Deep learning-based preoperative planning methods, devices, and equipment for spinal deformity correction

Abstract

This application provides a method, apparatus, device, and computer-readable storage medium for preoperative planning of spinal deformity correction based on deep learning. The method includes: acquiring patient identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; wherein the patient identity information includes at least gender and age; based on the patient identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, big data screening is used to identify the most suitable high-quality cases for the patient; the most suitable high-quality cases, the patient's spinal CT images, and spinal bone density are input into a preset preoperative planning model for spinal deformity correction, and the preoperative planning result is output. According to the embodiments of this application, preoperative planning for spinal deformity correction can be performed more accurately.

Description

Technical Field

[0001] This application belongs to the field of preoperative planning for spinal deformity correction, and particularly relates to a deep learning-based method, device, equipment, and computer-readable storage medium for preoperative planning of spinal deformity correction. Background Technology

[0002] Currently, the preoperative planning for spinal deformity correction in related technologies involves: using a segmentation planning neural network module to calculate a feature point heatmap and a CBCT spinal image segmentation map of the pedicle screw channel based on the CBCT spinal image; and using a feature extraction module to calculate the feature values ​​of the pedicle screw channel based on the feature point heatmap and the CBCT spinal image segmentation map. The feature values ​​include the entry point, target point, length, and diameter of the pedicle screw channel, and the feature point heatmap includes a midpoint heatmap and an entry point heatmap.

[0003] However, the accuracy of preoperative planning for spinal deformity correction based solely on CBCT spinal images is not high enough.

[0004] Therefore, how to conduct more accurate preoperative planning for spinal deformity correction is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a deep learning-based preoperative planning method, device, equipment, and computer-readable storage medium for spinal deformity correction, which can more accurately perform preoperative planning for spinal deformity correction.

[0006] In a first aspect, embodiments of this application provide a deep learning-based preoperative planning method for spinal deformity correction, including:

[0007] Obtain the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; the patient's identity information includes at least gender and age;

[0008] Based on the patient's identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, big data screening identifies the best-matched high-quality cases. Among these high-quality cases, the patient's identity information, preoperative spinal Cobb angle, spinal bone density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance, and the location, direction, and type of screws used for surgical placement are recorded.

[0009] The model inputs the best-matched high-quality case, the patient's spinal CT images, and spinal bone density into the preset preoperative planning model for spinal deformity correction, and outputs the preoperative planning results.

[0010] Optionally, obtain patient identification information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance, including:

[0011] Obtain the patient's identity information through the patient's medical card or ID card;

[0012] The patient's spinal region was scanned to obtain spinal CT images;

[0013] Based on the patient's spinal CT images, the patient's spinal Cobb angle, spinal bone density, and spinal offset distance were determined.

[0014] Optionally, based on the patient's spinal CT images, determine the patient's spinal bone density and spinal offset distance, including:

[0015] Input the patient's spinal CT images into a preset bone mineral density measurement and osteoporosis diagnosis model, and output the bone mineral density value and osteoporosis diagnosis result;

[0016] Among them, the diagnostic results for osteoporosis include: normal, osteopenia, and osteoporosis;

[0017] The bone mineral density measurement and osteoporosis diagnosis model is obtained through model training. It includes multiple DenseNet networks. Each DenseNet network consists of multiple dense blocks. The input of each dense block is connected to the output of all the previous dense blocks to form dense connections, which promotes the flow of information and gradients and mitigates gradient vanishing.

[0018] By analyzing spinal CT images, the midline of the spine is extracted as a reference line;

[0019] Based on the extracted spinal midline, the lateral displacement of each vertebra relative to the midline is measured by calculating the offset distance of each segment of the spine.

[0020] Optionally, based on patient identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, big data screening identifies the most suitable high-quality cases for the patient, including:

[0021] Based on the patient's identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, a multidimensional feature vector of the patient is constructed.

[0022] Similarity calculation is performed in medical databases based on patients' multidimensional feature vectors;

[0023] Based on the similarity calculation results, the best-matched high-quality cases were identified.

[0024] Optionally, based on the similarity calculation results, the best-matched high-quality cases are identified, including:

[0025] Based on the similarity calculation results, select the N cases with the highest similarity to the current patient;

[0026] A secondary screening is conducted based on the treatment outcomes, recovery progress, and complication occurrences of the top N cases with the highest similarity to the current patient. The cases with the best treatment outcomes and the best recovery outcomes are selected as the high-quality cases that best match the patient.

[0027] Optionally, the best-matched high-quality case, the patient's spinal CT images, and spinal bone density are input into a pre-defined preoperative planning model for spinal deformity correction, and the preoperative planning results are output, including:

[0028] The best-matched high-quality cases, the patient's spinal CT images, and spinal bone density are integrated into a multi-dimensional input vector.

[0029] Construct a neural network model, wherein the neural network includes a convolutional layer, a pooling layer, a copy layer of the convolutional layer, and a feature layer corresponding to the copy layer;

[0030] The corresponding feature layer is obtained by upsampling and adding the copy layer with the corresponding layer in the convolutional layer. The feature layers are then stacked to obtain a large feature layer, and the preoperative planning result is output.

[0031] Optional preoperative planning results include: the location, direction, and type of screws for surgical placement;

[0032] The screw model includes screw type, screw diameter, and screw length, all of which correspond to the patient's spinal bone density.

[0033] Secondly, embodiments of this application provide a deep learning-based preoperative planning device for spinal deformity correction, comprising:

[0034] The information acquisition module is used to acquire the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; among which, the patient's identity information includes at least gender and age;

[0035] The high-quality case screening module is used to screen out the best-matching high-quality cases based on the patient's identity information, spinal Cobb angle, spinal bone density, and spinal offset distance using big data. The high-quality cases record the patient's identity information, preoperative spinal Cobb angle, spinal bone density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance, and the location, direction, and type of screws used for surgical placement.

[0036] The preoperative planning module is used to input the best-matched high-quality case, the patient's spinal CT images, and spinal bone density into the preset preoperative planning model for spinal deformity correction, and output the preoperative planning results.

[0037] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions;

[0038] When the processor executes the computer program instructions, it implements a deep learning-based preoperative planning method for spinal deformity correction.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement a deep learning-based preoperative planning method for spinal deformity correction.

[0040] The deep learning-based preoperative planning method, apparatus, device, and computer-readable storage medium of this application can perform preoperative planning for spinal deformity correction more accurately.

[0041] This deep learning-based preoperative planning method for spinal deformity correction includes:

[0042] Obtain the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; the patient's identity information includes at least gender and age;

[0043] Based on the patient's identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, big data screening identifies the best-matched high-quality cases. Among these high-quality cases, the patient's identity information, preoperative spinal Cobb angle, spinal bone density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance, and the location, direction, and type of screws used for surgical placement are recorded.

[0044] The model inputs the best-matched high-quality case, the patient's spinal CT images, and spinal bone density into the pre-set preoperative planning model for spinal deformity correction, and outputs the preoperative planning results. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a deep learning-based preoperative planning method for spinal deformity correction provided in one embodiment of this application.

[0047] Figure 2 This is a schematic flowchart of a bone mineral density measurement and osteoporosis diagnosis method provided in one embodiment of this application;

[0048] Figure 3 This is a schematic diagram of the preoperative planning model for spinal deformity correction provided in one embodiment of this application;

[0049] Figure 4 This is a schematic diagram of the preoperative planning device for spinal deformity correction based on deep learning provided in one embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0051] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0053] To address the problems of existing technologies, embodiments of this application provide a method, apparatus, device, and computer-readable storage medium for preoperative planning of spinal deformity correction based on deep learning. The method for preoperative planning of spinal deformity correction based on deep learning provided in this application embodiment will be described first below.

[0054] Figure 1 This illustration shows a flowchart of a deep learning-based preoperative planning method for spinal deformity correction according to an embodiment of this application. Figure 1As shown, this deep learning-based preoperative planning method for spinal deformity correction includes:

[0055] S101. Obtain the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; wherein, the patient's identity information includes at least gender and age;

[0056] S102. Based on the patient's identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, big data screening identifies the best-matched high-quality cases; among them, the high-quality cases record the patient's identity information, preoperative spinal Cobb angle, spinal bone density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance, and the screw position, screw direction, and screw type of the surgical screw placement.

[0057] S103. Input the best matching high-quality case, the patient's spinal CT image, and spinal bone density into the preset preoperative planning model for spinal deformity correction, and output the preoperative planning results.

[0058] In one embodiment, acquiring patient identification information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance includes:

[0059] Obtain the patient's identity information through the patient's medical card or ID card;

[0060] The patient's spinal region was scanned to obtain spinal CT images;

[0061] Based on the patient's spinal CT images, the patient's spinal Cobb angle, spinal bone density, and spinal offset distance were determined.

[0062] Figure 2 This is a schematic flowchart of a bone mineral density measurement and osteoporosis diagnosis method according to one embodiment of this application. In one embodiment, based on a patient's spinal CT image, the spinal bone mineral density and spinal offset distance are determined, including:

[0063] Input the patient's spinal CT images into a preset bone mineral density measurement and osteoporosis diagnosis model, and output the bone mineral density value and osteoporosis diagnosis result;

[0064] Among them, the diagnostic results for osteoporosis include: normal, osteopenia, and osteoporosis;

[0065] The bone mineral density measurement and osteoporosis diagnosis model is obtained through model training. It includes multiple DenseNet networks. Each DenseNet network consists of multiple dense blocks. The input of each dense block is connected to the output of all the previous dense blocks to form dense connections, which promotes the flow of information and gradients and mitigates gradient vanishing.

[0066] By analyzing spinal CT images, the midline of the spine is extracted as a reference line;

[0067] Based on the extracted spinal midline, the lateral displacement of each vertebra relative to the midline is measured by calculating the offset distance of each segment of the spine.

[0068] In one embodiment, based on patient identification information, spinal Cobb angle, spinal bone density, and spinal offset distance, big data screening identifies the most suitable high-quality cases for the patient, including:

[0069] Based on the patient's identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, a multidimensional feature vector of the patient is constructed.

[0070] Similarity calculation is performed in medical databases based on patients' multidimensional feature vectors;

[0071] Based on the similarity calculation results, the best-matched high-quality cases were identified.

[0072] In one embodiment, based on similarity calculation results, the best-matched high-quality cases are identified, including:

[0073] Based on the similarity calculation results, select the N cases with the highest similarity to the current patient;

[0074] A secondary screening is conducted based on the treatment outcomes, recovery progress, and complication occurrences of the top N cases with the highest similarity to the current patient. The cases with the best treatment outcomes and the best recovery outcomes are selected as the high-quality cases that best match the patient.

[0075] Figure 3 This is a schematic diagram of the preoperative planning model for spinal deformity correction provided in one embodiment of this application. In one embodiment, the best-matched high-quality case, the patient's spinal CT image, and spinal bone density are input into the preset preoperative planning model for spinal deformity correction, and the preoperative planning results are output, including:

[0076] The best-matched high-quality cases, the patient's spinal CT images, and spinal bone density are integrated into a multi-dimensional input vector.

[0077] Construct a neural network model, wherein the neural network includes a convolutional layer, a pooling layer, a copy layer of the convolutional layer, and a feature layer corresponding to the copy layer;

[0078] The corresponding feature layer is obtained by upsampling and adding the copy layer with the corresponding layer in the convolutional layer. The feature layers are then stacked to obtain a large feature layer, and the preoperative planning result is output.

[0079] In one embodiment, the preoperative planning results include: the location, direction and type of screws for surgical placement;

[0080] The screw model includes screw type, screw diameter, and screw length, all of which correspond to the patient's spinal bone density.

[0081] Figure 4 This is a schematic diagram of the preoperative planning device for spinal deformity correction based on deep learning provided in one embodiment of this application;

[0082] This deep learning-based preoperative planning device for spinal deformity correction includes:

[0083] The information acquisition module 401 is used to acquire the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; wherein, the patient's identity information includes at least gender and age;

[0084] The high-quality case screening module 402 is used to screen out the best-matching high-quality cases based on the patient's identity information, spinal Cobb angle, spinal bone density and spinal offset distance using big data. The high-quality cases record the patient's identity information, preoperative spinal Cobb angle, spinal bone density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance and the screw position, screw direction and screw type of the surgical screw placement.

[0085] The preoperative planning module 403 is used to input the best matching high-quality case, the patient's spinal CT image, and spinal bone density into the preset preoperative planning model for spinal deformity correction, and output the preoperative planning results.

[0086] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0087] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0088] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0089] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to an electronic device. In a particular embodiment, memory 502 may be a non-volatile solid-state memory.

[0090] In one embodiment, memory 502 may be 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 flash memory, or a combination of two or more of these.

[0091] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the deep learning-based preoperative planning methods for spinal deformity correction in the above embodiments.

[0092] In one example, the electronic device may also include a communication interface 503 and a bus 510. Wherein, as... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0093] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0094] Bus 510 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0095] Furthermore, in conjunction with the deep learning-based preoperative planning method for spinal deformity correction described in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the deep learning-based preoperative planning methods for spinal deformity correction described in the above embodiments.

[0096] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0097] 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, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" 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, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0098] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. 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 embodiments, or in a different order, or several steps can be performed simultaneously.

[0099] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0100] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A deep learning-based preoperative planning method for spinal deformity correction, characterized in that, include: Obtain the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; the patient's identity information includes at least gender and age; Based on patient identification information, spinal Cobb angle, spinal bone mineral density, and spinal offset distance, big data screening identifies the most suitable high-quality cases for the patients. These high-quality cases include the patient's identification information, preoperative spinal Cobb angle, spinal bone mineral density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance, and the location, direction, and type of screws used for surgical placement. The big data screening process identifies the most suitable high-quality cases for the patients based on their identification information, spinal Cobb angle, spinal bone mineral density, and spinal offset distance, including: based on patient identification information, spinal Cobb angle, spinal bone mineral density, and spinal offset distance... Bone density and spinal offset distance are used to construct a multidimensional feature vector for the patient; similarity calculation is performed on the patient's multidimensional feature vector in a medical database; based on the similarity calculation results, the best-matching high-quality cases are identified; the selection of the best-matching high-quality cases includes: selecting the top N cases with the highest similarity to the current patient based on the similarity calculation results; and performing a secondary screening based on the treatment effect, rehabilitation progress, and complication occurrence of the top N cases with the highest similarity to the current patient, selecting the case with the best treatment effect and the best rehabilitation as the best-matching high-quality case for the patient. The process involves inputting the most suitable high-quality case, the patient's spinal CT image, and spinal bone density into a pre-defined preoperative planning model for spinal deformity correction, and outputting the preoperative planning result. This includes: integrating the most suitable high-quality case, the patient's spinal CT image, and spinal bone density into a multi-dimensional input vector; constructing a neural network model, which includes convolutional layers, pooling layers, a copy layer of the convolutional layer, and a feature layer corresponding to the copy layer; obtaining the corresponding feature layer by upsampling and adding the copy layer with the corresponding layer in the convolutional layer; and superimposing the feature layers to obtain a large feature layer, which is then output as the preoperative planning result.

2. The preoperative planning method for spinal deformity correction based on deep learning according to claim 1, characterized in that, Obtain patient identification information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance, including: Obtain the patient's identity information through the patient's medical card or ID card; The patient's spinal region was scanned to obtain spinal CT images; Based on the patient's spinal CT images, the patient's spinal Cobb angle, spinal bone density, and spinal offset distance were determined.

3. The preoperative planning method for spinal deformity correction based on deep learning according to claim 2, characterized in that, Based on the patient's spinal CT images, determine the patient's spinal bone density and spinal offset distance, including: Input the patient's spinal CT images into a preset bone mineral density measurement and osteoporosis diagnosis model, and output the bone mineral density value and osteoporosis diagnosis result; Among them, the diagnostic results for osteoporosis include: normal, osteopenia, and osteoporosis; The bone mineral density measurement and osteoporosis diagnosis model is obtained through model training. It includes multiple DenseNet networks. Each DenseNet network consists of multiple dense blocks. The input of each dense block is connected to the output of all the previous dense blocks to form dense connections, which promotes the flow of information and gradients and mitigates gradient vanishing. By analyzing spinal CT images, the midline of the spine is extracted as a reference line; Based on the extracted spinal midline, the lateral displacement of each vertebra relative to the midline is measured by calculating the offset distance of each segment of the spine.

4. The preoperative planning method for spinal deformity correction based on deep learning according to claim 1, characterized in that, Preoperative planning results include: the location, direction, and type of screws for surgical placement; The screw model includes screw type, screw diameter, and screw length, all of which correspond to the patient's spinal bone density.

5. A preoperative planning device for spinal deformity correction based on deep learning, characterized in that, The device comprises: The information acquisition module is used to acquire the patient's identity information, spinal CT images, spinal Cobb angle, spinal bone density, and spinal offset distance; among which, the patient's identity information includes at least gender and age; The high-quality case screening module is used to identify the most suitable high-quality cases for patients based on their identity information, spinal Cobb angle, spinal bone density, and spinal offset distance using big data analysis. These high-quality cases include the patient's identity information, preoperative spinal Cobb angle, spinal bone density, preoperative spinal offset distance, postoperative spinal Cobb angle, postoperative spinal offset distance, and the location, direction, and type of screws used for surgical placement. The module further utilizes big data analysis to identify the most suitable high-quality cases for patients based on their identity information, spinal Cobb angle, spinal bone density, and spinal offset distance, including: [details of patient information and spinal Cobb angle]. A multidimensional feature vector for the patient is constructed using BB angle, spinal bone density, and spinal offset distance. Similarity calculations are then performed on this multidimensional feature vector in a medical database. Based on the similarity calculation results, the best-matching high-quality cases are identified. This process includes: selecting the top N cases with the highest similarity to the current patient based on the similarity calculation results; and further screening based on the treatment effects, rehabilitation progress, and complication occurrences of the top N most similar cases to the current patient, selecting the case with the best treatment effect and rehabilitation status as the best-matching high-quality case. The preoperative planning module is used to input the best-matched high-quality case, the patient's spinal CT image, and spinal bone density into a preset preoperative planning model for spinal deformity correction, and output the preoperative planning result. This includes: integrating the best-matched high-quality case, the patient's spinal CT image, and spinal bone density into a multi-dimensional input vector; constructing a neural network model, which includes convolutional layers, pooling layers, a copy layer of the convolutional layer, and a feature layer corresponding to the copy layer; obtaining the corresponding feature layer by upsampling and adding the copy layer with the corresponding layer in the convolutional layer; and superimposing the feature layers to obtain a large feature layer, which is then output as the preoperative planning result.

6. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the deep learning-based preoperative planning method for spinal deformity correction as described in any one of claims 1-4.

7. 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 preoperative planning method for spinal deformity correction as described in any one of claims 1-4.

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