Dental orthodontic front bone health risk assessment system, electronic equipment and storage medium

Abstract

The invention discloses a dental orthodontic front bone health risk assessment system, electronic equipment and a storage medium, and the system comprises a data acquisition module which is used for acquiring two-dimensional or three-dimensional image data of the oral cavity of a patient; the image processing module is used for processing the acquired image data and extracting target parameters of bone tissues around teeth and / or teeth related to orthodontics; and the risk assessment module is used for comprehensively assessing the bone health risk before orthodontic treatment based on the target parameters in combination with clinical information of the patient. According to the technical scheme, the bone health risk before orthodontic treatment can be comprehensively and accurately evaluated by comprehensively considering factors such as bone volume, bone windowing and bone splitting possibility of bone tissues after orthodontic stress and relative position relation of bones and teeth, so that a doctor is helped to formulate a more scientific and reasonable treatment scheme before orthodontic treatment; the treatment risk is reduced and the treatment effect is improved.

Description

Technical Field

[0001] This invention relates to the field of medical technology, specifically to a pre-orthodontic bone health risk assessment system, electronic device, and storage medium. Background Technology

[0002] Orthodontic surgery is a treatment method for correcting teeth, primarily focusing on adjusting the position and occlusion of teeth to improve tooth alignment and bite function. This surgery aims to address problems such as crowding, misalignment, gaps, protrusion, or reverse bite. Although orthodontic surgery generally does not directly manipulate the upper and lower jawbones, the condition of the upper and lower jawbones directly affects the success rate of orthodontic treatment and the post-treatment outcome.

[0003] In orthodontic treatment, assessing the health of the patient's bone tissues, including the upper and lower jaws, is crucial. Current bone analysis methods are largely based on traditional experience, failing to comprehensively and accurately reflect the actual condition of the bones, thus increasing treatment risks. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a pre-orthodontic bone health risk assessment system, electronic device and storage medium that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a pre-orthodontic bone health risk assessment system is provided, the system comprising:

[0006] The data acquisition module is used to acquire two-dimensional or three-dimensional image data of the patient's oral cavity;

[0007] The image processing module is used to process the acquired image data and extract target parameters of the bone tissue around the teeth and / or the teeth related to orthodontics.

[0008] The risk assessment module is used to comprehensively assess bone health risks before orthodontic treatment based on the target parameters and the patient's clinical information.

[0009] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0010] In some implementations, the image processing module is used for:

[0011] The maxilla and / or mandible are segmented from three-dimensional image data based on a region growing algorithm or a first neural network recognition model. The thickness value of the maxilla or mandible is calculated, the volume is calculated based on the thickness value and the range of the region, and the health status of the maxilla or mandible is assessed based on the thickness value and the volume.

[0012] In some implementations, the image processing module is also used for:

[0013] The maxilla and / or mandible are segmented from the image data based on a region growing algorithm or a first neural network recognition model, and the size of the bone fenestration or bone fissure on the maxilla or mandible is identified using a second neural network recognition model.

[0014] In some embodiments, identifying the size of bone fenestrations or fractures on the maxilla or mandible using a second neural network recognition model includes:

[0015] Based on the regions and categories identified by the YOLOv5 neural network recognition model, the bone health risk level is determined according to the proportion and category of the area with bone fenestration or bone fracture to the whole bone.

[0016] In some implementations, segmenting the maxilla and / or mandible from image data based on a region growing algorithm includes:

[0017] Edge detection algorithms are used to perform edge detection on the image data to identify the edge contours of bone tissue;

[0018] Based on edge detection, starting from a seed point, neighboring pixels with similar attributes to the seed point are gradually merged into regions.

[0019] The region is subjected to erosion, expansion, opening and / or closing operations to eliminate noise, fill voids or smooth edges.

[0020] The region is identified and classified using feature extraction and classification algorithms to distinguish between the maxilla, mandible, or alveolar bone.

[0021] In some implementations, the image processing module is also used for:

[0022] The maxilla and / or mandible are segmented from image data based on a region growing algorithm or a first neural network recognition model. The relative positional relationship between the maxilla or mandible and the teeth is analyzed, and the impact of tooth movement on the bone structure and the support capacity of the bone structure for tooth movement are evaluated.

[0023] In some implementations, the risk assessment module is further used for:

[0024] A comprehensive risk assessment is conducted by combining the patient's clinical information, target parameters, and occlusion analysis results using a pre-set risk assessment model.

[0025] Based on the results of the comprehensive risk assessment, patients are classified into different risk levels;

[0026] The results and risk levels will be output in the form of a report.

[0027] In some embodiments, the system further includes:

[0028] An interaction module is used to enable interaction between patients or doctors and the system, including data input, result viewing, intelligent treatment suggestions and / or user feedback;

[0029] The data module is used to store and manage the data in the system, including patient clinical information, imaging data, comprehensive risk assessment results and risk levels, and to save and retrieve the data, providing data support for subsequent scientific research and clinical applications.

[0030] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the following operations:

[0031] Acquire two-dimensional or three-dimensional imaging data of the patient's oral cavity;

[0032] The acquired image data is processed to extract target parameters of the bone tissue surrounding the teeth or the teeth related to orthodontics.

[0033] Based on the target parameters and combined with the patient's clinical information, a comprehensive assessment of bone health risk is conducted before orthodontic treatment.

[0034] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0035] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores one or more programs, which, when executed by a processor, perform the following steps:

[0036] Acquire two-dimensional or three-dimensional imaging data of the patient's oral cavity;

[0037] The acquired image data is processed to extract target parameters of the bone tissue surrounding the teeth or the teeth related to orthodontics.

[0038] Based on the target parameters and combined with the patient's clinical information, a comprehensive assessment of bone health risk is conducted before orthodontic treatment.

[0039] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0040] The technical effects that can be obtained by the embodiments of the present invention are as follows:

[0041] By comprehensively considering factors such as bone volume, the possibility of bone fenestration and bone splitting after orthodontic stress, and the relative positional relationship between bone and teeth, this invention can comprehensively and accurately assess bone health risks before orthodontic treatment. This helps doctors develop more scientific and reasonable treatment plans before orthodontic treatment, reduce treatment risks, and improve treatment outcomes.

[0042] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0044] Figure 1 A schematic diagram of a pre-orthodontic bone health risk assessment system according to an embodiment of the present invention is shown.

[0045] Figure 2 A schematic diagram of the workflow of a pre-orthodontic bone health risk assessment system according to an embodiment of the present invention is shown.

[0046] Figure 3 A schematic diagram illustrating the working principle of bone tissue segmentation according to an embodiment of the present invention is shown;

[0047] Figure 4 A schematic diagram showing the state of alveolar bone thickness detection results according to an embodiment of the present invention is shown;

[0048] Figure 5 A schematic diagram showing the state of bone tissue on the outer surface of a tooth according to an embodiment of the present invention is shown;

[0049] Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0050] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0051] Figure 1 A pre-orthodontic bone health risk assessment system 100 according to an embodiment of the present invention is shown, the system 100 comprising:

[0052] The data acquisition module 110 is used to acquire two-dimensional or three-dimensional image data of the patient's oral cavity;

[0053] The image processing module 120 is used to process the acquired image data and extract the target parameters of the bone tissue around the teeth and the teeth related to orthodontics. It should be noted that it can also perform preprocessing operations such as cleaning, standardization and normalization on the received image data to ensure the consistency and accuracy of the data.

[0054] Risk assessment module 130 is used to comprehensively assess bone health risks before orthodontic treatment based on the target parameters and the patient's clinical information.

[0055] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0056] Therefore, according to the above embodiments, by comprehensively considering factors such as bone volume, the possibility of bone fenestration and bone splitting after orthodontic force, and the relative positional relationship between bone and teeth, it is possible to comprehensively and accurately assess bone health risks before orthodontic treatment. This helps doctors to develop more scientific and reasonable treatment plans before orthodontic treatment, reduce treatment risks, and improve treatment outcomes.

[0057] In some embodiments, the image processing module 120 is used for:

[0058] The maxilla and / or mandible are segmented from three-dimensional image data based on a region growing algorithm or a first neural network recognition model. The thickness value of the maxilla or mandible is calculated, the volume is calculated based on the thickness value and the range of the region, and the health status of the maxilla or mandible is assessed based on the thickness value and the volume.

[0059] In some embodiments, the image processing module 120 is further configured to:

[0060] The maxilla and / or mandible are segmented from the image data based on a region growing algorithm or a first neural network recognition model, and the size of the bone fenestration or bone fissure on the maxilla or mandible is identified using a second neural network recognition model.

[0061] The first neural network model can be selected as the U-net network model.

[0062] In some embodiments, identifying the size of a bone fenestration or bone fracture on the maxilla or mandible using a second neural network recognition model includes:

[0063] The YOLOv5 neural network recognition model identifies the level of bone fenestration or bone fracture.

[0064] The second neural network model can be any YOLOv5 neural network recognition model, comprising, from top to bottom, a feature backbone network, a feature fusion module, and a detection head. Optionally, the feature backbone network includes an EfficientNet module and a Squeeze-and-Excitation module, with the output of the EfficientNet module input into the Squeeze-and-Excitation module. The feature fusion module includes a BiFPN module, and the detection head includes the softer NMS algorithm. The EfficientNet module structure includes an input layer, convolutional layer, weakly connected layer, dilated convolutional layer, regularization layer, pooling layer, and fully connected layer. The Squeeze-and-Excitation module is an attention mechanism module used to improve the performance of convolutional neural networks, including a compression module, an activation module, and a scaling module. The compression module includes a global pooling operation, and the activation module includes two fully connected layers, a ReLU function, and a sigmoid function. The BiFPN module includes upsamplers and downsamplers, which can progressively reduce and increase the resolution of feature maps. BiFPN constructs a top-down and bottom-up feature fusion network by introducing bidirectional paths. Through bidirectional connections and feature fusion operations, BiFPN addresses the feature information loss and redundancy issues that FPN may encounter in object detection tasks. In the detection head, the softer NMS algorithm is introduced to reduce conflicts between overlapping bounding boxes, enabling post-processing optimization.

[0065] In summary, the YOLOv5 neural network recognition model described above can accurately identify the specific area and category of bone fenestration or bone fracture after labeling and training, and determine the area size and category, thus facilitating its use in the final bone health risk level assessment.

[0066] Furthermore, in some embodiments, combined with Figure 3 As shown, the bone segmentation and recognition of image data processing specifically includes the following steps:

[0067] 1. Data preprocessing:

[0068] The input medical image data first undergoes a data preprocessing stage, including operations such as denoising, enhancement, and normalization, to improve image quality and contrast, facilitating subsequent skeletal structure recognition.

[0069] 2. Edge detection:

[0070] Edge detection algorithms (such as Canny and Sobel) are used to detect edges in preprocessed images to identify the edge contours of skeletal structures. These algorithms detect edges by calculating gradient changes in pixels within the image, thus highlighting the boundary information of the skeletal structure.

[0071] 3. Regional growth:

[0072] Building upon edge detection, a region growing algorithm is used for further segmentation of the skeletal structure. Starting from a seed point, the region growing algorithm gradually merges adjacent pixels with similar attributes into a single region, thereby achieving complete extraction of the skeletal structure.

[0073] 4. Morphological manipulation:

[0074] Morphological operations, including erosion, dilation, opening, and closing operations, are used for post-processing of the grown bone structure to eliminate noise, fill voids, and smooth edges. These operations can further improve the accuracy and integrity of bone structure segmentation.

[0075] 5. Skeletal structure recognition:

[0076] After morphological operations, a series of skeletal structural regions are obtained. At this point, feature extraction and classification algorithms (such as machine learning, deep learning, etc.) are needed to identify and classify these regions to distinguish different skeletal parts (such as the maxilla, mandible, alveolar bone, etc.).

[0077] 6. Skeletal parameter extraction:

[0078] After identifying different skeletal parts, the bone segmentation submodule further extracts parameter information of these skeletal parts, such as area, volume, density, thickness, three-dimensional morphology, and relevant bone tissue parameters calculated from the segmented bone tissue (such as trabecular bone density, scaffold characteristics, etc.). This parameter information will serve as an important basis for subsequent risk assessment.

[0079] 7. Output and Feedback:

[0080] Finally, the bone segmentation submodule outputs the extracted skeletal structure and parameter information to the risk assessment module and receives feedback from it. Based on the feedback, the bone segmentation submodule can optimize and adjust its algorithm and parameters to improve the accuracy and stability of skeletal structure segmentation.

[0081] Therefore, in some embodiments, segmenting the maxilla and / or mandible from image data based on a region growing algorithm may include:

[0082] Edge detection algorithms are used to perform edge detection on the image data to identify the edge contours of bone tissue;

[0083] Based on edge detection, starting from a seed point, neighboring pixels with similar attributes to the seed point are gradually merged into regions.

[0084] The region is subjected to erosion, expansion, opening and / or closing operations to eliminate noise, fill voids or smooth edges.

[0085] The region is identified and classified using feature extraction and classification algorithms to distinguish between the maxilla, mandible, or alveolar bone.

[0086] The specific feature extraction and classification algorithms include the following steps: Feature Selection: Choose a suitable feature extraction algorithm based on the specific task and application scenario. Commonly used feature extraction algorithms include SIFT (Scale Invariant Feature Transform), SURF (Speed-Up Robust Feature Transform), and HOG (Histogram of Oriented Gradients). These algorithms can capture key information in the image, such as local texture and shape contours. Feature Region Localization: After selecting the feature extraction algorithm, specific regions for feature extraction need to be located in the image. This can be achieved through sliding windows, region selection algorithms, or attention mechanisms in deep learning networks. The selection of feature regions is crucial to the effectiveness of feature extraction, determining which information will be used for subsequent analysis and judgment. Feature Calculation and Encoding: After selecting the feature regions, use the selected feature extraction algorithm to calculate the features of those regions. These features may include color histograms, gradient orientation histograms, texture descriptors, etc. Then, these features are encoded into numerical representations that can be processed by a computer, such as feature vectors or feature descriptors. Feature Selection and Optimization: Select and optimize the extracted features according to the needs of the specific task. This can be achieved through feature dimensionality reduction, feature fusion, and feature selection algorithms. The purpose of feature selection and optimization is to remove redundant information, improve the effectiveness and robustness of features, and thus enhance the performance of subsequent risk assessment or other image analysis tasks. These features are mapped one-to-one with bone tissue in the image data, and the features are stored in a structured or unstructured manner.

[0087] Key features in the above analysis include the thickness of bone tissue relative to each tooth, such as... Figure 4 As shown. It also includes a mechanical analysis demonstration diagram, which analyzes the changes in bone tissue and teeth after being subjected to force. For example, if the mechanical analysis process shows instability around the tooth root exceeding a certain force range, or if the tooth root deviates from the outer surface of the buccal bone or the inner surface of the lingual bone by more than 1 mm or a limit value, it is considered to be of high risk.

[0088] In some embodiments, the image processing module 120 is further configured to:

[0089] The maxilla and / or mandible are segmented from image data based on a region growing algorithm or a first neural network recognition model. The relative positional relationship between the maxilla or mandible and the teeth is analyzed, and the impact of tooth movement on the bone structure and the support capacity of the bone structure for tooth movement are evaluated.

[0090] In some embodiments, the risk assessment module 130 is further configured to:

[0091] A comprehensive risk assessment is conducted by combining the patient's clinical information, target parameters, and occlusion analysis results using a pre-set risk assessment model.

[0092] Based on the results of the comprehensive risk assessment, patients are classified into different risk levels;

[0093] The results and risk levels will be output in the form of a report. The risk assessment results will be output in report form, including the risk level, potential risk points, treatment recommendations, etc. The report will serve as a reference for doctors in developing treatment plans, and is also important information for patients to understand their condition and treatment risks.

[0094] The occlusion analysis room uses patients' oral examination records and medical imaging data to simulate the patient's occlusion process, analyze the distribution and changes of occlusal force, and assess the impact of occlusal abnormalities on bone health.

[0095] The preset risk assessment models include decision tree-based models and Bayesian network-based models.

[0096] It should be noted that the comprehensive assessment requires combining the patient's personal information, bone health assessment results, and occlusion analysis results, and employing a risk assessment algorithm for a holistic risk evaluation. The algorithm will consider multiple factors, such as the patient's age, gender, oral health status, and treatment history, to comprehensively assess the risks of orthodontic treatment.

[0097] For example, in the risk assessment module, the risk level classification for assessing bone health risks is divided into five levels, from level 1 (minor risk) to level 5 (very high risk). The following is a possible classification method:

[0098] Level 1 (Minor Risk):

[0099] The teeth have moved slightly or are loose.

[0100] There may be small areas of bone cracking, but it will not affect the stability of the teeth.

[0101] Emergency treatment is usually not required, but monitoring is recommended.

[0102] Level 2 (Low Risk):

[0103] One or two teeth have shown noticeable movement.

[0104] The bone fracture area is slightly large, but the teeth remain relatively stable.

[0105] Some treatment may be necessary to prevent the condition from worsening.

[0106] Level 3 (Medium Risk):

[0107] Multiple teeth (e.g., 3-4) may shift or become loose.

[0108] The large area of ​​bone fracture threatens the stability of the teeth.

[0109] Active treatment is needed to prevent further tooth loss.

[0110] Level 4 (High Risk):

[0111] Most teeth (e.g., 5-6) are severely affected, and tooth movement is obvious.

[0112] Extensive bone fractures significantly reduce tooth stability.

[0113] Emergency treatment is crucial to preserve as many teeth as possible.

[0114] Level 5 (Extremely High Risk):

[0115] Almost all front teeth (such as 6 or more) are severely affected.

[0116] Tooth movement is so extreme that it may fall out with the slightest touch.

[0117] The bone fracture area was very large, and the supporting structure of the teeth was severely damaged.

[0118] Immediate emergency measures, such as stabilizing the tooth or performing surgery, are needed to prevent tooth loss and further damage.

[0119] In some embodiments, the system further includes:

[0120] An interaction module is used to enable interaction between patients or doctors and the system, including data input, result viewing, intelligent treatment suggestions and / or user feedback;

[0121] The data module is used to store and manage the data in the system, including patient clinical information, imaging data, comprehensive risk assessment results and risk levels, and to save and retrieve the data, providing data support for subsequent scientific research and clinical applications.

[0122] Therefore, in a specific embodiment, the resulting overall system processing flow can be as follows: Figure 2 As shown.

[0123] In an optional embodiment, combined with Figure 5 For the high-risk cases shown, the key points for orthodontic risk assessment are as follows:

[0124] The insidious nature and severity of bone fenestration and bone fracture:

[0125] Bone fractures may not be easily detected in a simple CT scan, and as orthodontic treatment progresses, tooth roots may shift to different locations.

[0126] Bone fractures can worsen pain.

[0127] Potential effects of malocclusion:

[0128] During biting, forces are applied to the teeth and surrounding bone tissue at different locations, and these forces affect the spatial relationship between the tooth root and the surrounding bone tissue.

[0129] The importance of periodontal health:

[0130] Before orthodontic treatment, the patient's periodontal health is a key consideration.

[0131] Periodontal disease or weak periodontal tissues may increase the risks of orthodontic treatment and affect its effectiveness.

[0132] The comprehensiveness and complexity of the treatment plan:

[0133] For this patient, a comprehensive treatment plan combining periodontal surgery, orthodontic treatment, and / or orthognathic surgery may be necessary.

[0134] The complexity of treatment options increases the risks involved, requiring patients to make decisions only after fully understanding the treatment process and potential risks.

[0135] Risks and Decisions:

[0136] face Figure 5 In the complex case illustrated, the orthodontist needs to comprehensively assess the patient's oral health, the severity of the bone fracture, the impact of the malocclusion, and the patient's treatment wishes and expectations. After thorough communication and explanation of the treatment risks, expected outcomes, and alternatives, the patient can choose whether to accept treatment. For this patient, although the treatment risk is high, not undergoing treatment may lead to a greater risk of future tooth loss. Therefore, with the patient fully understanding and accepting the treatment risks, the doctor should develop a personalized treatment plan to maximize the improvement of the patient's oral health.

[0137] Alternatively, the assessment methods for bone health risk levels can be further refined as follows:

[0138] 1. Clinical parameter assessment:

[0139] Exposed area of ​​tooth root on bone surface: Based on the location and angle of the tooth distribution within the bone, assess the risk of root fenestration and cracking. The risk of bone fenestration increases with the continuous action of external forces.

[0140] Number of teeth affected: This counts the number of teeth affected or potentially affected by bone fenestration. The more teeth affected, the higher the risk of bone fenestration in the surrounding bone.

[0141] Severity of symptoms: Assess the severity of symptoms related to bone fenestration, such as loose teeth, pain, bleeding, or bone devitalization. The more severe the symptoms, the higher the risk level.

[0142] 2. Imaging assessment:

[0143] Three-dimensional images of a patient's bone structure are obtained using medical imaging equipment such as CT, MRI, or X-ray.

[0144] The analysis included parameters such as alveolar bone volume, bone density distribution, cortical thickness, and trabecular structure. Smaller bone volume, uneven bone density distribution, thinner cortical bone, or disordered trabecular structure may increase the risk of bone fenestration.

[0145] 3. Mechanical Analysis:

[0146] By combining factors such as the patient's oral structure, tooth arrangement, and occlusal relationship, we can analyze the possible mechanical changes that may occur in the teeth during occlusion.

[0147] Assess the stress and strain experienced by the alveolar bone when a tooth is subjected to external forces. Excessive stress and strain may increase the risk of bone fenestration.

[0148] 4. Risk assessment model:

[0149] A risk assessment model for bone fenestration was constructed based on clinical parameters, imaging parameters, and biomechanical analysis results.

[0150] The model can use statistical methods, machine learning algorithms, or expert systems to conduct risk assessment and classification.

[0151] By inputting relevant patient parameters, the model can automatically output the risk level of bone fenestration and corresponding treatment recommendations or preventive measures.

[0152] 5. Expert consultation:

[0153] During the evaluation process, experts in related fields such as periodontology, oral and maxillofacial surgery, and orthodontics can be invited for consultation.

[0154] Experts can review and adjust the assessment results based on their clinical experience and in-depth understanding of the patient's condition.

[0155] Expert consultation can improve the accuracy and reliability of the assessment.

[0156] 6. Follow-up and monitoring:

[0157] For patients whose assessment results indicate a medium to high risk, regular follow-up and monitoring should be conducted.

[0158] Follow-up and monitoring include clinical symptoms, imaging parameters, and mechanical analysis.

[0159] Follow-up and monitoring can help detect changes in the condition in a timely manner and allow for appropriate treatment.

[0160] 7. Risk Level Classification:

[0161] Based on the assessment results and clinical experience, the risks of bone fenestration are classified into levels from 1 (minor risk) to 5 (very high risk).

[0162] Different risk levels correspond to different treatment recommendations and preventative measures. For example, high-risk patients may require surgical interventions to reduce the risk of bone fenestration.

[0163] In summary, the orthodontic pre-orthodontic bone health risk assessment system of this invention integrates advanced image processing technology, occlusal analysis technology, and risk assessment algorithms to comprehensively and accurately assess patients' bone health risks, providing a scientific and rational basis for orthodontic treatment. Furthermore, the system also features excellent user interactivity and data storage capabilities, meeting the diverse needs of clinical applications.

[0164] It should be noted that:

[0165] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0166] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0167] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0168] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0169] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0170] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the enamel segmentation system for an independent tooth according to embodiments of the present invention. The present invention can also be implemented as a device or system program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0171] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can perform the operation steps corresponding to any of the above-described systems:

[0172] Acquire three-dimensional imaging data of the patient's oral cavity;

[0173] The acquired image data is processed to extract target parameters of the bone tissue surrounding the teeth or the teeth related to orthodontics.

[0174] Based on the target parameters and combined with the patient's clinical information, a comprehensive assessment of bone health risk is conducted before orthodontic treatment.

[0175] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0176] Figure 6 The diagram shows a structural schematic of an embodiment of the electronic device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the electronic device.

[0177] like Figure 6 As shown, the electronic device may include: a processor 602, a communications interface 604, a memory 606, and a communications bus 608.

[0178] The processor 602, communication interface 604, and memory 606 communicate with each other via communication bus 608. Communication interface 604 is used to communicate with other network elements such as clients or other servers. The processor 602 executes program 610, specifically performing the following steps in the above embodiment of the pre-orthodontic bone health risk assessment system for electronic devices:

[0179] Acquire three-dimensional imaging data of the patient's oral cavity;

[0180] The acquired image data is processed to extract target parameters of the bone tissue surrounding the teeth or the teeth related to orthodontics.

[0181] Based on the target parameters and combined with the patient's clinical information, a comprehensive assessment of bone health risk is conducted before orthodontic treatment.

[0182] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0183] Specifically, program 610 may include program code that includes computer operation instructions.

[0184] Processor 602 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The airborne image processing board includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0185] Memory 606 is used to store program 610. Memory 606 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0186] Specifically, program 610 can be used to cause processor 602 to perform the operations corresponding to the above-described embodiment of the pre-orthodontic bone health risk assessment system:

[0187] Acquire three-dimensional imaging data of the patient's oral cavity;

[0188] The acquired image data is processed to extract target parameters of the bone tissue surrounding the teeth or the teeth related to orthodontics.

[0189] Based on the target parameters and combined with the patient's clinical information, a comprehensive assessment of bone health risk is conducted before orthodontic treatment.

[0190] The target parameters include at least one of the following: bone volume, bone window size, bone fracture size, relative position of bone and tooth, thickness and mechanical characteristics of bone relative to tooth, exposed area of ​​tooth, and distance of tooth offset; the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

[0191] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. A pre-orthodontic bone health risk assessment system, comprising: a data acquisition module configured to acquire two-dimensional or three-dimensional image data of a patient's oral cavity; an image processing module configured to process the acquired image data to extract target parameters of bone tissue surrounding teeth and / or teeth related to orthodontics; a risk assessment module configured to comprehensively assess the bone health risk before orthodontic treatment based on the target parameters in combination with clinical information of the patient; wherein the target parameters comprise at least one of the following: volume of bone, size of bone fenestration, size of bone dehiscence, relative position of bone to teeth, thickness and mechanical characteristics of bone relative to teeth, exposed area of teeth, and distance of tooth displacement; and the clinical information comprises at least one of the following: age, gender, oral health status, and treatment history of the patient.

2. The system of claim 1, wherein, the image processing module is configured to: segment the maxilla and / or the mandible from the three-dimensional image data based on a region growing algorithm or a first neural network recognition model, calculate the thickness value of the maxilla or the mandible, calculate the volume based on the thickness value and the range of the region, and evaluate the health status of the maxilla or the mandible based on the thickness value and the volume.

3. The system of claim 1, wherein, the image processing module is further configured to: segment the maxilla and / or the mandible from the image data based on a region growing algorithm or a first neural network recognition model, and identify the size of bone fenestration or bone dehiscence on the maxilla or the mandible using a second neural network recognition model.

4. The system of claim 3, wherein, identifying the size of bone fenestration or bone dehiscence on the maxilla or the mandible using the second neural network recognition model comprises: identifying the region and category of bone fenestration or bone dehiscence based on a YOLOV5 neural network recognition model, and determining the bone health risk level based on the proportion of the region of bone fenestration or bone dehiscence to the whole bone and the category.

5. The system of claim 3, wherein, segmenting the maxilla and / or the mandible from the image data based on a region growing algorithm comprises: performing edge detection on the image data using an edge detection algorithm to identify the edge profile of the bone tissue; starting from a seed point, gradually merging adjacent pixel points with similar attributes to the seed point into a region; performing erosion, dilation, opening operation, and / or closing operation processing on the region to eliminate noise, fill in cavities, or smooth edges; identifying and classifying the region using a feature extraction and classification algorithm to distinguish the maxilla, the mandible, or the alveolar bone.

6. The system of claim 1, wherein, the image processing module is further configured to: segment the maxilla and / or the mandible from the image data based on a region growing algorithm or a first neural network recognition model, analyze the relative position relationship between the maxilla or the mandible and the teeth, evaluate the influence of tooth movement on the bone structure, and evaluate the support ability of the bone structure to tooth movement.

7. The system of any one of claims 1-6, wherein, the risk assessment module is further configured to: conduct comprehensive risk assessment using a preset risk assessment model in combination with the clinical information of the patient, the target parameters, and the occlusion analysis results; classify the patient into different risk levels according to the results of the comprehensive risk assessment; output the results and the risk levels in the form of a report.

8. The system of claim 7, wherein, the system further comprises: an interaction module for enabling interaction between the patient or the doctor and the system, the interaction including data input, result viewing, intelligent treatment suggestion and / or user feedback; a data module for storing and managing data in the system, the data including patient clinical information, image data, result of comprehensive risk assessment and risk level, and enabling saving and retrieval of the data to provide data support for subsequent research and clinical application.

9. An electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform operations comprising: obtaining two-dimensional or three-dimensional image data of the patient's oral cavity; processing the obtained image data to extract target parameters of bone tissue around teeth or teeth related to orthodontics; based on the target parameters, in combination with the patient's clinical information, comprehensively assessing the bone health risk before orthodontic treatment of teeth; wherein the target parameters include at least one of the following: bone volume, bone window size, bone crack size, relative position of bone and teeth, thickness and mechanical characteristics of bone relative to teeth, exposed area of teeth, and distance of tooth displacement; and the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

10. A computer-readable storage medium, characterized in that, the computer readable storage medium stores one or more programs that, when executed by the processor, implement steps comprising: obtaining two-dimensional or three-dimensional image data of the patient's oral cavity; processing the obtained image data to extract target parameters of bone tissue around teeth or teeth related to orthodontics; based on the target parameters, in combination with the patient's clinical information, comprehensively assessing the bone health risk before orthodontic treatment of teeth; wherein the target parameters include at least one of the following: bone volume, bone window size, bone crack size, relative position of bone and teeth, thickness and mechanical characteristics of bone relative to teeth, exposed area of teeth, and distance of tooth displacement; and the clinical information includes at least one of the following: patient age, gender, oral health status, and treatment history.

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