Deep learning-based intelligent evaluation method and system for screening multiple diseases of lumbar vertebra

Abstract

The invention provides an intelligent assessment method, system and device for screening multiple diseases of lumbar vertebra based on deep learning and a computer readable storage medium. The lumbar vertebra multi-disease screening intelligent evaluation method based on deep learning comprises the following steps: acquiring a lumbar vertebra normal position X-ray image and a lumbar vertebra lateral position X-ray image; inputting the lumbar vertebra normal position X-ray image and the lumbar vertebra lateral position X-ray image into a preset lumbar vertebra multi-disease disease diagnosis model, and outputting a lumbar vertebra multi-disease disease diagnosis result; wherein the diagnosis results of the multiple diseases of the lumbar vertebra comprise lumbar spondylolisthesis, degenerative lumbar spondylolisthesis, lumbar degeneration and lumbar vertebra fracture; the lumbar degeneration comprises intervertebral space change and osteophyte; the lumbar fracture comprises compression fracture, burst fracture and comminuted fracture. According to the embodiment of the invention, the accuracy of diagnosis of multiple diseases of the lumbar vertebra can be improved.

Description

Technical Field

[0001] The present application belongs to the field of lumbar multi-disease screening, and in particular, relates to an intelligent assessment method, system, device and computer-readable storage medium for lumbar multi-disease screening based on deep learning. Background Art

[0002] At present, the diagnosis method for a single lumbar disease in related technologies is to diagnose through deep capsule networks (CapsuleNetworks, CapsNets).

[0003] However, CapsNets have difficulty detecting multiple objects of the same type that are close together in an image because only one capsule at each location is responsible for detecting a specific type of object, which limits the performance of the model in crowded scenes. Therefore, for multiple vertebrae of the same type that are close together in the lumbar spine, CapsNets have low accuracy in diagnosing multiple lumbar diseases.

[0004] Therefore, how to improve the accuracy of diagnosis of multiple lumbar diseases is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] The embodiments of the present application provide a deep learning-based intelligent assessment method, system, device and computer-readable storage medium for screening of multiple lumbar diseases, which can improve the accuracy of diagnosis of multiple lumbar diseases.

[0006] In a first aspect, the present application provides an intelligent assessment method for lumbar multi-disease screening based on deep learning, comprising:

[0007] Obtain anteroposterior and lateral lumbar spine X-ray images;

[0008] Inputting the lumbar spine frontal X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and outputting the lumbar spine multi-disease diagnosis results;

[0009] Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures.

[0010] The optional network structure of the lumbar multi-disease diagnosis model includes:

[0011] 4 layers of attention graph perceptron layers, 4 layers of gated recurrent units, and multiple multi-layer perceptrons are used to output results;

[0012] Starting from the input layer, it accepts node features and edge features of the non-graph, and is processed by multi-layer attention graph perceptrons and gated recurrent units to gradually extract and learn the complex structural relationships and functional characteristics of the graph;

[0013] Finally, a feature vector representing the non-graph is generated through the multi-layer perceptron output layer.

[0014] Optional, including:

[0015] The non-graph data, physical data labels and graph pattern data are used as the input vectors of the lumbar multi-disease diagnosis model to be trained, and the randomly initialized orthogonal vector is used as the initial value. The number of network iterations is set to 1000, the learning rate is set to 0.001, and the Adam optimizer is used to combine the representation features and physical features to form a hybrid loss function.

[0016] The predicted value is calculated through forward propagation, and the loss function is used to measure the gap between the predicted value and the actual label;

[0017] Then, the optimization algorithm is applied to adjust the model parameters through back propagation, gradually reducing the value of the loss function, and through multiple iterative training, a pre-trained lumbar spine multi-disease diagnosis model is obtained.

[0018] Optional, including:

[0019] The test data is input into the pre-trained lumbar spine multi-disease diagnosis model for testing, the predicted non-graph physical parameter characteristics are compared with the actual parameter characteristics, and the network model is reversely optimized based on the comparison results to obtain the trained lumbar spine multi-disease diagnosis model.

[0020] Optionally, after obtaining the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image, the following steps are further included:

[0021] The collected lumbar spine anteroposterior X-ray images and lumbar spine lateral X-ray images are preprocessed, including image cropping, scaling, and denoising, to meet the input requirements of the model.

[0022] Optionally, the collected lumbar spine anteroposterior X-ray images and lumbar spine lateral X-ray images are preprocessed, including image cropping, scaling, and denoising, including:

[0023] Resize images to a uniform size;

[0024] Convert color images to grayscale or black and white to simplify analysis and reduce noise;

[0025] Apply smoothing, blurring, and filtering techniques to remove unwanted noise from images;

[0026] Normalize pixel values ​​to a standard range;

[0027] Crop the image to remove irrelevant or unimportant areas;

[0028] Increase the diversity of the training dataset by applying random transformations;

[0029] Convert raw data into a form suitable for deep learning;

[0030] The dataset is divided into training, validation, and test sets to facilitate evaluating the performance of the model.

[0031] Optionally, the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image are input into a preset lumbar spine multi-disease diagnosis model to output lumbar spine multi-disease diagnosis results, including:

[0032] Automatically extract features from pre-processed images using deep learning models;

[0033] The image data after preprocessing and feature extraction are input into the preset lumbar multi-disease diagnosis model;

[0034] The model analyzes the input image data and outputs a diagnosis result.

[0035] In a second aspect, the present application embodiment provides an intelligent assessment system for lumbar multi-disease screening based on deep learning, including:

[0036] An image acquisition module, used for acquiring lumbar anteroposterior X-ray images and lumbar lateral X-ray images;

[0037] A diagnosis module, used for inputting the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and outputting the lumbar spine multi-disease diagnosis results;

[0038] Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;

[0040] When the processor executes the computer program instructions, an intelligent assessment method for lumbar vertebrae multi-disease screening based on deep learning is implemented.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, an intelligent assessment method for screening multiple lumbar diseases based on deep learning is implemented.

[0042] The deep learning-based intelligent assessment method, system, device and computer-readable storage medium for lumbar spine multi-disease screening in the embodiments of the present application can improve the accuracy of diagnosis of lumbar spine multi-diseases.

[0043] The deep learning-based intelligent assessment method for lumbar multi-disease screening includes:

[0044] Obtain anteroposterior and lateral lumbar spine X-ray images;

[0045] Inputting the lumbar spine frontal X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and outputting the lumbar spine multi-disease diagnosis results;

[0046] Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 It is a flowchart of a method for intelligent assessment of lumbar vertebrae multi-disease screening based on deep learning provided by an embodiment of the present application;

[0049] Figure 2 and Figure 3 It is a diagnosis result of multiple lumbar diseases provided by an embodiment of the present application;

[0050] Figure 4 This is a schematic diagram of the network structure of a lumbar multi-disease diagnosis model provided by an embodiment of the present application;

[0051] Figure 5 This is a schematic diagram of the training process of a lumbar multi-disease diagnosis model provided by an embodiment of the present application;

[0052] Figure 6 It is a structural schematic diagram of a deep learning-based intelligent assessment system for lumbar multi-disease screening provided by an embodiment of the present application;

[0053] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0054] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0056] In order to solve the problems of the prior art, the embodiments of the present application provide a method, system, device and computer-readable storage medium for intelligent assessment of lumbar vertebrae multi-disease screening based on deep learning. The following first introduces the intelligent assessment method for lumbar vertebrae multi-disease screening based on deep learning provided by the embodiments of the present application.

[0057] Figure 1 The flowchart of the intelligent assessment method for lumbar multi-disease screening based on deep learning provided by one embodiment of the present application is shown. Figure 1 As shown, this deep learning-based intelligent assessment method for lumbar multi-disease screening includes:

[0058] S101, obtaining an anteroposterior X-ray image of the lumbar spine and a lateral X-ray image of the lumbar spine;

[0059] S102, inputting the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and outputting the lumbar spine multi-disease diagnosis results;

[0060] Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures.

[0061] Figure 2 and Figure 3 This is the diagnosis result of multiple lumbar diseases provided by an embodiment of the present application.

[0062] Figure 4 This is a schematic diagram of the network structure of a lumbar multi-disease diagnosis model provided by an embodiment of the present application;

[0063] In one embodiment, the network structure of the lumbar multi-disease diagnosis model includes:

[0064] 4 layers of attention graph perceptron layers, 4 layers of gated recurrent units, and multiple multi-layer perceptrons are used to output results;

[0065] Starting from the input layer, it accepts node features and edge features of the non-graph, and is processed by multi-layer attention graph perceptrons and gated recurrent units to gradually extract and learn the complex structural relationships and functional characteristics of the graph;

[0066] Finally, a feature vector representing the non-graph is generated through the multi-layer perceptron output layer.

[0067] Figure 5 This is a schematic diagram of the training process of a lumbar multi-disease diagnosis model provided by an embodiment of the present application;

[0068] In one embodiment, it includes:

[0069] The non-graph data, physical data labels and graph pattern data are used as the input vectors of the lumbar multi-disease diagnosis model to be trained, and the randomly initialized orthogonal vector is used as the initial value. The number of network iterations is set to 1000, the learning rate is set to 0.001, and the Adam optimizer is used to combine the representation features and physical features to form a hybrid loss function.

[0070] The predicted value is calculated through forward propagation, and the loss function is used to measure the gap between the predicted value and the actual label;

[0071] Then, the optimization algorithm is applied to adjust the model parameters through back propagation, gradually reducing the value of the loss function, and through multiple iterative training, a pre-trained lumbar spine multi-disease diagnosis model is obtained.

[0072] In one embodiment, it includes:

[0073] The test data is input into the pre-trained lumbar spine multi-disease diagnosis model for testing, the predicted non-graph physical parameter characteristics are compared with the actual parameter characteristics, and the network model is reversely optimized based on the comparison results to obtain the trained lumbar spine multi-disease diagnosis model.

[0074] In one embodiment, after acquiring the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image, the method further includes:

[0075] The collected lumbar spine anteroposterior X-ray images and lumbar spine lateral X-ray images are preprocessed, including image cropping, scaling, and denoising, to meet the input requirements of the model.

[0076] In one embodiment, the collected lumbar spine anteroposterior X-ray images and lumbar spine lateral X-ray images are preprocessed, including image cropping, scaling, and denoising, including:

[0077] Resize images to a uniform size;

[0078] Convert color images to grayscale or black and white to simplify analysis and reduce noise;

[0079] Apply smoothing, blurring, and filtering techniques to remove unwanted noise from images;

[0080] Normalize pixel values ​​to a standard range;

[0081] Crop the image to remove irrelevant or unimportant areas;

[0082] Increase the diversity of the training dataset by applying random transformations;

[0083] Convert raw data into a form suitable for deep learning;

[0084] The dataset is divided into training, validation, and test sets to facilitate evaluating the performance of the model.

[0085] In one embodiment, the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image are input into a preset lumbar spine multi-disease diagnosis model, and the lumbar spine multi-disease diagnosis results are output, including:

[0086] Automatically extract features from pre-processed images using deep learning models;

[0087] The image data after preprocessing and feature extraction are input into the preset lumbar multi-disease diagnosis model;

[0088] The model analyzes the input image data and outputs a diagnosis result.

[0089] Figure 6 It is a structural schematic diagram of a deep learning-based intelligent assessment system for lumbar multi-disease screening provided by an embodiment of the present application;

[0090] The deep learning-based intelligent assessment system for lumbar multi-disease screening includes:

[0091] An image acquisition module 601 is used to acquire an anteroposterior X-ray image of the lumbar spine and a lateral X-ray image of the lumbar spine;

[0092] The diagnosis module 602 is used to input the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and output the lumbar spine multi-disease diagnosis results;

[0093] Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures.

[0094] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application.

[0095] The electronic device may include a processor 701 and a memory 702 storing computer program instructions.

[0096] Specifically, the processor 701 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0097] The memory 702 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 702 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 702 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 702 may be inside or outside the electronic device. In a particular embodiment, the memory 702 may be a non-volatile solid-state memory.

[0098] In one embodiment, the memory 702 may be a read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0099] The processor 701 reads and executes the computer program instructions stored in the memory 702 to implement any one of the deep learning-based intelligent assessment methods for lumbar multi-disease screening in the above-mentioned embodiments.

[0100] In one example, the electronic device may further include a communication interface 703 and a bus 710. Figure 7 As shown, the processor 701, the memory 702, and the communication interface 703 are connected via a bus 710 and communicate with each other.

[0101] The communication interface 703 is mainly used to implement communication between various modules, systems, units and / or devices in the embodiments of the present application.

[0102] Bus 710 includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 710 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0103] In addition, in combination with the deep learning-based intelligent assessment method for lumbar multi-disease screening in the above embodiments, the present application embodiment can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any of the deep learning-based intelligent assessment methods for lumbar multi-disease screening in the above embodiments is implemented.

[0104] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0105] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0106] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or systems. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0107] The above reference is according to the method of the embodiment of the present application, the flow chart of system (system) and computer program product and / or block diagram and describe various aspects of the present application.It should be understood that each square frame in the flow chart and / or block diagram and the combination of each square frame in the flow chart and / or block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of general-purpose computer, special-purpose computer or other programmable data processing system, to produce a kind of machine, so that these instructions executed by the processor of computer or other programmable data processing system enable the realization of the function / action specified in one or more square frames of flow chart and / or block diagram.Such processor can be but not limited to general-purpose processor, special-purpose processor, special application processor or field programmable logic circuit.It can also be understood that each square frame in the block chart and / or flow chart and the combination of square frames in the block chart and / or flow chart can also be realized by the special-purpose hardware that performs the specified function or action, or can be realized by the combination of special-purpose hardware and computer instructions.

[0108] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A deep learning-based intelligent assessment method for lumbar multi-disease screening, characterized in that: include: Obtain anteroposterior and lateral lumbar spine X-ray images; Inputting the lumbar spine frontal X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and outputting the lumbar spine multi-disease diagnosis results; Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures.

2. The deep learning-based intelligent assessment method for lumbar multi-disease screening according to claim 1 is characterized in that: The network structure of the lumbar multi-disease diagnosis model includes: 4 layers of attention graph perceptron layers, 4 layers of gated recurrent units, and multiple multi-layer perceptrons are used to output results; Starting from the input layer, it accepts node features and edge features of the non-graph, and is processed by multi-layer attention graph perceptrons and gated recurrent units to gradually extract and learn the complex structural relationships and functional characteristics of the graph; Finally, a feature vector representing the non-graph is generated through the multi-layer perceptron output layer.

3. The deep learning-based intelligent assessment method for lumbar multi-disease screening according to claim 2 is characterized in that: include: The non-graph data, physical data labels and graph pattern data are used as the input vectors of the lumbar multi-disease diagnosis model to be trained, and the randomly initialized orthogonal vector is used as the initial value. The number of network iterations is set to 1000, the learning rate is set to 0.001, and the Adam optimizer is used to combine the representation features and physical features to form a hybrid loss function. The predicted value is calculated through forward propagation, and the loss function is used to measure the gap between the predicted value and the actual label; Then, the optimization algorithm is applied to adjust the model parameters through back propagation, gradually reducing the value of the loss function, and through multiple iterative training, a pre-trained lumbar spine multi-disease diagnosis model is obtained.

4. The deep learning-based intelligent assessment method for lumbar multi-disease screening according to claim 3 is characterized in that: include: The test data is input into the pre-trained lumbar spine multi-disease diagnosis model for testing, the predicted non-graph physical parameter characteristics are compared with the actual parameter characteristics, and the network model is reversely optimized based on the comparison results to obtain the trained lumbar spine multi-disease diagnosis model.

5. The deep learning-based intelligent assessment method for lumbar multi-disease screening according to claim 1 is characterized in that: After obtaining the lumbar spine anteroposterior X-ray image and lumbar spine lateral X-ray image, it also includes: The collected lumbar spine anteroposterior X-ray images and lumbar spine lateral X-ray images are preprocessed, including image cropping, scaling, and denoising, to meet the input requirements of the model.

6. The deep learning-based intelligent assessment method for lumbar multi-disease screening according to claim 5 is characterized in that: The collected lumbar spine X-ray images and lumbar spine lateral X-ray images are preprocessed, including image cropping, scaling, and denoising, including: Resize images to a uniform size; Convert color images to grayscale or black and white to simplify analysis and reduce noise; Apply smoothing, blurring, and filtering techniques to remove unwanted noise from images; Normalize pixel values ​​to a standard range; Crop the image to remove irrelevant or unimportant areas; Increase the diversity of the training dataset by applying random transformations; Convert raw data into a form suitable for deep learning; The dataset is divided into training, validation, and test sets to facilitate evaluating the performance of the model.

7. The deep learning-based intelligent assessment method for lumbar multi-disease screening according to claim 6 is characterized in that: The lumbar spine frontal X-ray image and the lumbar spine lateral X-ray image are input into the preset lumbar spine multi-disease diagnosis model, and the lumbar spine multi-disease diagnosis results are output, including: Automatically extract features from pre-processed images using deep learning models; The image data after preprocessing and feature extraction are input into the preset lumbar multi-disease diagnosis model; The model analyzes the input image data and outputs a diagnosis result.

8. A deep learning-based intelligent assessment system for lumbar multi-disease screening, characterized in that: The system comprises: An image acquisition module, used for acquiring lumbar anteroposterior X-ray images and lumbar lateral X-ray images; A diagnosis module, used for inputting the lumbar spine anteroposterior X-ray image and the lumbar spine lateral X-ray image into a preset lumbar spine multi-disease diagnosis model, and outputting the lumbar spine multi-disease diagnosis results; Among them, the diagnosis results of multiple lumbar spine diseases include: lumbar spondylolisthesis, degenerative lumbar scoliosis, lumbar degeneration, and lumbar fracture; lumbar degeneration includes: changes in the intervertebral disc and osteophytes; lumbar fractures include: compression fractures, burst fractures, and comminuted fractures.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the deep learning-based intelligent assessment method for lumbar multi-disease screening as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the deep learning-based intelligent assessment method for lumbar multi-disease screening as described in any one of claims 1 to 7.

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