Artificial Intelligence-based Bone Injury Degree Assessment System

Abstract

The present invention discloses an artificial intelligence-based bone injury degree evaluation system, which includes a data set construction module, an image enhancement module, a bone injury degree evaluation model establishment module, and a bone injury degree evaluation module. The present invention relates to the technical field of bone injury evaluation, specifically an artificial intelligence-based bone injury degree evaluation system. Through the adaptive adjustment of the local scale factor, the extraction of base information by Gaussian kernel convolution, the enhancement of details by anti-base information, and the fusion of multi-scale structure tensors, the visibility of micro-injury features is enhanced, the interference of noise on injury recognition is reduced, and the accuracy and reliability of the bone injury evaluation system are effectively improved; through dynamic convolution to enhance local information expression, variational distribution modeling, introducing a channel attention mechanism, projecting features into the Riemannian manifold space, and integrating variational loss to optimize model training, the accuracy, stability, and generalization ability of bone injury degree evaluation are improved, and treatment decisions are more accurately assisted.

Description

Technical Field

[0001] The present invention relates to the technical field of bone injury assessment, specifically an artificial intelligence-based bone injury degree assessment system. Background Art

[0002] The artificial intelligence-based bone injury degree assessment system utilizes computer vision, deep learning, and medical image analysis technologies. By training medical image data, it can automatically interpret medical images, accurately assess the degree of bone injury, improve the efficiency of clinical decision-making, and reduce misdiagnosis and missed diagnosis. Traditional bone injury assessment schemes have problems such as unclear medical image features of bone injuries, and traditional image enhancement methods have problems of blurred edges, lost details, and large noise interference in bone injury detection; traditional bone injury assessment schemes have problems of insufficient feature extraction, lost local information, inability to adaptively adjust channel weights, and limited assessment accuracy. Summary of the Invention

[0003] In view of the above situation, to overcome the defects of the prior art, the present invention provides an artificial intelligence-based bone injury degree assessment system. Aiming at the problems of unclear medical image features of bone injuries in traditional bone injury assessment schemes and the problems of blurred edges, lost details, and large noise interference in traditional image enhancement methods in bone injury detection, this solution improves the contrast and edge sharpness of the bone injury area through adaptive adjustment of local scale factors, extraction of base information by Gaussian kernel convolution, enhancement of details by anti-base information, and fusion of multi-scale structure tensors. At the same time, it enhances the visibility of micro-injury features, reduces the interference of noise on injury recognition, and thus effectively improves the accuracy and reliability of the bone injury assessment system; aiming at the problems of insufficient feature extraction, lost local information, inability to adaptively adjust channel weights, and limited assessment accuracy in traditional bone injury assessment schemes, this solution enhances the expression of local information through dynamic convolution, variational distribution modeling, introduction of a channel attention mechanism, projection of features into the Riemannian manifold space, and incorporation of variational loss to optimize model training, improving the accuracy, robustness, and generalization ability of bone injury degree assessment, and thus more accurately assisting clinical diagnosis and treatment decisions.

[0004] The technical solution adopted by the present invention is as follows: The artificial intelligence-based bone injury degree assessment system provided by the present invention includes a data set construction module, an image enhancement module, a bone injury degree assessment model establishment module, and a bone injury degree assessment module;

[0005] The data set construction module collects historical CT images of bone injuries and the degree of bone injuries, annotates the CT images with the degree of bone injuries, and finally creates a model training data set, adding the annotated CT images to the model training data set;

[0006] The image enhancement module includes: a local scale determination unit, a base information acquisition unit, an information inversion unit, a bilateral weight generation unit, a detail enhancement unit, a local structure tensor construction unit, an adaptive weight generation unit, and a feature fusion unit;

[0007] The bone injury degree assessment model establishment module includes: a dynamic convolution unit, a variational distribution definition unit, a variational feature pooling unit, a channel attention enhancement unit, a feature projection unit, an evaluation unit, a loss calculation unit, and a parameter learning unit;

[0008] The bone injury degree assessment module collects the bone injury CT images of the user, inputs the images into the bone injury degree assessment model, and the model outputs the bone injury degree of the user.

[0009] Furthermore, the dataset construction module collects the historical bone injury CT images and the bone injury degrees, where the bone injury degrees include mild injury, moderate injury, and severe injury, annotates the CT images with the bone injury degree data, and finally creates a model training dataset and adds the annotated CT images to the model training dataset.

[0010] Furthermore, the image enhancement module specifically includes the following units:

[0011] The local scale determination unit first sets the size of the local window, calculates the variance of the pixels within the local window, and finally calculates the local scale unit;

[0012] The base information acquisition unit performs Gaussian kernel convolution using the local scale factor to obtain the base information;

[0013] The information inversion unit subtracts the base information from the original image to obtain the anti-base information containing the bone injury edges and fine textures;

[0014] The bilateral weight generation unit calculates the bilateral weights within the local window while considering the spatial distance and the anti-base information difference;

[0015] The detail enhancement unit magnifies the edge and texture features of the bone injury in the detail layer through local bilateral weighting while retaining the edge information;

[0016] The local structure tensor construction unit captures the local structure information and edge direction features by calculating the image gradient and performing Gaussian smoothing;

[0017] The adaptive weight generation unit;

[0018] The feature fusion unit uses the adaptive weights to fuse the features of the detail layer and the structure layer to comprehensively depict the bone injury details and local structures.

[0019] Furthermore, the module for establishing the bone injury degree assessment model specifically includes the following units:

[0020] The dynamic convolution unit introduces a depth adjustment matrix to modulate the weights of the convolution kernels, dynamically respond to convolution, and reconstruct local information;

[0021] The variational distribution definition unit constructs a variational distribution using the statistical mean and variance of local region features;

[0022] The variational feature pooling unit performs a pooling operation using the method of variational sampling to pool local feature information;

[0023] The channel attention enhancement unit enhances important channel features by generating channel attention weights;

[0024] The feature projection unit projects the features into the Riemannian manifold space;

[0025] The evaluation unit evaluates the bone injury degree category;

[0026] The loss calculation unit measures the classification error of the model using cross-entropy loss, and at the same time introduces a variational distribution regularization term to encourage the model to learn a stable and information-rich feature distribution;

[0027] The parameter learning unit calculates the gradient of the loss function with respect to the model parameters and sets the learning rate for parameter update.

[0028] Furthermore, the bone injury degree assessment module collects the bone injury CT images of the user, inputs the images into the bone injury degree assessment model, and the model outputs the bone injury degree of the user.

[0029] The beneficial effects achieved by the present invention using the above solution are as follows:

[0030] (1) Aiming at the problems existing in the traditional bone injury assessment scheme, such as unclear medical image features of bone injuries and edge blurring, detail loss, and large noise interference in traditional image enhancement methods for bone injury detection, this solution improves the contrast and edge sharpness of the bone injury area through local scale factor adaptive adjustment, Gaussian kernel convolution to extract base information, anti-base information to enhance details, and multi-scale structure tensor fusion. At the same time, it enhances the visibility of micro-injury features and reduces the interference of noise on injury recognition, thereby effectively improving the accuracy and reliability of the bone injury assessment system.

[0031] (2) Aiming at the problems of insufficient feature extraction, local information loss, inability to adaptively adjust channel weights, and limited evaluation accuracy in traditional bone injury assessment schemes, this solution enhances the expression of local information through dynamic convolution, variational distribution modeling, introducing a channel attention mechanism, projecting features into the Riemannian manifold space, and integrating variational loss to optimize model training, improving the accuracy, robustness, and generalization ability of bone injury degree assessment, thereby more accurately assisting clinical diagnosis and treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a schematic diagram of the artificial intelligence-based bone injury degree assessment system provided by the present invention;

[0033] Figure 2 is a schematic diagram of the image enhancement module;

[0034] Figure 3 is a schematic diagram of the module for establishing a bone injury degree assessment model.

[0035] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0038] Embodiment 1, refer to Figure 1 , the artificial intelligence-based bone injury degree assessment system provided by the present invention includes a dataset construction module, an image enhancement module, a bone injury degree assessment model establishment module, and a bone injury degree assessment module;

[0039] The dataset construction module collects historical CT images of bone injuries and the degrees of bone injuries, where the degrees of bone injuries include mild injuries, moderate injuries, and severe injuries. The CT images are labeled with the bone injury degree data, and finally a model training dataset is created, and the labeled CT images are added to the model training dataset;

[0040] The image enhancement module receives the data sent by the image enhancement module, performs image enhancement through the local scale unit determination unit, base information acquisition unit, information inversion unit, bilateral weight generation unit, detail enhancement unit, local structure tensor construction unit, adaptive weight generation unit, and feature fusion unit, and sends the data to the bone injury degree evaluation model establishment module;

[0041] The bone injury degree evaluation model establishment module receives the data sent by the image enhancement module, and establishes a bone injury degree evaluation model through the dynamic convolution unit, variational distribution definition unit, variational feature pooling unit, channel attention enhancement unit, feature projection unit, evaluation unit, loss calculation unit, and parameter learning unit, and sends the data to the bone injury degree evaluation module;

[0042] The bone injury degree evaluation module receives the data sent by the bone injury degree evaluation model establishment module, and uses the bone injury degree evaluation model to evaluate the user's bone injury degree.

[0043] Embodiment 2, refer to Figure 1 and Figure 2 , based on the above embodiment, the image enhancement module specifically includes the following units:

[0044] The local scale unit determination unit first sets the size of the local window to 5 by 5 pixels, calculates the variance of the pixels within the local window, and finally calculates the local scale unit, which is expressed as follows:

[0045] ;

[0046] where x and y respectively represent the abscissa index and ordinate index of the pixels of the image, represents the local scale factor of the pixel at position , represents the exponential function with the natural constant as the base, represents the local window, represents the pixel variance within the local window centered on the pixel at position , represents the sensitivity coefficient;

[0047] The base information acquisition unit uses the local scale factor for Gaussian kernel convolution to obtain the base information, which is expressed as follows:

[0048] ;

[0049] Among them, represents the base information of the pixel at position . represents pi, u and v respectively represent the abscissa index and ordinate index of the pixel in the local window, represents the pixel value of the original image at the local window position .

[0050] Information reverse unit, subtracting the base information from the original image to obtain the anti-base information containing the bone injury edge and fine texture, which is expressed as follows:

[0051] ;

[0052] Among them, represents the anti-base information at position , represents the pixel value of the original image at position .

[0053] Generate bilateral weight unit, considering both spatial distance and anti-base information difference, calculate the bilateral weight within the local window, which is expressed as follows:

[0054] ;

[0055] Among them, represents the bilateral weight centered on the pixel at position at the local window position , represents the spatial smoothing coefficient, represents the pixel similarity coefficient, represents the anti-base information at the local window position .

[0056] Detail enhancement unit, through local bilateral weighting enhancement, amplify the edge and texture features of bone injury in the detail layer while retaining edge information, which is expressed as follows:

[0057] ;

[0058] Among them, represents the pixel value of the image after detail enhancement at position , represents the detail enhancement coefficient, represents the coordinate within the local window, represents taking the absolute value;

[0059] Construct a local structure tensor unit. By calculating the image gradient and performing Gaussian smoothing, local structure information and edge direction features are captured, which is expressed as follows:

[0060] ;

[0061] where, represents the local structure tensor, represents a Gaussian smoothing kernel with a standard deviation of ; represents the value of the original image at position after taking the partial derivative in the x direction, represents the value of the original image at position after taking the partial derivative in the y direction;

[0062] Generate an adaptive weight unit, which is expressed as follows:

[0063] ;

[0064] where, and represent the adaptive weights, represents the detail threshold;

[0065] A feature fusion unit uses the adaptive weights to fuse the features of the detail layer and the structure layer, and comprehensively depicts the bone injury details and local structures, which is expressed as follows:

[0066] ;

[0067] where, represents the value of the image after feature fusion at position ; represents the local structure tensor 's maximum eigenvalue.

[0068] By performing the above operations, aiming at the problems existing in the traditional bone injury assessment scheme, such as unclear medical image features of bone injuries and edge blurring, detail loss, and large noise interference in traditional image enhancement methods for bone injury detection, this scheme improves the contrast and edge sharpness of the bone injury area through local scale factor adaptive adjustment, Gaussian kernel convolution to extract base information, anti-base information to enhance details, and multi-scale structure tensor fusion. At the same time, it enhances the visibility of tiny injury features, reduces the interference of noise on injury recognition, and thus effectively improves the accuracy and reliability of the bone injury assessment system.

[0069] Example 3, referring to Figure 1 and Figure 3 , based on the above example, the module for establishing a bone injury degree assessment model specifically includes the following units:

[0070] The dynamic convolution unit introduces a depth adjustment matrix to modulate the weights of the convolution kernel, dynamically respond to the convolution, and reconstruct the local information, which is expressed as follows:

[0071] ;

[0072] Among them, h and w respectively represent the horizontal coordinate index and vertical coordinate index of the feature map, and i and j respectively represent the position indexes of the rows and columns of the convolution kernel. represents the depth adjustment matrix of the convolution kernel for the feature map at position . represents the sigmoid function, c represents the number of channels of the feature map, represents the depth adjustment weight of channel c, represents the eigenvalue of the input feature map at position and channel c, represents the depth adjustment bias of the convolution kernel at position . represents the dynamic convolution output of channel c for the feature map at position , k represents the size of the convolution kernel, represents the dynamic convolution weight of channel c for the convolution kernel at position . represents the eigenvalue of the input feature map at position and channel c;

[0073] Define the variational distribution unit, which constructs a variational distribution by using the statistical mean and variance of local region features, and is expressed as follows:

[0074] ;

[0075] Among them, represents the mean of the variational distribution, represents the variance of the variational distribution, represents the variational distribution at the feature map position , represents the value of variational sampling at the feature map position , represents a normal distribution with a mean of and a variance of ;

[0076] The variational feature pooling unit uses the method of variational sampling to perform the pooling operation and pool the local feature information, which is expressed as follows:

[0077] ;

[0078] Among them, Denotes the variational feature pooling output of the feature map for channel c at position ; Denotes the variational distribution, Denotes the value of variational sampling, Denotes the expectation operation;

[0079] Channel attention enhancement unit, which enhances important channel features by generating channel attention weights, is expressed as follows:

[0080] ;

[0081] Among them, Denotes the importance score of channel c. H and W respectively denote the maximum index of the abscissa and the maximum index of the ordinate of the feature map. Denotes the attention weight of channel c. C denotes the maximum number of channels, and l denotes the index of the channel. Denotes the importance score of channel l. Denotes the attention enhancement output of the feature map for channel c at position ;

[0082] Feature projection unit, which projects features into the Riemannian manifold space, is expressed as follows:

[0083] ;

[0084] Among them, Denotes the feature projection output of the feature map for channel c at position ; Denotes the logarithmic function, Denotes taking the modulus;

[0085] Evaluation unit, which evaluates the category of bone injury degree, is expressed as follows:

[0086] ;

[0087] Among them, Denotes the output value of the evaluation unit, Denotes the normalized exponential function, and respectively denote the evaluation weight and the evaluation bias, Denotes the input value of the evaluation unit;

[0088] Loss calculation unit, which measures the classification error of the model using cross-entropy loss and introduces a variational distribution regularization term to encourage the model to learn a stable and information-rich feature distribution, is expressed as follows:

[0089] ;

[0090] Among them, Denote the loss value of the bone injury degree evaluation model, \(n\) represents the index of the data sample in the model training dataset, \(M\) represents the total number of data samples in the model training dataset, \(o\) represents the index of the category of bone injury degree, and \(O\) represents the total number of categories of bone injury degree. Denote the true label of the \(n\)th data sample in the \(o\)th category. Denote the probability that the model predicts the \(n\)th data sample as the \(o\)th category.

[0091] The parameter learning unit calculates the gradient of the loss function with respect to the model parameters and sets the learning rate for parameter update, which is expressed as follows:

[0092] ;

[0093] where \(t\) represents the number of times of parameter learning. Denote the model weights and bias parameters at the \((t + 1)\)th parameter learning. Denote the model weights and bias parameters at the \(t\)th parameter learning. Denote the model parameter learning rate. Denote the gradient of the model's loss function with respect to the parameters.

[0094] By performing the above operations, for the problems of insufficient feature extraction, local information loss, inability to adaptively adjust channel weights, and limited evaluation accuracy existing in the traditional bone injury evaluation scheme, this scheme enhances local information expression through dynamic convolution, variational distribution modeling, introducing a channel attention mechanism, projecting features into the Riemannian manifold space, and integrating variational loss to optimize model training, improving the accuracy, robustness, and generalization ability of bone injury degree evaluation, thereby more accurately assisting clinical diagnosis and treatment decision-making.

[0095] Example 4, refer to Figure 1 , based on the above example, the bone injury degree evaluation module collects the user's bone injury CT image, inputs the image into the bone injury degree evaluation model, and the model outputs the user's bone injury degree.

[0096] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0097] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

[0098] The present invention and its embodiments have been described above. Such description is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and, without departing from the purpose of the present invention, design similar structural modes and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. An artificial intelligence-based bone injury degree assessment system, characterized in that: It includes a dataset construction module, an image enhancement module, a bone injury degree assessment model establishment module, and a bone injury degree assessment module; The dataset construction module collects historical CT images of bone injuries and the degree of bone injuries, annotates the CT images with the degree of bone injuries, and then creates a model training dataset, adding the annotated CT images to the model training dataset; The image enhancement module separates the base and high-frequency details through local scale adaptive Gaussian smoothing, generates adaptive weights using bilateral filtering and structure tensors, and fuses the detail layer and the structure layer. Specifically, it includes: a local scale determination unit, a base information acquisition unit, an information inversion unit, a bilateral weight generation unit, a detail enhancement unit, a local structure tensor construction unit, an adaptive weight generation unit, and a feature fusion unit; The bone injury degree assessment model establishment module classifies and evaluates the degree of bone injuries through dynamic convolution and depth adjustment, variational distribution-based feature pooling, channel attention, and Riemannian manifold feature projection. Specifically, it includes: a dynamic convolution unit, a variational distribution definition unit, a variational feature pooling unit, a channel attention enhancement unit, a feature projection unit, an evaluation unit, a loss calculation unit, and a parameter learning unit; The bone injury degree assessment module collects the user's bone injury CT image, inputs the image into the bone injury degree assessment model, and the model outputs the degree of the user's bone injury.

2. The artificial intelligence-based bone injury degree assessment system according to claim 1, wherein: The image enhancement module specifically includes the following units: The local scale determination unit first sets the size of the local window, calculates the variance of the pixels within the local window, and finally calculates the local scale unit; The base information acquisition unit performs Gaussian kernel convolution using the local scale factor to obtain the base information; The information inversion unit subtracts the base information from the original image to obtain the anti-base information containing the bone injury edges and fine textures; The bilateral weight generation unit calculates the bilateral weights within the local window while considering the spatial distance and the anti-base information difference; The detail enhancement unit amplifies the edge and texture features of the bone injury in the detail layer through local bilateral weighting while retaining the edge information; The local structure tensor construction unit captures the local structure information and edge direction features by calculating the image gradient and performing Gaussian smoothing; The adaptive weight generation unit; The feature fusion unit uses the adaptive weights to fuse the features of the detail layer and the structure layer, comprehensively depicting the bone injury details and local structures.

3. The artificial intelligence-based bone injury degree assessment system according to claim 1, wherein: The bone injury degree assessment model establishment module specifically includes the following units: The dynamic convolution unit introduces a depth adjustment matrix to modulate the weights of the convolution kernel, dynamically responds to the convolution, and reconstructs the local information; The variational distribution definition unit constructs a variational distribution using the statistical mean and variance of the local region features; The variational feature pooling unit uses the method of variational sampling to perform the pooling operation and pool the local feature information; The channel attention enhancement unit enhances the important channel features by generating channel attention weights; The feature projection unit projects the features into the Riemannian manifold space; The evaluation unit evaluates the category of the bone injury degree; A loss calculation unit that uses cross-entropy loss to measure the classification error of the model, and at the same time introduces a variational distribution regularization term to encourage the model to learn a stable and information-rich feature distribution; A parameter learning unit that calculates the gradient of the loss function with respect to the model parameters and sets a learning rate for parameter update.

4. The artificial intelligence-based bone injury degree evaluation system according to claim 1, wherein: The dataset construction module collects historical CT images of bone injuries and the degrees of bone injuries, where the degrees of bone injuries include mild injuries, moderate injuries, and severe injuries. The CT images are labeled with the bone injury degree data, and finally a model training dataset is created, and the labeled CT images are added to the model training dataset.

5. The bone injury degree evaluation system based on artificial intelligence according to claim 1, characterized in that: The bone injury degree evaluation module collects the user's bone injury CT image, inputs the image into the bone injury degree evaluation model, and the model outputs the user's bone injury degree.

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