Oral root canal therapy filling quality evaluation method and system based on deep learning

Abstract

The invention belongs to the technical field of image processing. The invention provides an oral root canal therapy filling quality evaluation method and system based on deep learning. The method comprises the following steps: acquiring an X-ray dental film image of oral root canal therapy; converting the space information of the teeth into frequency domain information, storing the frequency domain information into a loss function of a first deep learning model, segmenting the teeth of the root canal treatment part in the X-ray dental film image by adopting the first deep learning model, and obtaining a root canal treatment effect classification result by adopting a second deep learning model according to a segmentation result; according to the method, the space information of the tooth is converted into the frequency domain information, and the frequency domain information is stored in the loss function, so that the segmentation model can identify the boundary information of the tooth more accurately, the segmentation precision and robustness are improved, and the automatic and accurate evaluation of the root canal therapy filling quality can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method and system for evaluating the quality of oral root canal treatment fillings based on deep learning. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Root canal treatment, as one of the main means of treating periodontal disease, plays an important role in the patient's later recovery stage. However, the quality of treatment has a huge impact on the patient's health. Insufficient filling may lead to bacterial residue and cause inflammation, while excessive filling may cause complications such as pain or tissue necrosis. Therefore, it is particularly important to accurately evaluate the root canal after treatment, which helps to promptly detect and remedy insufficient or excessive filling. However, the traditional root canal treatment result evaluation process has some challenges. First, it is highly dependent on the doctor's professional knowledge and rich experience. Second, the long-term manual evaluation of multiple X-rays is cumbersome and prone to subjective errors; in addition, the evaluation standards between different doctors may differ, resulting in inconsistent results.

[0004] In the process of evaluating root canal treatment results, the first step is to segment the teeth for detailed analysis. However, the tooth boundary areas are often unclear in X-rays, which may be caused by the shooting angle or obstruction of the tissues around the teeth. In addition, the roots may be blocked by the surrounding soft tissue or other teeth, making the structure of the root canal system difficult to observe, thereby limiting the visual information that can be obtained during the evaluation process. In this case, the doctor needs to rely on experience and imagination to infer the actual shape of the tooth root, which makes traditional manual evaluation methods face great difficulties. Summary of the Invention

[0005] In order to address the shortcomings of the existing technology, the present invention provides an oral root canal treatment filling quality assessment method based on deep learning, which converts the spatial information of the tooth into frequency domain information and stores it in the loss function, which helps the segmentation model to more accurately identify the boundary information of the tooth, thereby improving the accuracy and robustness of the segmentation, and helping to achieve automatic and accurate root canal treatment filling quality assessment.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for evaluating the quality of oral root canal treatment filling based on deep learning.

[0007] A deep learning-based method for evaluating the quality of oral root canal treatment fillings includes the following steps: Obtain X-ray images of oral root canal treatment; The spatial information of the teeth is converted into frequency domain information and stored in the loss function of the first deep learning model. The first deep learning model is used to segment the root canal treatment part of the teeth in the X-ray dental image. Based on the segmentation results, the second deep learning model is used to obtain the root canal treatment effect classification results.

[0008] As a further limitation of the first aspect of the present invention, converting the spatial information of the tooth into frequency domain information and storing it in the loss function of the first deep learning model includes: The loss function of the first deep learning model is the original loss function and The product of , where , represents the frequency domain representation of the prediction boundary a, The frequency domain representation of the manually marked boundary b is represented, and the loss function of the first deep learning model is the quantitative representation of the closed shape.

[0009] As a further limitation of the first aspect of the present invention, the first deep learning model is a symmetrical U-shaped structure, wherein the first branch of the U-shaped structure is a sequentially connected convolutional layer and four Mamba modules, and the second branch of the U-shaped structure is a sequentially connected four Mamba modules and a convolutional layer; wherein the Mamba module is composed of a VSS module and a depthwise separable convolution; Two convolutional layers are used to extract shallow features, and eight Mamba modules introduce global visual information through depthwise separable convolution and VSS modules while processing local features to segment teeth and root canal fillings.

[0010] As a further limitation of the first aspect of the present invention, the Mamba modules on the first branch are sequentially a first Mamba module, a second Mamba module, a third Mamba module, and a fourth Mamba module, and the Mamba modules on the second branch are sequentially a fifth Mamba module, a sixth Mamba module, a seventh Mamba module, and an eighth Mamba module; The first Mamba module is connected to the eighth Mamba module through a spatial attention mechanism, the second Mamba module is connected to the seventh Mamba module through a spatial attention mechanism, the third Mamba module is connected to the sixth Mamba module through a spatial attention mechanism, and the fourth Mamba module is connected to the fifth Mamba module through a spatial attention mechanism.

[0011] As a further limitation of the first aspect of the present invention, the Mamba module includes two branches. For each branch, depthwise separable convolution is used to extract shallow features, which are then passed through two VSS modules in sequence. The VSS modules capture global semantic information in the image by introducing global information and cross-channel interactions. After each branch extracts features, the features extracted by each branch are integrated and fused with the original input features to form a final feature representation. The VSS module consists of two branches. The first branch includes a linear layer, a depth-wise separable convolutional layer, a SiLU activation function, an SS2D module, and a normalization layer to extract and model the long-range spatial features of the image. The second branch includes a linear layer and a SiLU activation function to extract the local features of the image. The outputs of the two branches are multiplied to aggregate the features of the two branches, and then processed through a linear layer to obtain the fused features. The fused features are weighted summed with the original input of the VSS module to obtain the final output of the VSS module.

[0012] As a further limitation of the first aspect of the present invention, the classification results of root canal treatment effects include just-filled and under-filled. The distance between the top pixel points of the tooth and the filling is calculated. If the distance is greater than a set threshold, it is under-filled. If the distance is less than or equal to the set threshold, it is just-filled. The distance is a positive number.

[0013] In a second aspect, the present invention provides an oral root canal treatment filling quality assessment system based on deep learning.

[0014] A deep learning-based oral root canal treatment filling quality assessment system, including: An image acquisition unit is configured to: acquire an X-ray dental image of an oral root canal treatment; The evaluation and classification unit is configured to: convert the spatial information of the teeth into frequency domain information and store it in the loss function of the first deep learning model, use the first deep learning model to segment the root canal treatment part of the teeth in the X-ray dental image, and use the second deep learning model to obtain the root canal treatment effect classification result based on the segmentation result.

[0015] In a third aspect, the present invention provides a computer device comprising: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by the processor, implements the oral root canal treatment filling quality assessment method based on deep learning as described in the first aspect of the present invention.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the oral root canal treatment filling quality assessment method based on deep learning as described in the first aspect of the present invention.

[0017] In a fifth aspect, the present invention provides a computer program product, comprising a computer program. When the computer program is executed by a processor, the computer program implements the oral root canal treatment filling quality assessment method based on deep learning as described in the first aspect of the present invention.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention introduces the concept of prior knowledge loss function, converts the spatial information of teeth into frequency domain information, and stores it in the loss function, which helps the segmentation model to more accurately identify the boundary information of teeth, thereby improving the accuracy and robustness of segmentation, enabling the model to better understand the spatial relationship of tooth structure, and then effectively distinguish the boundaries between teeth and surrounding tissues.

[0019] 2. The present invention adopts two deep learning models (i.e., a first deep learning model and a second deep learning model) to effectively evaluate the quality of oral root canal treatment. It combines image segmentation and classification techniques, performs tooth segmentation in the first stage, and classifies the root canal treatment effects in the second stage. Through this two-stage design, the results of root canal treatment can be evaluated more comprehensively, improving the accuracy and reliability of the evaluation.

[0020] 3. The present invention achieves feature fusion and integration by combining the semantic information of the edge local features of the convolutional neural network with the semantic information context of the global features of the Mamba module body, so that features of different scales can better complement each other, thereby improving the model's ability to recognize complex tooth structures and enhancing the fusion effect of features of different scales.

[0021] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1 A schematic flow chart of a method for evaluating oral root canal treatment filling quality based on deep learning provided in Example 1 of the present invention; Figure 2A schematic diagram of a first deep learning model provided in Example 1 of the present invention; Figure 3 A schematic diagram of the Mamba module provided in Example 1 of the present invention; Figure 4 A schematic diagram of a VSS module provided in Example 1 of the present invention; Figure 5 This is a schematic diagram of an oral root canal treatment filling quality assessment system based on deep learning provided in Example 2 of the present invention; Figure 6 This is a schematic diagram of an electronic device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0026] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0027] Example 1: In the embodiment, a method for evaluating the quality of oral root canal treatment filling based on deep learning is proposed, such as Figure 1 As shown, the following process is included: S1: Obtain X-ray images of oral root canal treatment; S2: Convert the spatial information of the teeth into frequency domain information and store it in the loss function of the first deep learning model. Use the first deep learning model to segment the root canal treatment part of the teeth in the X-ray dental image. Use the second deep learning model to obtain the root canal treatment effect classification result based on the segmentation result.

[0028] In step S2 of this implementation method, specifically, it includes: by introducing a priori knowledge Loss function, the spatial information of the teeth can be converted into frequency domain information, and embedded into the loss function to help the segmentation model better identify the boundary information of the teeth. Specifically, the process of converting spatial information into frequency domain information can enable the model to more effectively capture the global features and local details of the tooth structure. Such a priori knowledge Loss function can not only improve the accuracy of the segmentation model for tooth boundaries, but also enhance the model's ability to understand the tooth structure, thereby improving the quality and stability of the segmentation results.

[0029] According to the prior knowledge of anatomy, tooth roots have similar shapes. In order to better guide the model to segment the tooth roots, shape information is added to the Loss function.

[0030] Assume that (xm, ym) is one of the coordinates on the boundary of the tooth root containing N pixels, and the shape of the boundary can form a complex number z(m)=xm+jym, where j is an imaginary unit. This shape can be described by the prior knowledge Loss function. Specifically, it can be used to represent the boundary shape. By using the prior knowledge Loss function, the shape information of the tooth root boundary can be converted into frequency domain information, thereby better capturing its structural features. Adding this shape information to the loss function can make the model pay more attention to the shape characteristics of the tooth root during the segmentation process, thereby improving the accuracy and stability of the segmentation. This method can effectively utilize the prior knowledge of anatomy to provide stronger guidance and constraints for the segmentation model, thereby improving the segmentation effect of complex structures such as the tooth root.

[0031] Furthermore, the formula is as follows: (1); (2); (3); in, Z(i) Represents input i The frequency domain representation of z(l) Represents a point l The complex number representation of BCE represents the original loss function based on the predicted boundary a and the true boundary b. N Represents the number of pixels, m Representative m pixels, is the input for predicting boundary a i The frequency domain representation of is the input of the true boundary b i The frequency domain representation of is the input for predicting boundary a i The frequency domain representation of the input and the real boundary b i The modulus of the difference in frequency domain representation, is the final prior knowledge loss function.

[0032] Furthermore, the prior knowledge loss function is a quantitative representation of a closed shape, which is independent of its starting point, scale, position and rotation. The frequency domain representation of the predicted boundary a and the true boundary b can be calculated according to formula (1). The shape difference between the predicted boundary a and the manually marked boundary b can be quantified according to formula (2). Formula (3) is used to add to the training process of the segmentation network.

[0033] In step S2 of this implementation, the training of the first deep learning model and the second deep learning model both require the construction of data sets and data annotation, specifically including: A certain amount of X-ray dental image data was obtained, all of which were from patients who had undergone root canal treatment, while the dental film data with extremely poor exposure and poor quality were eliminated; multiple professional dentists annotated the treated teeth and root canal fillings, and multiple doctors who did not participate in the labeling evaluated them to ensure the authenticity of the labels.

[0034] More specifically, X-ray dental image data of patients who have undergone root canal treatment are collected (the X-ray dental data here are all data used with the patient's consent and exclude patient privacy) to build a comprehensive root canal treatment evaluation dataset. These X-ray dental data will cover a variety of treatment situations and the diversity of different cases, thereby providing a rich source of information for model training and evaluation, ensuring the quality and integrity of the data. Through careful screening and analysis, the accuracy and reliability of each data sample are confirmed. Each dataset is crucial for the development and verification of the evaluation model. It will provide dentists with a powerful tool for accurately evaluating the effectiveness of root canal treatment and the patient's dental health. After a rigorous process, senior dentists will accurately annotate the teeth that have undergone root canal treatment and their fillings. This process requires careful review by multiple professional doctors to ensure the accuracy and consistency of the annotation. Each doctor will conduct a detailed review of the images and the actual situation in order to accurately capture any possible problems or potential treatment options. The annotation results will be a highly reliable and accurate dataset, providing a solid basis for subsequent treatment decisions.

[0035] like Figure 2 As shown, the first deep learning model focuses on tooth segmentation in X-ray images. This model uses deep learning algorithms to accurately separate tooth regions from complex X-ray images and precisely identify their boundaries and structures. Considering the morphological differences between teeth and patients and the potential ambiguity in X-ray images, this invention emphasizes the robustness and generalization capabilities of the model to ensure reliable segmentation.

[0036] More specifically, the first deep learning model is a symmetrical U-shaped structure, and the first branch of the U-shaped structure (i.e. Figure 2 The left branch in the figure is composed of convolutional layers and four Mamba modules connected in sequence. The second branch of the U-shaped structure (i.e. Figure 2 The right branch in the figure shows four Mamba modules and convolutional layers connected in sequence. These modules work together to efficiently capture the semantic information of global and local attention. The Mamba module is composed of a VSS module and a depth-wise separable convolution.

[0037] In this implementation, two convolutional layers are used to extract shallow features, and eight Mamba modules are responsible for introducing global visual information through depthwise separable convolution and VSS modules, while effectively processing local features to achieve accurate segmentation of teeth and root canal fillings.

[0038] In this implementation, further, the Mamba modules on the first branch are sequentially the first Mamba module, the second Mamba module, the third Mamba module, and the fourth Mamba module, and the Mamba modules on the second branch are sequentially the fifth Mamba module, the sixth Mamba module, the seventh Mamba module, and the eighth Mamba module; The first Mamba module and the eighth Mamba module are jump-connected through the spatial attention mechanism, the second Mamba module and the seventh Mamba module are jump-connected through the spatial attention mechanism, the third Mamba module and the sixth Mamba module are jump-connected through the spatial attention mechanism, and the fourth Mamba module and the fifth Mamba module are jump-connected through the spatial attention mechanism. This means that shallow spatial information and deep spatial information can be effectively exchanged. This jump connection mechanism helps to improve the model's perception of features at different levels, thereby enhancing the model's representation ability and segmentation accuracy.

[0039] In this implementation, preferably, the Mamba module, such as Figure 3 As shown, it includes two branches (to achieve multi-scale extraction of image features and modeling of long-range dependencies). For each branch, depth-separable convolution is used to extract shallow features (the first convolution of the above branch is , the last convolution of the above branch is , the first convolution of the following branch is , the first convolution of the following branch is ), this convolution operation can effectively capture local details in the image and extract features at different scales by setting symmetrical convolution kernels of different sizes. This design enables the model to have a broader perception ability when processing images, and can simultaneously focus on feature information at different scales, thereby improving the model's understanding and representation of images. Then, two VSS modules are used to model long-range dependencies. By introducing global information and cross-channel interactions, the VSS modules can better capture global semantic information in the image and effectively model long-range contextual dependencies. This design makes the model more accurate and comprehensive in understanding the overall structure and semantic information of the image. At the same time, to maintain the importance of shallow features and promote the fusion of features at different scales, skip connections are combined to maintain the effectiveness of shallow features and ensure information exchange between features at different scales, thereby enhancing the model's representation ability and segmentation accuracy. After extracting features from each branch, the features extracted by each branch are integrated and fused with the original input features to form the final feature representation. This fusion operation further improves the model's understanding and segmentation performance, providing a more reliable feature representation for subsequent classification tasks.

[0040] In this implementation, preferably, Figure 4 As shown in the figure, the VSS module consists of two branches. The first branch includes a linear layer, a depth-separable convolutional layer, a SiLU activation function, an SS2D module (i.e., 2D-Selective-Scan) and a normalization layer, which is used to extract and model the long-range spatial features of the image; the second branch includes a linear layer and a SiLU activation function, which is used to extract the local features of the image. The outputs of the two branches are multiplied to aggregate the features of the two branches, and then processed through a linear layer to obtain the fused features. The fused features are weighted and summed with the original input of the VSS module to obtain the final output of the VSS module. This design enables the VSS module to comprehensively utilize global and local features, fully explore the information in the image, and provide the model with richer and more accurate feature representation, thereby improving the performance of the model in image processing tasks.

[0041] The second deep learning model in this implementation mainly uses CNN and Vision Transformer modules as the backbone network to extract local and global features of the root canal treatment part and finally complete the classification.

[0042] In step S2 of this implementation method, the root canal treatment effect classification results, including just-filled and under-filled, are calculated. The distance between the top pixel points of the tooth and the filling is calculated. If the distance is greater than a set threshold, it is under-filled. If the distance is less than or equal to the set threshold, it is just-filled. The distance is a positive number.

[0043] More specifically, by accurately segmenting the teeth and root canal fillings, the pixel points of the root apex and the filling apex can be obtained, and the accurate distance between the root and the filling can be obtained by calculating the vertical distance between these two pixel points. This process is crucial for evaluating the effectiveness of root canal treatment, because the distance between the root apex and the filling apex can reflect the success of the root canal treatment. A smaller distance usually means a smaller gap between the filling and the root apex, and a better treatment effect, while a larger distance may imply that the position of the filling is not ideal or there are other problems. Therefore, by accurately segmenting the teeth and root canal fillings, more reliable data can be obtained, which can then more accurately evaluate the quality of root canal treatment and the patient's dental health.

[0044] For example, after classification, two categories, just-filled and under-filled, will be screened out. The pixel positions at the top of the segmented teeth and fillings can be obtained, and then the distance between the two points can be calculated by calling np.sqrt using the numpy package in Python. If it is greater than 0.02 mm (that is, the set threshold) mm, it is under-filled. If it is between 0 and 0.02 mm (that is, the set threshold), it is just-filled.

[0045] In this implementation, after the first deep learning model and the second deep learning model are trained, they need to be tested. The specific testing process includes: The input X-ray image is sent to the trained first deep learning model and the second deep learning model for processing. The first deep learning model and the second deep learning model have been fully trained and have the ability to efficiently process X-ray images. During the processing process, the first deep learning model will first perform tooth segmentation on the image, accurately distinguish the areas of teeth and root canal fillings, and obtain clear boundary information of teeth and fillings, laying the foundation for subsequent evaluation work; for the root canal treatment part, the second deep learning model will further evaluate the quality of the filling. It will accurately identify the location, shape, texture and other features of the filling, calculate the vertical distance of the key points, that is, the distance between the top of the root and the top of the filling, and set the threshold for evaluation based on the pre-set distance.

[0046] Example 2: like Figure 5 As shown, this implementation provides an oral root canal treatment filling quality assessment system based on deep learning, including: An image acquisition unit is configured to acquire an X-ray dental image of an oral root canal treatment. The specific working method of this unit is the same as the process of step S1 provided in Example 1 and will not be repeated here. The evaluation and classification unit is configured to: convert the spatial information of the teeth into frequency domain information and store it in the loss function of the first deep learning model, use the first deep learning model to segment the root canal treatment part of the teeth in the X-ray dental image, and use the second deep learning model to obtain the root canal treatment effect classification result based on the segmentation result; the specific working method of this unit is the same as the process of step S2 provided in Example 1, and will not be repeated here.

[0047] It is understandable that each of the above-mentioned units can be separately or completely combined into one or several other units to form a whole, or one (or more) of the units can be further divided into multiple functionally smaller units to form a whole, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the image processing device may also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0048] According to another embodiment of the present application, the system described in this embodiment can be constructed and the image processing method of the embodiment of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0049] Example 3: like Figure 6 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0050] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0051] The processor 1001 (also called CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0052] The processor 1001 is configured to execute the following process: Acquire an X-ray dental image of an oral root canal treatment; the specific working method of this unit is the same as the process of step S1 provided in Example 1, and will not be repeated here; The spatial information of the teeth is converted into frequency domain information and stored in the loss function of the first deep learning model. The first deep learning model is used to segment the teeth of the root canal treatment part in the X-ray dental image. The second deep learning model is used to obtain the root canal treatment effect classification result based on the segmentation result. The specific working method of this unit is the same as the process of step S2 provided in Example 1, and will not be repeated here.

[0053] Example 4: This implementation provides a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device within an electronic device that stores programs and data. It should be understood that the computer-readable storage medium herein may include both built-in storage media within the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides storage space that stores the processing system of the electronic device.

[0054] Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; alternatively, it may be at least one computer-readable storage medium located remotely from the processor.

[0055] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process: Acquire an X-ray dental image of an oral root canal treatment; the specific working method of this unit is the same as the process of step S1 provided in Example 1, and will not be repeated here; The spatial information of the teeth is converted into frequency domain information and stored in the loss function of the first deep learning model. The first deep learning model is used to segment the teeth of the root canal treatment part in the X-ray dental image. The second deep learning model is used to obtain the root canal treatment effect classification result based on the segmentation result. The specific working method of this unit is the same as the process of step S2 provided in Example 1, and will not be repeated here.

[0056] Example 5: This embodiment provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process: Acquire an X-ray dental image of an oral root canal treatment; the specific working method of this unit is the same as the process of step S1 provided in Example 1, and will not be repeated here; The spatial information of the teeth is converted into frequency domain information and stored in the loss function of the first deep learning model. The first deep learning model is used to segment the teeth of the root canal treatment part in the X-ray dental image. The second deep learning model is used to obtain the root canal treatment effect classification result based on the segmentation result. The specific working method of this unit is the same as the process of step S2 provided in Example 1, and will not be repeated here.

[0057] Those skilled in the art will appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0058] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data processing device such as a server or data center that integrates one or more available media. Available media can be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive (SSD)).

[0059] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the quality of oral root canal treatment and filling based on deep learning, characterized in that: The process includes: Obtain X-ray dental images for oral root canal treatment; The spatial information of the teeth is converted into frequency domain information and stored in the loss function of the first deep learning model. The first deep learning model is used to segment the root canal treatment part of the teeth in the X-ray dental image. The second deep learning model is used according to the segmentation result to obtain the classification result of the root canal treatment effect.

2. The oral root canal treatment filling quality assessment method based on deep learning according to claim 1, characterized in that: The spatial information of the teeth is converted into frequency domain information and stored in the loss function of the first deep learning model, including: The loss function of the first deep learning model is the original loss function and The product of , represents the frequency domain representation of the prediction boundary a, Represents the frequency domain representation of the manually labeled boundary b. The loss function of the first deep learning model is a quantitative representation of the closed shape.

3. The oral root canal treatment filling quality assessment method based on deep learning according to claim 1 or 2, characterized in that: The first deep learning model is a symmetrical U-shaped structure, with the first branch of the U-shaped structure consisting of sequentially connected convolutional layers and four Mamba modules, and the second branch of the U-shaped structure consisting of sequentially connected four Mamba modules and a convolutional layer; wherein the Mamba module is composed of a VSS module and a depthwise separable convolution; Two convolutional layers are used to extract shallow features, and eight Mamba modules introduce global visual information through depthwise separable convolutions and VSS modules while processing local features to segment teeth and root canal fillings.

4. The oral root canal treatment filling quality assessment method based on deep learning as claimed in claim 3, characterized in that: The Mamba modules on the first branch are the first Mamba module, the second Mamba module, the third Mamba module and the fourth Mamba module, and the Mamba modules on the second branch are the fifth Mamba module, the sixth Mamba module, the seventh Mamba module and the eighth Mamba module; The first Mamba module is connected to the eighth Mamba module through a spatial attention mechanism, the second Mamba module is connected to the seventh Mamba module through a spatial attention mechanism, the third Mamba module is connected to the sixth Mamba module through a spatial attention mechanism, and the fourth Mamba module is connected to the fifth Mamba module through a spatial attention mechanism.

5. The oral root canal treatment filling quality assessment method based on deep learning as claimed in claim 3, characterized in that: The Mamba module consists of two branches. For each branch, a depth-wise separable convolution is used to extract shallow features. Then, the features are passed through two VSS modules in sequence. The VSS modules capture the global semantic information in the image by introducing global information and cross-channel interactions. After extracting features from each branch, the features extracted from each branch are integrated and fused with the original input features to form the final feature representation. The VSS module consists of two branches. The first branch includes a linear layer, a depth-wise separable convolutional layer, a SiLU activation function, an SS2D module, and a normalization layer to extract and model the long-range spatial features of the image. The second branch includes a linear layer and SiLU activation function to extract local features of the image; The outputs of the two branches are multiplied to aggregate the features of the two branches, and then processed through a linear layer to obtain the fused features. The fused features are weighted summed with the original input of the VSS module to obtain the final output of the VSS module.

6. The oral root canal treatment filling quality assessment method based on deep learning according to claim 1 or 2, characterized in that: The classification results of root canal treatment effects include just-filled and under-filled. The distance between the top pixel points of the tooth and the filling is calculated. If the distance is greater than a set threshold, it is under-filled. If the distance is less than or equal to the set threshold, it is just-filled. The distance is a positive number.

7. A deep learning-based oral root canal treatment filling quality assessment system, characterized in that: include: An image acquisition unit is configured to: acquire an X-ray dental film image of an oral root canal treatment; The evaluation and classification unit is configured to: convert the spatial information of the teeth into frequency domain information and store it in the loss function of the first deep learning model, use the first deep learning model to segment the root canal treatment part of the teeth in the X-ray dental image, and use the second deep learning model to obtain the root canal treatment effect classification result based on the segmentation result.

8. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the method for evaluating the quality of oral root canal treatment and filling based on deep learning as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the oral root canal treatment filling quality assessment method based on deep learning as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the oral root canal treatment filling quality assessment method based on deep learning as described in any one of claims 1 to 6.

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