensive symmetry index generation module for generating a comprehensive symmetry index based on the geometric difference parameters. The module comprises a module for generating an adjustment instruction, a module for comparing the change trend of the comprehensive symmetry indicator in the time series data with the change trend of the target comprehensive symmetry indicator of the corresponding stage in the preset treatment pathway model, and a module for determining whether to generate an adjustment instruction based on the comparison result; a module for obtaining a treatment parameter adjustment amount, a module for calculating the treatment parameter adjustment amount including the time interval adjustment and the allowable deviation range adjustment based on the adjustment instruction through a mapping table of preset rules or a pre-trained machine learning model; and a module for updating the treatment pathway model based on the treatment parameter adjustment amount to maintain the comprehensive symmetry indicator within the range of the treatment pathway model.

[0013] According to another aspect of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which implement the steps of the above-described method when executed by the processor.

[0014] According to another aspect of the present application, a computer storage medium is provided, on which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the steps of the above method are implemented.

[0015] Compared with the existing technology, the digital oral dental treatment precision monitoring and analysis method and system according to the embodiment of the present application can generate treatment parameter adjustment amounts by calculating the dental arch symmetry index and dynamically comparing it with the preset path, and establish a closed-loop feedback mechanism. It has the advantages of dynamically monitoring the evolution trend of dental arch symmetry, establishing a closed-loop feedback mechanism for the treatment path, and realizing individualized treatment parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 This is a flow chart of the digital oral dental treatment precision monitoring and analysis method of the present invention.

[0018] Figure 2 This is a flow chart of the generation and adjustment instructions of the digital oral and dental treatment precision monitoring and analysis method of the present invention.

[0019] Figure 3 This is a hierarchical trigger logic flow chart of the digital oral and dental treatment precision monitoring and analysis method of the present invention.

[0020] Figure 4 This is a block diagram of the digital oral and dental treatment precision monitoring and analysis system of the present invention.

[0021] Figure 5 This is a block diagram of an electronic device according to the present invention. DETAILED DESCRIPTION

[0022] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0023] Application Overview

[0024] In the traditional existing digital orthodontic treatment system:

[0025] Although basic digitalization has been achieved for three-dimensional modeling and staged correction path planning, the lack of dynamic symmetry assessment mechanism has led to a lack of quantitative monitoring dimension for the evolution of dental arch morphology.

[0026] The lack of dynamic adaptability between the static characteristics of the treatment pathway model and the actual biological response of the patient causes the adjustment of treatment parameters to lag behind the actual clinical changes, and the deviation of dental arch symmetry forms a cumulative effect in the continuous treatment stages.

[0027] For example, in the invisible aligner replacement cycle decision scenario:

[0028] The clinical system triggers appliance replacement based on preset time points, and does not establish a continuous tracking mechanism for dental arch symmetry indicators;

[0029] The current 3D scanning data is only compared with the initial model for morphological purposes. A dynamic alignment system for feature points across treatment stages has not been established, making it impossible to detect the progressive shift of bilateral arch width and arch height parameters.

[0030] When the patient wore the third-stage braces, although the tooth surface point cloud data met the morphological matching threshold of the current stage, it failed to recognize that there was a millimeter-level difference in the distance between the bilateral canine vertices. This difference continued to amplify in the subsequent treatment stage, resulting in the sixth-stage braces being unable to achieve the predetermined occlusal relationship.

[0031] If the above issues are not resolved, then:

[0032] The progressive asymmetric changes in dental arch morphology will exceed the tolerance limit of the preset pathway model, leading to cascading failures of the clinical pathway.

[0033] The mismatch between appliance replacement decisions and actual tooth movement rates will lead to an increased risk of root resorption, while the lack of a dynamic baseline adjustment mechanism will force invasive interventions in the middle and late stages of treatment.

[0034] The static characteristics of the treatment pathway model cannot adapt to the nonlinear characteristics of individual biomechanical responses, which ultimately leads to prolonged treatment cycles and increased recurrence rates.

[0035] When faced with the above problems, the present application first realized that the lack of a dynamic symmetry evaluation system leads to the cumulative effect of dental arch morphological deviation. The traditional scheme only performs morphological matching judgment in a single stage and cannot capture the progressive changes across stages. To this end, the present application attempts to establish a multi-stage feature point spatial alignment system, and realizes dynamic comparison of historical and current data through standardized dental arch feature point data sets. At the same time, the present application found that the static treatment path model lacks a feedback mechanism, and attempts to introduce a comparison of the changing trends of time series data and preset models, and designs a comprehensive indicator generation method based on geometric difference parameters. In order to solve the problem of lagging adjustment mechanism, the present application explores the dual-path calculation of parameter adjustment through mapping tables and machine learning models, and finally forms a closed-loop control logic.

[0036] In this regard, this application proposes a method and system for precise monitoring and analysis of digital oral and dental treatment.

[0037] Exemplary Methods

[0038] like Figure 1-Figure 3 As shown, according to an embodiment of the present application, a digital oral dental treatment precision detection and analysis method includes: receiving oral three-dimensional scanning data of a current treatment stage; obtaining a standardized dental arch feature point dataset of a historical treatment stage; extracting the dental arch feature point dataset from the oral three-dimensional scanning data of the current treatment stage, and spatially aligning it with the standardized dental arch feature point dataset; based on the aligned feature point dataset, calculating multiple geometric difference parameters reflecting the symmetry of the dental arch; generating a comprehensive symmetry index based on the geometric difference parameters and recording it as time series data; comparing the change trend of the comprehensive symmetry index in the time series data with the change trend of the target comprehensive symmetry index of the corresponding stage in the preset treatment pathway model, and determining whether to generate an adjustment instruction based on the comparison result; according to the adjustment instruction, calculating a treatment parameter adjustment amount including time interval adjustment and allowable deviation range adjustment through a mapping table of preset rules or a pre-trained machine learning model; updating the treatment pathway model based on the treatment parameter adjustment amount to maintain the comprehensive symmetry index within the treatment pathway model range; wherein the preset treatment pathway model includes a target comprehensive symmetry index, time node information and allowable deviation range preset for each treatment stage, which is used as a dynamic benchmark for comparing the trend of the comprehensive symmetry index during the actual treatment process.

[0039] Among them, oral 3D scanning data refers to the 3D point cloud or mesh model of the patient's oral structure obtained through 3D scanning equipment. It can be achieved by using intraoral scanners or CBCT imaging technology to record the shape and position changes of teeth in real time during treatment.

[0040] Among them, the standardized dental arch feature point dataset refers to the three-dimensional coordinate set of key anatomical landmark points pre-defined according to orthodontic treatment standards. It can be constructed through expert annotation or cluster analysis of historical case data as a benchmark reference for the evaluation of dental arch morphological symmetry.

[0041] Among them, spatial alignment processing refers to mapping the dental arch feature point data sets at different time points to the same coordinate system through three-dimensional coordinate transformation. It can be implemented by using an iterative closest point algorithm or a rigid registration algorithm to eliminate spatial offsets caused by patient position or scanning equipment errors.

[0042] Among them, the geometric difference parameter refers to the degree of asymmetry of the bilateral dental arches in length, width, height and projected area quantified by mathematical methods. Specifically, it can be achieved by Euclidean distance calculation, area integration or vector angle analysis to construct a multidimensional symmetry evaluation index.

[0043] Among them, the comprehensive symmetry index refers to a single evaluation value generated by normalizing and weighting multiple geometric difference parameters. It can be implemented by linear weighting or nonlinear function mapping to simplify the trend analysis of symmetry evolution during the treatment process.

[0044] Among them, time series data refers to a set of comprehensive symmetry indicators recorded in sequence according to the treatment stage. It can be stored in a database or time series file format to reveal the temporal correlation and dynamic deviation characteristics of symmetry changes.

[0045] Among them, the preset treatment pathway model specifically refers to a digital treatment plan that includes target symmetry indicators, time nodes, and allowable deviations for each stage. It can be generated through clinical experience summary or machine learning model training as a benchmark for dynamically adjusting treatment parameters.

[0046] Among them, the adjustment instruction refers to the parameter correction command generated according to the degree of deviation between the actual symmetry index and the preset path. It can be triggered by a rule engine or classification model to automatically adjust the wearing cycle or morphological parameters of the orthodontic appliance.

[0047] The treatment parameter adjustment amount refers to the specific correction value for the time interval and deviation range, which can be calculated through a table lookup method or a regression model to optimize the execution strategy of the subsequent treatment stage.

[0048] Among them, the treatment pathway model update refers to the data iteration process of feeding back the adjustment amount to the preset treatment plan in real time. It can be implemented by version control or parameter rewriting mechanism to maintain the dynamic consistency of the treatment process and the expected path.

[0049] The core innovation of this application lies in constructing a closed-loop feedback control system based on dynamic symmetry indicators and preset treatment paths. By comparing the symmetry evolution trends of actual treatment data and preset models in real time, it automatically generates treatment parameter adjustment instructions and updates the path model, thereby solving the technical bottleneck of traditional methods that lack dynamic evaluation systems and adaptive control capabilities.

[0050] The working process and principle of the present application are as follows: first, the oral three-dimensional scanning data of the current treatment stage is received, and the standardized dental arch feature point data set of the historical treatment stage is obtained. Then, the dental arch feature point data set in the oral three-dimensional scanning data of the current treatment stage is extracted, and spatial alignment processing is performed with the standardized dental arch feature point data set. Based on the aligned feature point data set, multiple geometric difference parameters reflecting the symmetry of the dental arch are calculated, and a comprehensive symmetry index is generated according to the geometric difference parameters and recorded as time series data. Then, the change trend of the comprehensive symmetry index in the time series data is compared with the change trend of the target comprehensive symmetry index of the corresponding stage in the preset treatment path model. According to the comparison result, it is determined whether to generate an adjustment instruction. If adjustment is required, according to the adjustment instruction, the treatment parameter adjustment amount including time interval adjustment and allowable deviation range adjustment is calculated through a mapping table with preset rules or a pre-trained machine learning model. Finally, the treatment path model is updated according to the treatment parameter adjustment amount to maintain the comprehensive symmetry index within the range of the treatment path model. In this way, dynamic monitoring and adjustment of the treatment process are achieved to ensure that dental treatment is carried out according to the expected path.

[0051] In some of the above-mentioned schemes of the present application, it is proposed to extract the dental arch feature point dataset from the oral three-dimensional scanning data of the current treatment stage and perform spatial alignment processing with the standardized dental arch feature point dataset. However, when screening the dental planar point cloud data, if the accuracy threshold is not set, non-critical points with excessive coordinate fluctuations may be retained, resulting in errors in feature point identification, affecting the accuracy of subsequent alignment and geometric difference parameter calculation. In addition, if the key points are not identified based on the preset anatomical feature template, the subjective judgment differences of different operators may lead to inconsistent feature point selection, making it difficult to establish a unified analysis benchmark.

[0052] The present application further proposes to filter the tooth surface point cloud data based on a preset accuracy threshold, retain the key points whose coordinate fluctuation is less than the preset accuracy threshold, and identify the midpoint of the central incisor, the apex of the bilateral canines and the apex of the first molar from the key points as the dental arch feature point data set according to the preset anatomical feature template.

[0053] The preset accuracy threshold may range from 0.1 mm to 0.3 mm. For example, when the accuracy threshold is set to 0.2 mm, only point cloud data with coordinate fluctuation values ​​between adjacent scanning points less than the threshold are retained.

[0054] Among them, the selected key points must conform to the continuous and smooth characteristics of the tooth surface morphology to avoid the introduction of abnormal outliers due to scanning noise.

[0055] Among them, the preset anatomical feature template defines the positioning rule of the midpoint of the central incisor as the geometric center of the line connecting the contact points of adjacent incisors, the positioning rule of the bilateral canine apex as the projection vertex of the highest point of the canine cusp along the long axis of the tooth, and the positioning rule of the first molar apex as the midpoint of the line connecting the mesio-buccal cusp apex and the distal lingual cusp apex. This template ensures the comparability of the spatial positions of feature points in different cases and different treatment stages through the mapping relationship of the anatomical standard coordinate system.

[0056] Specifically, when performing the screening, the original point cloud data is input into the preset filtering algorithm, and the standard deviation of the coordinate fluctuation of each point and its adjacent points in the neighborhood is calculated. If the fluctuation value exceeds the threshold, it is eliminated, and the retained key points are input into the feature recognition algorithm after downsampling. The algorithm loads the geometric constraints in the preset anatomical feature template, and determines the candidate point set that meets the midpoint of the central incisor, the vertices of the bilateral canines and the vertices of the first molar through iterative calculation. Finally, the point with the highest matching degree with the template is selected as the feature point.

[0057] For example, when identifying the midpoint of the central incisor, the algorithm first extracts the point cloud of the incisor area, fits the line connecting the incisor contact points, calculates the coordinates of the midpoint of the line, and searches for the point with the smallest fluctuation in the neighborhood of the point as the final feature point. Thus, through the coordinated application of the preset accuracy threshold and the anatomical feature template, it not only eliminates noise interference but also realizes the standardized identification of feature points, providing a high-precision and consistent data basis for subsequent spatial alignment and symmetry analysis.

[0058] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0059] First, the dentition surface point cloud data is filtered based on a preset accuracy threshold, retaining key points whose coordinate fluctuations are less than the preset accuracy threshold. The preset accuracy threshold can be set to 0.1mm. By performing noise reduction and accuracy screening on the oral 3D scan data, more stable and reliable dentition surface point cloud data can be obtained.

[0060] Next, based on the preset anatomical feature template, the central incisor midpoint, bilateral canine apex, and first molar apex were identified from the key points as a dental arch feature point dataset. The preset anatomical feature template contains tooth morphology and position information and is used to locate and extract specific anatomical landmarks in the point cloud data. The central incisor midpoint is located at the midpoint of the incisal edge of the maxillary central incisor, the bilateral canine apex is located at the tip of the maxillary canine, and the first molar apex is located at the tip of the mesiobuccal cusp of the maxillary first molar. These feature points constitute the key dataset describing the dental arch morphology.

[0061] Through the above technical solution, this application achieves efficient processing of oral 3D scanning data and accurate extraction of key feature points. By setting a reasonable accuracy threshold, noise and unstable data generated during the scanning process can be effectively removed, improving the accuracy of subsequent analysis. The application of preset anatomical feature templates makes the feature point recognition process more standardized and automated, reducing errors caused by human judgment. This method not only improves the quality and reliability of the dental arch feature point dataset, but also lays the foundation for subsequent symmetry analysis and treatment monitoring.

[0062] In some of the above-mentioned schemes of this application, although the symmetry of the dental arch can be reflected by calculating the geometric difference parameters, the existing methods are difficult to comprehensively characterize the symmetry evolution of the dental arch morphology through a single parameter, and lack a dynamic fusion mechanism for multi-dimensional geometric differences, resulting in one-sidedness and static limitations in symmetry evaluation.

[0063] The present application further proposes that the geometric difference parameters include the absolute difference in left and right arch lengths, the relative difference in arch width between the bilateral canine apexes, the arch height difference from the midpoint of the central incisor to the bilateral first molars, and the area ratio of the bilateral dental arch projection areas; generating a comprehensive symmetry index includes normalizing the absolute difference, relative difference and arch height difference and outputting the normalized parameters, and weighted summing the normalized parameters with the area ratio as the weight.

[0064] Among them, the absolute difference in arch length between the left and right sides is achieved by measuring the difference in straight-line distance between the vertices of the first molars on the left and right sides of the dental arch. The relative difference in arch width between the vertices of the bilateral canines is expressed as a percentage of the difference in the lateral spacing between the vertices of the bilateral canines and the reference spacing. The difference in arch height from the midpoint of the central incisor to the line connecting the midpoint of the central incisor to the vertices of the bilateral first molars is determined by calculating the difference in vertical distance from the midpoint of the central incisor to the line connecting the vertices of the bilateral first molars. The area ratio of the projection area of ​​the bilateral dental arches is calculated based on the ratio of the area enclosed by the left and right dental arch contours in the two-dimensional projection plane.

[0065] Among them, the normalization processing uses the maximum and minimum scaling method to map each geometric difference parameter to the range of 0 to 1. In the weighted summation process, the area ratio is used as a weight factor to adjust the influence of different parameters on the comprehensive index.

[0066] Specifically, the absolute difference in arch length between the left and right sides reflects the symmetry of the dental arch length, the relative difference in arch width between the apexes of the bilateral canines represents the proportional coordination of the dental arch width, the difference in arch height from the midpoint of the central incisor to the line connecting the bilateral first molars evaluates the vertical symmetry of the dental arch, and the area ratio quantifies the level of symmetry of the overall dental arch shape. Normalization eliminates the dimensional differences of different parameters, making multi-dimensional geometric differences comparable. The weighted summation is performed using the area ratio as the weight to ensure that the contribution of the overall dental arch shape symmetry to the comprehensive index matches its actual clinical weight.

[0067] For example, when the ratio of the projection areas of the bilateral dental arches deviates from 1, the weighting mechanism automatically increases the proportion of the normalized parameter in the comprehensive index, strengthening the decision-making influence of the overall morphological symmetry of the dental arch on the generation of adjustment instructions. The multi-parameter fusion model constructed in this way can dynamically reflect the multi-dimensional characteristics of the dental arch symmetry and provide a quantitative basis for the dynamic adjustment of the treatment path.

[0068] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0069] The geometric difference parameters include the absolute difference in arch length between the left and right sides, the relative difference in arch width between the bilateral canine apexes, the difference in arch height from the midpoint of the central incisor to the bilateral first molars, and the area ratio of the bilateral dental arch projection areas.

[0070] Generating a comprehensive symmetry index involves the following steps:

[0071] The absolute difference, relative difference and arch height difference are normalized and the normalized parameters are output.

[0072] The normalized parameters are weighted and summed with the area ratio as the weight to generate a comprehensive symmetry index.

[0073] In specific implementations, the absolute difference in arch length between the left and right arches is calculated by calculating the absolute value of the difference in arch length between the left and right arches. The relative difference in arch width between the canine apexes is calculated by calculating the ratio of the difference in the distance between the left and right canine apexes to the average value. The difference in arch height between the midpoint of the central incisor and the line connecting the first molars is calculated by calculating the difference in arch height between the left and right arches. The ratio of the projected areas of the two arches is the ratio of the areas enclosed by the left and right arch outlines in the two-dimensional projection plane.

[0074] Normalization uses the maximum and minimum normalization method to map each parameter to the range of 0 to 1. When performing weighted summation, the area ratio is used as the weight coefficient and multiplied by the sum of the other normalized parameters to obtain the final comprehensive symmetry index.

[0075] Through the above technical solution, this application can comprehensively evaluate dental arch symmetry, comprehensively considering multiple geometric parameters to improve the accuracy and reliability of symmetry assessment. Through normalization and weighted summation, the influence of different parameter dimensions is eliminated, making the comprehensive symmetry index highly comparable. At the same time, using area ratio as a weight emphasizes the importance of the overall dental arch shape, making the evaluation results more reasonable.

[0076] In some of the above-mentioned schemes of the present application, although the need to adjust the treatment parameters is determined by comparing the changing trend of the comprehensive symmetry index of the time series data with the preset treatment pathway model, short-term fluctuations or delayed responses may occur during the actual treatment process, resulting in the comparison results of a single data point being unable to accurately reflect the actual degree of deviation, thereby affecting the timing and accuracy of generating the adjustment instructions.

[0077] The present application further proposes applying sliding window analysis to the comprehensive symmetry index data in the time series, calculating the actual comprehensive symmetry index change rate within the window period, and comparing the actual comprehensive symmetry index change rate with the target comprehensive symmetry index change rate of the corresponding stage in the preset treatment pathway model. When the actual comprehensive symmetry index change rate continues to exceed the allowable deviation range of the target comprehensive symmetry index change rate, an adjustment instruction is generated according to the deviation direction and amplitude.

[0078] Among them, sliding window analysis uses a data set with a fixed time span as a calculation unit, for example, a window period of five days, and each time it slides forward one day to form a new calculation window.

[0079] Among them, the actual comprehensive symmetry index change rate is calculated by the linear regression method to obtain the slope value of the data points within the window period, and the target comprehensive symmetry index change rate is extracted from the preset treatment pathway model to obtain the expected change slope of the corresponding time interval.

[0080] The allowable deviation range is set as an absolute value percentage interval according to different treatment stages, such as ±15%.

[0081] The deviation direction is determined by the difference in algebraic signs between the actual rate of change and the target rate of change, and the magnitude is calculated by the absolute value of the difference between the actual rate of change and the target rate of change.

[0082] Specifically, after the time series data for the comprehensive symmetry index reaches the preset window length, data points within the window period are intercepted forward from the current time point. A linear trend line is fitted using the least squares method, and the slope is extracted as the actual rate of change. This actual rate of change is numerically compared with the target rate of change for the same treatment stage in the preset treatment pathway model. If the actual rate of change remains outside the allowable deviation range of the target rate of change for three consecutive windows, the adjustment instruction generation mechanism is triggered.

[0083] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0084] First, apply a sliding window analysis to the time series of comprehensive symmetry index data to calculate the actual rate of change of the comprehensive symmetry index within the window period. For example, you can set the sliding window size to 30 days, move forward 5 days at a time, and calculate the average rate of change of the comprehensive symmetry index over 30 days.

[0085] Next, the actual rate of change in the comprehensive symmetry index is compared with the target rate of change in the corresponding stage of the preset treatment pathway model. Specifically, the difference between the actual rate of change and the target rate of change can be calculated to determine whether it exceeds the allowable deviation range.

[0086] Finally, if the actual rate of change of the comprehensive symmetry index continues to exceed the tolerance range of the target rate of change, an adjustment instruction is generated based on the direction and magnitude of the deviation. For example, if the actual rate of change exceeds the tolerance range of the target rate of change by plus or minus 10% for three consecutive sliding window periods, an adjustment instruction is triggered.

[0087] Through the above technical solution, this application achieves dynamic monitoring and timely adjustment of the treatment process. This allows for precise control of treatment progress and avoids situations where the treatment effect deviates from the expected goal. Furthermore, by setting up hierarchical triggering logic, adjustments are made more flexible and accurate, effectively improving the accuracy and efficiency of treatment.

[0088] In some of the above-mentioned schemes in this application, the logic for generating adjustment instructions only judges the deviation based on a single threshold, and is unable to distinguish the degree of impact of different deviation amplitudes on the treatment path, resulting in a lack of refined management capabilities in the adjustment strategy and difficulty in achieving individualized dynamic regulation.

[0089] The present application further proposes setting a hierarchical trigger logic according to the deviation direction and amplitude of the actual comprehensive symmetry index change rate, including: when the actual comprehensive symmetry index change rate deviates positively and the amplitude is in the first threshold interval, generating an instruction to shorten the current appliance wearing period; when the actual comprehensive symmetry index change rate deviates negatively and the amplitude is in the first threshold interval, generating an instruction to extend the current appliance wearing period; when the actual comprehensive symmetry index change rate deviates positively and the amplitude is in a larger second threshold interval, generating an instruction to enter the next stage appliance replacement in advance; when the actual comprehensive symmetry index change rate deviates negatively and the amplitude is in a larger second threshold interval, generating an instruction to delay entering the next stage appliance replacement.

[0090] Among them, the first threshold interval is defined as the absolute value of the change rate deviation within the range of 5%-15%, and the second threshold interval is defined as the absolute value of the deviation exceeding 15%; the shortening wearing cycle instruction corresponds to reducing the current braces usage cycle by 3-5 days, and the extension instruction corresponds to increasing it by 5-7 days; after the early replacement instruction is triggered, it jumps to the next stage of the braces application plan, and after the delayed replacement instruction is triggered, a 1-2 week observation period is inserted.

[0091] Among them, the mapping relationship between different threshold intervals and adjustment amounts is obtained through clinical data training to ensure that the adjustment amount matches the biomechanical response characteristics.

[0092] Specifically, when it is detected that the rate of change of the comprehensive symmetry index deviates from the target trend, the system first calculates the absolute value of the deviation between the actual rate of change and the preset value, and determines the threshold interval it falls into. When the deviation is in the first threshold interval, only the wearing time of the orthodontic appliance in the current stage is adjusted, and the original stage division is retained; when the deviation exceeds the second threshold interval, the stage switching operation is triggered. This hierarchical logic avoids frequent modifications of the treatment path due to small fluctuations by distinguishing between mild deviations and significant deviations, while ensuring that major deviations are intervened in a timely manner. In the scenario of positive deviation, shortening the wearing period can accelerate the process of tooth movement; extending the period in the case of negative deviation provides additional time for tooth adjustment. For deviations exceeding the second threshold, the stage switching operation can effectively prevent error accumulation and achieve dynamic optimization of the treatment process through path reset.

[0093] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0094] Generating adjustment instructions includes hierarchical triggering logic:

[0095] When the actual rate of change of the comprehensive symmetry index deviates positively and its magnitude falls within the first threshold range, an instruction is generated to shorten the current appliance wearing period. For example, if the actual rate of change of the comprehensive symmetry index is 0.15 and the target rate of change is 0.1, and 0.15 falls within the first threshold range of [0.12, 0.18], the system generates an instruction to shorten the current appliance wearing period from the original 14 days to 12 days.

[0096] When the actual rate of change of the comprehensive symmetry index deviates negatively and its magnitude falls within the first threshold range, an instruction is generated to extend the current appliance wearing period. For example, if the actual rate of change of the comprehensive symmetry index is 0.05 and the target rate of change is 0.1, and 0.05 falls within the first threshold range of [0.02, 0.08], the system generates an instruction to extend the current appliance wearing period from the original 14 days to 16 days.

[0097] When the actual rate of change of the comprehensive symmetry index deviates positively from the second threshold range, a command to advance the appliance to the next stage is generated. For example, if the actual rate of change of the comprehensive symmetry index is 0.25 and the target rate of change is 0.1, and 0.25 is within the second threshold range of [0.2, 0.3], the system generates a command to advance the appliance to the next stage three days.

[0098] If the actual rate of change of the comprehensive symmetry index deviates negatively from the second threshold, a command to postpone the next appliance replacement is generated. For example, if the actual rate of change of the comprehensive symmetry index is -0.05, while the target rate of change is 0.1, and -0.05 is within the second threshold range of [-0.1, 0], the system will generate a command to postpone the next appliance replacement by 5 days.

[0099] Through the above technical solution, the present application realizes the precise control of the speed of tooth movement during the treatment process. According to the degree of deviation between the actual comprehensive symmetry index change rate and the target change rate, the system can automatically generate corresponding adjustment instructions, including shortening or extending the current braces wearing period, and advancing or delaying the replacement of the next stage braces. This hierarchical trigger logic can take corresponding adjustment measures for different degrees of deviation, avoiding the problem of over-adjustment or under-adjustment. At the same time, by setting different threshold intervals, the system can respond to various deviations more flexibly, improving the adaptability and accuracy of the treatment process. In addition, this automated adjustment mechanism reduces the need for manual intervention, improves treatment efficiency, and can better adapt to individual differences in patients, thereby optimizing the overall treatment effect.

[0100] In some of the above-mentioned solutions of this application, there is a lack of a systematic parameter correction and change recording mechanism when dynamically adjusting the treatment pathway, resulting in the inability to timely update the comparison benchmark in subsequent stages, affecting the continuity and accuracy of treatment pathway control.

[0101] The present application further proposes updating the treatment pathway model including model parameter modification, version identification generation and closed-loop control execution.

[0102] The model parameter correction specifically includes performing at least one of the following corrections based on the treatment parameter adjustment amount: adjusting the intervals between the time nodes of subsequent treatment stages; resetting the allowable deviation range of the dental arch morphology target parameters of the corresponding stage.

[0103] Version identification generation specifically generates a data structure containing treatment pathway model change records: change time and effective stage identification; adjusted time node interval and allowable deviation range value; and association identification with the symmetry indicator time series in historical treatment data.

[0104] The closed-loop control is implemented as follows: the change records are written into the preset treatment pathway model as a comparison benchmark for the next monitoring cycle.

[0105] Among them, the model parameter correction is achieved by modifying the time interval or deviation range threshold of the subsequent stage to match the treatment plan with the actual dental arch morphology change rate; the version identification generation records the change time, effective stage, adjustment parameter value and historical data association information through the data structure to ensure the traceability of the treatment path adjustment process; the closed-loop control execution provides a dynamic benchmark for subsequent monitoring by writing the updated parameters into the model.

[0106] For example, when the treatment parameter adjustment requires shortening the time interval between subsequent stages, the model parameter correction will change the original two-week interval to ten days. The version identifier is generated to record the modification time, the effective stage number and the associated historical symmetry indicator data. The closed-loop control execution will use the updated time interval as the comparison benchmark for the next stage.

[0107] Specifically, after obtaining the treatment parameter adjustment amount, the model parameter correction first makes numerical adjustments to the time node intervals or allowable deviation ranges of the subsequent stages, such as shortening the time interval from the third to the fourth stage in the original plan from four weeks to three weeks; the version identification generation then creates a data structure containing the change effective time, the adjusted interval value and the corresponding stage identification, and establishes an associated index with the previous symmetry indicator time series; the closed-loop control execution integrates the adjusted parameters into the preset treatment pathway model, so that in the next monitoring cycle, the system automatically calls the updated time interval and deviation range as a comparison benchmark. When the actual comprehensive symmetry index change rate exceeds the allowable range again, the system makes a secondary adjustment based on the latest version of the model parameters to form a continuous closed-loop control. Thus, through the synergy of parameter correction, version identification and closed-loop execution, it is ensured that the treatment pathway model can dynamically adapt to the individualized treatment response and maintain precise control of the dental arch symmetry index.

[0108] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0109] Updating the treatment pathway model includes model parameter modification, version identification generation, and closed-loop control execution.

[0110] The model parameter correction specifically includes performing at least one of the following corrections based on the treatment parameter adjustment amount: adjusting the intervals between the time nodes of subsequent treatment stages; resetting the allowable deviation range of the dental arch morphology target parameters of the corresponding stage.

[0111] Version identification generation specifically generates a data structure containing the following treatment pathway model change records: change time and effective stage identification; adjusted time node interval and allowable deviation range value; and association identification with the symmetry indicator time series in historical treatment data.

[0112] The closed-loop control is implemented as follows: the change records are written into the preset treatment pathway model as a comparison benchmark for the next monitoring cycle.

[0113] For example, during a treatment pathway model update, the system extends the treatment duration of Stage 3 from the original 4 weeks to 5 weeks based on the treatment parameter adjustments. It also adjusts the tolerance range for the target dental arch morphology parameters for this stage from ±0.5mm to ±0.8mm. The system then generates a change record containing the change timestamp "2023-05-01 10:30:00," the effective stage identifier "Stage_3," the adjusted time interval "5 weeks," the new tolerance range "±0.8mm," and the historical treatment data time series identifier "TS_P20230501_01" associated with patient ID "P20230501." Finally, the system writes the change record to the pre-set treatment pathway model database, making it effective during the next monitoring cycle.

[0114] Through the above technical solution, the present application realizes the dynamic update and version management of the treatment pathway model. Through the correction of model parameters, the system can flexibly adjust the timing and target parameters of subsequent stages according to the actual progress of treatment, thereby improving the adaptability of the treatment process. The version identification generation mechanism ensures that each model update is accurately recorded and tracked, which is convenient for subsequent analysis and backtracking. Closed-loop control execution ensures that the updated model can be immediately applied to the next monitoring cycle, realizing the continuous optimization of the treatment process. This dynamic adjustment and closed-loop feedback mechanism significantly improves the accuracy and efficiency of oral orthodontic treatment, and effectively solves the problem that traditional static treatment plans are difficult to cope with individual differences and nonlinear changes.

[0115] In some of the above-mentioned solutions of this application, in the process of dynamically adjusting the treatment pathway model, there is a lack of continuous tracking and adaptive learning mechanism of individual patient response rates, resulting in an inability to effectively cope with nonlinear changes and cumulative effects of individual differences in long-term treatment.

[0116] The present application further proposes recording the gradient of change of the comprehensive symmetry index over time after executing the adjustment instruction to generate individual response rate data; when the deviation between the response rate and the target comprehensive symmetry index change rate in the preset treatment pathway model exceeds the learning threshold, the following operations are performed: extending or shortening the interval between the time nodes of subsequent treatment stages in the preset treatment pathway model; adjusting the allowable deviation range of the comprehensive symmetry index of the corresponding stage in the preset treatment pathway model.

[0117] Among them, individual response rate data are obtained by calculating the change gradient of the comprehensive symmetry index at consecutive time nodes. In specific implementation, linear regression or sliding average algorithm can be used to extract the change trend; the learning threshold is set to the range of 10%-30% of the target change rate in the preset treatment pathway model, and this numerical range is determined by statistical analysis of clinical data; when adjusting the time node interval of subsequent stages, the interval adjustment amplitude is linearly mapped according to the deviation ratio. For example, when the deviation exceeds 20%, the next stage interval will be shortened or extended by 5 days; the allowable deviation range adjustment adopts a gradient scaling mechanism. When the deviation exceeds 15%, the original allowable deviation range will be expanded or reduced by 15%.

[0118] Specifically, after completing the update of the treatment parameter adjustment amount, the system continuously monitors the time gradient of the comprehensive symmetry index, and generates an individual response rate curve by calculating the average rate of change of the index in adjacent treatment stages. This curve is dynamically compared with the target rate curve in the preset path model. When the rate difference between the two exceeds the set learning threshold, the adaptive adjustment mechanism is triggered.

[0119] For example, when the patient's tooth movement rate continues to be higher than expected, the system automatically shortens the time interval of the next correction stage and narrows the allowable deviation range to match the accelerated movement trend; otherwise, the interval is extended and the allowable range is relaxed. This process establishes a dynamic mapping relationship between the response rate and the treatment parameters to achieve adaptive optimization of the treatment path model, thereby overcoming the error accumulation problem caused by individual differences.

[0120] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0121] Record the gradient of the change of the comprehensive symmetry index over time after the execution of the adjustment instruction, generate individual response rate data, and when the deviation between the response rate and the change rate of the target comprehensive symmetry index in the preset treatment pathway model exceeds the learning threshold, perform the following operations: extend or shorten the interval between the time nodes of the subsequent treatment stages in the preset treatment pathway model; adjust the allowable deviation range of the comprehensive symmetry index of the corresponding stage in the preset treatment pathway model.

[0122] Specifically, after executing adjustment commands, the system continuously monitors changes in the patient's comprehensive symmetry index. For example, it collects oral 3D scan data weekly, calculates the comprehensive symmetry index, and records its gradient. By repeatedly measuring this data, it then fits an individual response rate curve.

[0123] Furthermore, the system compares the actual measured individual response rate with the target change rate in the preset treatment pathway model. If the deviation exceeds a preset learning threshold, for example, the average deviation of three consecutive measurements exceeds 20%, the model adaptive adjustment mechanism is triggered.

[0124] Based on the direction and magnitude of the deviation, the system adjusts the pre-set treatment pathway model accordingly. For example, if the actual response rate is faster than expected, the interval between subsequent treatment phases will be shortened; otherwise, the interval will be extended. Furthermore, the tolerance for the overall symmetry index will be adjusted based on the actual level of fluctuation to better accommodate individual differences.

[0125] Through the above technical solutions, this application realizes the dynamic optimization and personalized adjustment of the treatment pathway model. By continuously monitoring the actual response of patients, the system can timely capture individual differences and make corresponding parameter adjustments. This adaptive learning mechanism improves the accuracy and efficiency of the treatment process, reduces the treatment deviation caused by individual differences, thereby effectively shortening the overall treatment cycle and improving the patient's treatment experience and satisfaction. At the same time, this closed-loop feedback mechanism also provides doctors with more personalized treatment reference data, which helps to further optimize the treatment plan.

[0126] Exemplary Systems

[0127] Figure 4 The diagram illustrates a digital oral dental treatment precision monitoring and analysis system according to an embodiment of the present application, comprising: a first data acquisition module for receiving oral 3D scan data of a current treatment phase; a second data acquisition module for acquiring a standardized dental arch feature point dataset of a historical treatment phase; a data processing module for extracting the dental arch feature point dataset from the oral 3D scan data of the current treatment phase and spatially aligning it with the standardized dental arch feature point dataset; a difference parameter calculation module for calculating, based on the aligned feature point dataset, a plurality of geometric difference parameters reflecting dental arch symmetry; a comprehensive symmetry index generation module for generating a comprehensive symmetry index based on the geometric difference parameters and recording it as time series data; an adjustment instruction generation module for comparing the changing trend of the comprehensive symmetry index in the time series data with the changing trend of the target comprehensive symmetry index of the corresponding phase in a preset treatment pathway model, and determining whether to generate an adjustment instruction based on the comparison result; a treatment parameter adjustment amount acquisition module for calculating, based on the adjustment instruction, a treatment parameter adjustment amount, including a time interval adjustment and an allowable deviation range adjustment, using a mapping table with preset rules or a pre-trained machine learning model; and a feedback adjustment module for updating the treatment pathway model based on the treatment parameter adjustment amount to maintain the comprehensive symmetry index within the treatment pathway model range.

[0128] In one example, the data processing module extracts a dental arch feature point dataset from the oral three-dimensional scanning data of the current treatment stage, including: filtering the tooth table surface point cloud data based on a preset accuracy threshold, retaining key points whose coordinate fluctuations are less than the preset accuracy threshold; according to a preset anatomical feature template, identifying the midpoint of the central incisor, the vertices of the bilateral canines, and the vertices of the first molar from the key points as the dental arch feature point dataset.

[0129] In one example, the difference parameters calculated by the difference parameter calculation module include: the absolute difference in left and right arch lengths; the relative difference in arch width between the apexes of the bilateral canines; the difference in arch height from the midpoint of the central incisor to the line connecting the bilateral first molars; and the area ratio of the bilateral dental arch projection areas.

[0130] In one example, the comprehensive symmetry index generation module generates a comprehensive symmetry index including: normalizing the absolute difference, relative difference and arch height difference, and outputting normalized parameters; taking the area ratio as the weight, performing weighted summation on the normalized parameters, and generating a comprehensive symmetry index.

[0131] In one example, the adjustment instruction generation module compares and generates adjustment instructions, including: applying sliding window analysis to the comprehensive symmetry index data in the time series to calculate the actual comprehensive symmetry index change rate within the window period; comparing the actual comprehensive symmetry index change rate with the target comprehensive symmetry index change rate of the corresponding stage in the preset treatment pathway model; when the actual comprehensive symmetry index change rate continues to exceed the allowable deviation range of the target comprehensive symmetry index change rate, generating an adjustment instruction based on the deviation direction and amplitude.

[0132] In one example, generating an adjustment instruction includes a hierarchical trigger logic: when the actual comprehensive symmetry index change rate deviates positively and the amplitude is within a first threshold interval, an instruction to shorten the current appliance wearing period is generated; when the actual comprehensive symmetry index change rate deviates negatively and the amplitude is within the first threshold interval, an instruction to extend the current appliance wearing period is generated; when the actual comprehensive symmetry index change rate deviates positively and the amplitude is within a larger second threshold interval, an instruction to advance to the next stage appliance replacement is generated; when the actual comprehensive symmetry index change rate deviates negatively and the amplitude is within a larger second threshold interval, an instruction to delay entering the next stage appliance replacement is generated.

[0133] In one example, the feedback adjustment module updates the treatment pathway model, including model parameter correction, version identification generation, and closed-loop control execution; the model parameter correction is specifically to perform at least one of the following corrections based on the treatment parameter adjustment amount: adjusting the intervals between the time nodes of subsequent treatment stages; resetting the allowable deviation range of the dental arch morphology target parameters of the corresponding stages; the version identification generation is specifically to generate a data structure containing the following treatment pathway model change records: change time and effective stage identification; adjusted time node interval and allowable deviation range value; and association identification with the symmetry indicator time series in the historical treatment data; the closed-loop control execution is specifically to write the change record into the preset treatment pathway model as a comparison benchmark for the next monitoring cycle.

[0134] Exemplary electronic devices

[0135] Below, reference Figure 5 The electronic device according to an embodiment of the present application is described below. The electronic device may be the mobile device itself, or a standalone device independent of the mobile device, which can communicate with the mobile device to receive collected input signals from the mobile device and send the selected target driving behavior to the mobile device.

[0136] Figure 5 The figure shows a block diagram of an electronic device according to an embodiment of the present application.

[0137] like Figure 5 As shown, the electronic device includes one or more processors and memory.

[0138] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0139] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the driving behavior decision-making method of each embodiment of the present application described above and / or other desired functions.

[0140] In one example, the electronic device may further include an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0141] Of course, to simplify, Figure 5 Only some of the components in the electronic device related to the present application are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0142] Exemplary computer storage media

[0143] The embodiment of the present application may also be a computer-readable storage medium having stored thereon a computer program indicating

[0144] The computer program instructions, when executed by a processor, cause the processor to execute the steps of the driving behavior decision-making method according to various embodiments of the present application described in the above “Exemplary Method” section of this specification.

[0145] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0146] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0147] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0148] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0149] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0150] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A digital oral dental treatment precision monitoring and analysis method, characterized in that: include: Receive oral 3D scanning data of the current treatment stage; Obtain a standardized dental arch feature point dataset during the historical treatment phase; Extracting a dental arch feature point dataset from the oral three-dimensional scanning data of the current treatment stage, and performing spatial alignment processing with the standardized dental arch feature point dataset; Based on the aligned feature point dataset, multiple geometric difference parameters reflecting the symmetry of the dental arch are calculated; generating a comprehensive symmetry index according to the geometric difference parameters and recording the index as time series data; Comparing the changing trend of the comprehensive symmetry index in the time series data with the changing trend of the target comprehensive symmetry index of the corresponding stage in the preset treatment pathway model, and determining whether to generate an adjustment instruction based on the comparison result; According to the adjustment instruction, the treatment parameter adjustment amount including the time interval adjustment and the allowable deviation range adjustment is calculated through a mapping table of preset rules or a pre-trained machine learning model; updating the treatment pathway model according to the treatment parameter adjustment amount to maintain the comprehensive symmetry index within the treatment pathway model range; Among them, the preset treatment pathway model includes the target comprehensive symmetry index, time node information and allowable deviation range preset for each treatment stage, which is used as a dynamic benchmark for comparing the trend of the comprehensive symmetry index during the actual treatment process.

2. The digital oral dental treatment precision monitoring and analysis method according to claim 1, characterized in that: The extraction of a dental arch feature point dataset from the oral three-dimensional scanning data of the current treatment stage includes: Filter the tooth surface point cloud data based on a preset accuracy threshold, and retain key points whose coordinate fluctuation is less than the preset accuracy threshold; According to the preset anatomical feature template, the midpoint of the central incisor, the apex of the bilateral canines and the apex of the first molar are identified from the key points as a dental arch feature point dataset.

3. The digital oral dental treatment precision monitoring and analysis method according to claim 1, characterized in that: The geometric difference parameters include: The absolute difference in left and right arch lengths; The relative difference in arch width between bilateral canine apexes; The difference in arch height between the midpoint of the central incisor and the line connecting the bilateral first molars; The area ratio of the bilateral dental arch projection areas; The generating of the comprehensive symmetry index comprises: Normalizing the absolute difference, relative difference and arch height difference, and outputting normalized parameters; The area ratio is used as a weight to perform weighted summation on the normalized parameters to generate a comprehensive symmetry index.

4. The digital oral dental treatment precision monitoring and analysis method according to claim 1, characterized in that: The comparison and generation of adjustment instructions include: Applying sliding window analysis to the comprehensive symmetry index data in the time series to calculate the actual rate of change of the comprehensive symmetry index during the window period; Comparing the actual comprehensive symmetry index change rate with the target comprehensive symmetry index change rate of the corresponding stage in the preset treatment pathway model; When the actual comprehensive symmetry index change rate continues to exceed the allowable deviation range of the target comprehensive symmetry index change rate, an adjustment instruction is generated according to the deviation direction and amplitude.

5. The digital oral dental treatment precision monitoring and analysis method according to claim 4, characterized in that: The generating adjustment instruction includes a hierarchical trigger logic: When the actual comprehensive symmetry index change rate deviates in a positive direction and the amplitude is within a first threshold range, generating an instruction to shorten the current appliance wearing period; When the actual comprehensive symmetry index change rate deviates in a negative direction and the amplitude is within a first threshold range, generating an instruction to extend the current appliance wearing period; When the positive deviation amplitude of the actual comprehensive symmetry index change rate is within a larger second threshold range, an instruction to advance to the next stage of appliance replacement is generated; When the negative deviation amplitude of the actual comprehensive symmetry index change rate is within a larger second threshold range, an instruction to delay entering the next stage of appliance replacement is generated.

6. The digital oral and dental treatment precision monitoring and analysis method according to claim 1, characterized in that: Said updating of said treatment pathway model includes model parameter modification, version identification generation and closed-loop control execution; The model parameter modification specifically includes performing at least one of the following modifications according to the treatment parameter adjustment amount: Adjust the intervals between the time points of subsequent treatment phases; Reset the allowable deviation range of the dental arch morphology target parameters for the corresponding stage; The version identification generation specifically generates a data structure including the following treatment pathway model change record: Change time and effective stage identification; The adjusted time node interval and allowable deviation range values; Correlation identification with the time series of symmetry indicators in historical treatment data; The closed-loop control is specifically performed as follows: The change record is written into the preset treatment pathway model as a comparison benchmark for the next monitoring cycle.

7. The digital oral dental treatment precision monitoring and analysis method according to claim 1, characterized in that: Also includes: Record the gradient of the comprehensive symmetry index over time after executing the adjustment instruction to generate individual response rate data; When the deviation between the response rate and the target comprehensive symmetry index change rate in the preset treatment pathway model exceeds a learning threshold, the following operations are performed: Extending or shortening the intervals between time nodes of subsequent treatment stages in the preset treatment pathway model; Adjust the allowable deviation range of the comprehensive symmetry index of the corresponding stage in the preset treatment pathway model.

8. A digital oral and dental treatment precision monitoring and analysis system, applying the method according to any one of claims 1 to 7, characterized in that: include: A first data acquisition module is used to receive oral three-dimensional scanning data of the current treatment stage; The second data acquisition module is used to obtain a standardized dental arch feature point dataset of the historical treatment stage; a data processing module, configured to extract a dental arch feature point dataset from the oral three-dimensional scanning data of the current treatment stage, and perform spatial alignment processing with the standardized dental arch feature point dataset; A difference parameter calculation module is used to calculate multiple geometric difference parameters reflecting the symmetry of the dental arch based on the aligned feature point data set; a comprehensive symmetry index generating module, configured to generate a comprehensive symmetry index according to the geometric difference parameters and record the index as time series data; an adjustment instruction generating module, configured to compare the changing trend of the comprehensive symmetry index in the time series data with the changing trend of the target comprehensive symmetry index of the corresponding stage in the preset treatment pathway model, and determine whether to generate an adjustment instruction based on the comparison result; a treatment parameter adjustment amount acquisition module, configured to calculate, according to the adjustment instruction, the treatment parameter adjustment amount including time interval adjustment and allowable deviation range adjustment through a mapping table with preset rules or a pre-trained machine learning model; A feedback adjustment module is used to update the treatment pathway model according to the treatment parameter adjustment amount to maintain the comprehensive symmetry index within the treatment pathway model range.

9. An electronic device comprising a memory and a processor, characterized in that : The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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