Interbody fusion state real-time monitoring method and system based on intelligent fusion cage

Abstract

The invention provides an interbody fusion state real-time monitoring method, system and device based on an intelligent fusion cage and a computer readable storage medium. The interbody fusion state real-time monitoring method based on the intelligent fusion device comprises the steps that a posture recognition sensor arranged in the intelligent fusion device is used for recognizing the moving posture of an implantation part of the fusion device, and recognized moving posture data are sent to an external terminal; a stress sensor arranged on the surface of the intelligent fusion cage is used for collecting stress data of an implantation part of the fusion cage and sending the stress data to an external terminal; and the external terminal inputs the activity posture data and the stress data into a preset deep learning neural network model, and outputs an intervertebral fusion state monitoring result. According to the embodiment of the invention, the interbody fusion state can be monitored in real time more accurately.

Description

Technical Field

[0001] The present application belongs to the field of intervertebral fusion status monitoring, and in particular, relates to a real-time monitoring method, system, device and computer-readable storage medium for intervertebral fusion status based on an intelligent fusion device. Background Art

[0002] Anterior cervical decompression and fusion (ACDF) is currently the most commonly used surgical method for the treatment of cervical spondylosis and is considered the "gold standard" for the treatment of degenerative cervical spondylosis. The intervertebral fusion condition is closely related to the surgical effect.

[0003] As the research on uncovertebral fusion continues to deepen, the research team found that stress plays an important role in the fusion process and effect, and monitoring of stress helps to understand the status of intervertebral fusion. The applicant found that the existing technology has the following problems:

[0004] (1) The current finite element simulation stress method itself is limited and cannot be used for in vivo research. Therefore, there is a lack of direct stress monitoring evidence and research data, which cannot clearly guide clinicians to improve surgery and enhance treatment effects;

[0005] (2) During the operation and follow-up, the patient will undergo multiple imaging examinations to ensure satisfactory prosthesis posture and detect the postoperative fusion effect. However, excessive and repeated imaging and radiological examinations have been proven to affect the patient's physical health and waste social medical resources;

[0006] (3) Existing fusion devices are unable to provide monitoring data during use, which slows down the iteration and upgrade of intervertebral fusion devices.

[0007] Therefore, how to more accurately monitor the intervertebral fusion status in real time is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0008] The embodiments of the present application provide a method, system, device and computer-readable storage medium for real-time monitoring of intervertebral fusion status based on an intelligent fusion device, which can more accurately perform real-time monitoring of intervertebral fusion status.

[0009] In a first aspect, an embodiment of the present application provides a method for real-time monitoring of intervertebral fusion status based on an intelligent fusion device, comprising:

[0010] Using the posture recognition sensor set inside the intelligent fusion device, the activity posture of the fusion device implantation site is recognized, and the recognized activity posture data is sent to the external terminal;

[0011] The stress sensor provided on the surface of the intelligent fusion device is used to collect stress data of the implanted part of the fusion device, and the stress data is sent to an external terminal;

[0012] The external terminal inputs the activity posture data and stress data into the preset deep learning neural network model and outputs the intervertebral fusion status monitoring results.

[0013] Optionally, the external terminal inputs the activity posture data and stress data into a preset deep learning neural network model, and outputs intervertebral fusion status monitoring results, including:

[0014] Use the CNN network to perform multi-scale encoding on the activity posture data and stress data to obtain the CNN encoded feature map;

[0015] Use the Transformer network to perform multi-scale encoding on the CNN encoded feature map to obtain the Transformer encoded feature map;

[0016] Decode the feature map encoded by Transformer to obtain a decoded feature map;

[0017] Use the Attention Gate network and perform the Concat operation to fuse the encoded feature map with the decoded feature map to obtain the target feature map;

[0018] Based on the target feature map, the intervertebral fusion status monitoring results are obtained.

[0019] Optionally, a CNN network is used to perform multi-scale encoding on the activity posture data and stress data to obtain a CNN-encoded feature map, including:

[0020] Using the CNN network to perform multi-scale encoding on the activity posture data and stress data to obtain the first feature map;

[0021] Using the CNN network to perform multi-scale encoding on the first feature map to obtain a second feature map;

[0022] The CNN network is used to perform multi-scale encoding on the second feature map to obtain the third feature map.

[0023] Optionally, also include:

[0024] Using the Transformer network, the third feature map is multi-scale encoded to obtain the fourth feature map;

[0025] The fourth feature map is decoded to obtain a fifth feature map.

[0026] Optionally, a posture recognition sensor provided inside the intelligent fusion device is used to recognize the activity posture of the implanted part of the fusion device, including:

[0027] Start the posture recognition sensor in the intelligent fusion device to ensure that the sensor is in normal working condition;

[0028] Initialize the sensor and set parameters, including sensitivity, data sampling frequency, etc., to adapt to specific usage scenarios;

[0029] The sensor monitors the posture changes inside the fusion device in real time, detecting the direction, angle and position of movement;

[0030] The collected posture information is preliminarily processed, denoised or filtered to remove possible environmental interference;

[0031] converting the processed posture data into recognizable activity posture markers;

[0032] The built-in algorithm or model is used to analyze the gesture data after preliminary processing to identify the specific activity gesture.

[0033] Optionally, a stress sensor disposed on the surface of the intelligent fusion device is used to collect stress data of the implantation site of the fusion device, including:

[0034] Start the pressure sensor on the surface of the intelligent fusion device to ensure that the sensor is in normal working condition;

[0035] Initialize sensor parameters, including pressure measurement range, sampling frequency, and sensitivity, to adapt to the required measurement accuracy;

[0036] Calibrate the sensor to ensure accurate pressure data and eliminate possible interference from environmental factors;

[0037] The sensor detects the pressure changes at the implantation site of the fusion device in real time and records the changes in the pressure value;

[0038] Perform preliminary processing on the collected pressure data to remove noise or apply filtering algorithms to reduce interference;

[0039] The processed pressure data is converted into digital signals to facilitate subsequent data transmission and analysis.

[0040] Optionally, a displacement sensor is also provided inside the intelligent fusion device, and the displacement sensor is used to collect the activity displacement data of the fusion device at the implantation site.

[0041] In a second aspect, an embodiment of the present application provides a real-time monitoring system for intervertebral fusion status based on an intelligent fusion device, comprising:

[0042] An activity posture data acquisition module is used to use a posture recognition sensor provided inside the intelligent fusion device to recognize the activity posture of the implanted part of the fusion device, and send the recognized activity posture data to an external terminal;

[0043] A stress data acquisition module is used to collect stress data of the implanted part of the intelligent fusion device by using stress sensors arranged on the surface of the intelligent fusion device, and send the stress data to an external terminal;

[0044] The intervertebral fusion status monitoring module is used for the external terminal to input the activity posture data and stress data into the preset deep learning neural network model, and output the intervertebral fusion status monitoring results.

[0045] 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;

[0046] When the processor executes the computer program instructions, a real-time monitoring method for intervertebral fusion status based on an intelligent fusion device is implemented.

[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a method for real-time monitoring of intervertebral fusion status based on an intelligent fusion device is implemented.

[0048] The method, system, device and computer-readable storage medium for real-time monitoring of intervertebral fusion status based on the intelligent fusion device according to the embodiments of the present application can more accurately perform real-time monitoring of intervertebral fusion status.

[0049] The real-time monitoring method of intervertebral fusion status based on intelligent fusion device includes:

[0050] Using the posture recognition sensor set inside the intelligent fusion device, the activity posture of the fusion device implantation site is recognized, and the recognized activity posture data is sent to the external terminal;

[0051] The stress sensor provided on the surface of the intelligent fusion device is used to collect stress data of the implanted part of the fusion device, and the stress data is sent to an external terminal;

[0052] The external terminal inputs the activity posture data and stress data into the preset deep learning neural network model and outputs the intervertebral fusion status monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] 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.

[0054] Figure 1It is a flowchart of a method for real-time monitoring of intervertebral fusion status based on an intelligent fusion device provided by an embodiment of the present application;

[0055] Figure 2 It is a structural diagram of a deep learning neural network model provided by an embodiment of the present application;

[0056] Figure 3 It is a structural schematic diagram of a real-time monitoring system for intervertebral fusion status based on an intelligent fusion device provided by an embodiment of the present application;

[0057] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0058] 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.

[0059] 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.

[0060] 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 real-time monitoring of intervertebral fusion status based on an intelligent fusion device. The following first introduces the method for real-time monitoring of intervertebral fusion status based on an intelligent fusion device provided by the embodiments of the present application.

[0061] Figure 1 FIG. 1 is a flow chart of a method for real-time monitoring of intervertebral fusion status based on an intelligent fusion device provided by an embodiment of the present application. Figure 1As shown, the real-time monitoring method of intervertebral fusion status based on intelligent fusion device includes:

[0062] S101, using a posture recognition sensor provided inside the intelligent fusion device to recognize the activity posture of the implanted part of the fusion device, and sending the recognized activity posture data to an external terminal;

[0063] S102, using a stress sensor disposed on the surface of the intelligent fusion device to collect stress data of the implanted part of the fusion device, and sending the stress data to an external terminal;

[0064] S103: The external terminal inputs the activity posture data and stress data into a preset deep learning neural network model, and outputs the intervertebral fusion status monitoring result.

[0065] In one embodiment, the external terminal inputs the activity posture data and stress data into a preset deep learning neural network model, and outputs intervertebral fusion status monitoring results, including:

[0066] Use the CNN network to perform multi-scale encoding on the activity posture data and stress data to obtain the CNN encoded feature map;

[0067] Use the Transformer network to perform multi-scale encoding on the CNN encoded feature map to obtain the Transformer encoded feature map;

[0068] Decode the feature map encoded by Transformer to obtain a decoded feature map;

[0069] Use the Attention Gate network and perform the Concat operation to fuse the encoded feature map with the decoded feature map to obtain the target feature map;

[0070] Based on the target feature map, the intervertebral fusion status monitoring results are obtained.

[0071] Figure 2 It is a structural diagram of a deep learning neural network model provided by an embodiment of the present application;

[0072] In one embodiment, a CNN network is used to perform multi-scale encoding on the activity posture data and the stress data to obtain a CNN-encoded feature map, including:

[0073] Using the CNN network to perform multi-scale encoding on the activity posture data and stress data to obtain the first feature map;

[0074] Using the CNN network to perform multi-scale encoding on the first feature map to obtain a second feature map;

[0075] The CNN network is used to perform multi-scale encoding on the second feature map to obtain the third feature map.

[0076] In one embodiment, it further includes:

[0077] Using the Transformer network, the third feature map is multi-scale encoded to obtain the fourth feature map;

[0078] The fourth feature map is decoded to obtain a fifth feature map.

[0079] In one embodiment, a posture recognition sensor provided inside the intelligent fusion device is used to recognize the activity posture of the implanted part of the fusion device, including:

[0080] Start the posture recognition sensor in the intelligent fusion device to ensure that the sensor is in normal working condition;

[0081] Initialize the sensor and set parameters, including sensitivity, data sampling frequency, etc., to adapt to specific usage scenarios;

[0082] The sensor monitors the posture changes inside the fusion device in real time, detecting the direction, angle and position of movement;

[0083] The collected posture information is preliminarily processed, denoised or filtered to remove possible environmental interference;

[0084] converting the processed posture data into recognizable activity posture markers;

[0085] The built-in algorithm or model is used to analyze the gesture data after preliminary processing to identify the specific activity gesture.

[0086] In one embodiment, the stress data of the implantation site of the intelligent fusion device is collected by using the stress sensor disposed on the surface of the intelligent fusion device, including:

[0087] Start the pressure sensor on the surface of the intelligent fusion device to ensure that the sensor is in normal working condition;

[0088] Initialize sensor parameters, including pressure measurement range, sampling frequency, and sensitivity, to adapt to the required measurement accuracy;

[0089] Calibrate the sensor to ensure accurate pressure data and eliminate possible interference from environmental factors;

[0090] The sensor detects the pressure changes at the implantation site of the fusion device in real time and records the changes in the pressure value;

[0091] Perform preliminary processing on the collected pressure data to remove noise or apply filtering algorithms to reduce interference;

[0092] The processed pressure data is converted into digital signals to facilitate subsequent data transmission and analysis.

[0093] In one embodiment, a displacement sensor is also provided inside the intelligent fusion device, and the displacement sensor is used to collect the activity displacement data of the fusion device at the implantation site.

[0094] Figure 3 It is a structural schematic diagram of a real-time monitoring system for intervertebral fusion status based on an intelligent fusion device provided by an embodiment of the present application;

[0095] A real-time monitoring system for intervertebral fusion status based on an intelligent fusion device, the system comprising:

[0096] The activity posture data acquisition module 301 is used to use the posture recognition sensor provided inside the intelligent fusion device to recognize the activity posture of the implanted part of the fusion device, and send the recognized activity posture data to an external terminal;

[0097] The stress data acquisition module 302 is used to collect stress data of the implanted part of the intelligent fusion device by using the stress sensor provided on the surface of the intelligent fusion device, and send the stress data to an external terminal;

[0098] The intervertebral fusion status monitoring module 303 is used for the external terminal to input the activity posture data and stress data into a preset deep learning neural network model, and output the intervertebral fusion status monitoring result.

[0099] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown.

[0100] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0101] Specifically, the processor 401 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.

[0102] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 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, memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, memory 402 may be inside or outside of an electronic device. In a particular embodiment, memory 402 may be a non-volatile solid-state memory.

[0103] In one embodiment, the memory 402 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.

[0104] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the real-time monitoring methods for intervertebral fusion status based on the intelligent fusion device in the above embodiments.

[0105] In one example, the electronic device may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0106] The communication interface 403 is mainly used to implement communication between various modules, systems, units and / or devices in the embodiments of the present application.

[0107] Bus 410 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 410 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.

[0108] In addition, in combination with the real-time monitoring method of intervertebral fusion status based on intelligent fusion device in the above embodiments, the embodiment of the present application 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 real-time monitoring method of intervertebral fusion status based on intelligent fusion device in the above embodiments is implemented.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The above reference is according to the method of the embodiment of the present application, the flowchart and / or block diagram of the system and computer program product described various aspects of the present application.It should be understood that each square block in the flowchart and / or block diagram and the combination of each square block in the flowchart and / or block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing system, to produce a kind of machine, so that these instructions executed by the processor of the computer or other programmable data processing system enable the realization of the function / action specified in one or more square blocks of the flowchart and / or block diagram.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 can also be understood that each square block in the block diagram and / or the flowchart and the combination of the square blocks in the block diagram and / or the flowchart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0113] 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 real-time monitoring method for intervertebral fusion status based on intelligent fusion device, characterized in that: include: Using the posture recognition sensor set inside the intelligent fusion device, the activity posture of the fusion device implantation site is recognized, and the recognized activity posture data is sent to the external terminal; The stress sensor provided on the surface of the intelligent fusion device is used to collect stress data of the implanted part of the fusion device, and the stress data is sent to an external terminal; The external terminal inputs the activity posture data and stress data into the preset deep learning neural network model and outputs the intervertebral fusion status monitoring results.

2. The real-time monitoring method of intervertebral fusion status based on intelligent fusion device according to claim 1 is characterized in that: The external terminal inputs the activity posture data and stress data into the preset deep learning neural network model and outputs the intervertebral fusion status monitoring results, including: Use the CNN network to perform multi-scale encoding on the activity posture data and stress data to obtain the CNN encoded feature map; Use the Transformer network to perform multi-scale encoding on the CNN encoded feature map to obtain the Transformer encoded feature map; Decode the feature map encoded by Transformer to obtain a decoded feature map; Use the Attention Gate network and perform the Concat operation to fuse the encoded feature map with the decoded feature map to obtain the target feature map; Based on the target feature map, the intervertebral fusion status monitoring results are obtained.

3. The real-time monitoring method of intervertebral fusion status based on intelligent fusion device according to claim 2 is characterized in that: The CNN network is used to perform multi-scale encoding on the activity posture data and stress data to obtain the CNN encoded feature map, including: Using the CNN network to perform multi-scale encoding on the activity posture data and stress data to obtain the first feature map; Using the CNN network to perform multi-scale encoding on the first feature map to obtain a second feature map; The CNN network is used to perform multi-scale encoding on the second feature map to obtain the third feature map.

4. The real-time monitoring method of intervertebral fusion status based on intelligent fusion device according to claim 3 is characterized in that: Also includes: Using the Transformer network, the third feature map is multi-scale encoded to obtain the fourth feature map; The fourth feature map is decoded to obtain a fifth feature map.

5. The real-time monitoring method of intervertebral fusion status based on intelligent fusion device according to claim 1 is characterized in that: The posture recognition sensor set inside the intelligent fusion device is used to identify the activity posture of the fusion device implantation site, including: Start the posture recognition sensor in the intelligent fusion device to ensure that the sensor is in normal working condition; Initialize the sensor and set parameters, including sensitivity, data sampling frequency, etc., to adapt to specific usage scenarios; The sensor monitors the posture changes inside the fusion device in real time, detecting the direction, angle and position of movement; The collected posture information is preliminarily processed, denoised or filtered to remove possible environmental interference; converting the processed posture data into recognizable activity posture markers; The built-in algorithm or model is used to analyze the gesture data after preliminary processing to identify the specific activity gesture.

6. The real-time monitoring method of intervertebral fusion status based on intelligent fusion device according to claim 1 is characterized in that: The stress sensor set on the surface of the intelligent fusion device is used to collect stress data at the implantation site of the fusion device, including: Start the pressure sensor on the surface of the intelligent fusion device to ensure that the sensor is in normal working condition; Initialize sensor parameters, including pressure measurement range, sampling frequency, and sensitivity, to adapt to the required measurement accuracy; Calibrate the sensor to ensure accurate pressure data and eliminate possible interference from environmental factors; The sensor detects the pressure changes at the implantation site of the fusion device in real time and records the changes in the pressure value; Perform preliminary processing on the collected pressure data to remove noise or apply filtering algorithms to reduce interference; The processed pressure data is converted into digital signals to facilitate subsequent data transmission and analysis.

7. The real-time monitoring method of intervertebral fusion status based on intelligent fusion device according to claim 1, characterized in that: A displacement sensor is also provided inside the intelligent fusion device, which is used to collect the activity displacement data of the fusion device at the implantation site.

8. A real-time monitoring system for intervertebral fusion status based on intelligent fusion device, characterized in that: The system comprises: An activity posture data acquisition module is used to use a posture recognition sensor provided inside the intelligent fusion device to recognize the activity posture of the implanted part of the fusion device, and send the recognized activity posture data to an external terminal; A stress data acquisition module is used to collect stress data of the implanted part of the intelligent fusion device by using stress sensors arranged on the surface of the intelligent fusion device, and send the stress data to an external terminal; The intervertebral fusion status monitoring module is used for the external terminal to input the activity posture data and stress data into the preset deep learning neural network model, and output the intervertebral fusion status monitoring results.

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, the real-time monitoring method for intervertebral fusion status based on an intelligent fusion device according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the real-time monitoring method for intervertebral fusion status based on an intelligent fusion device according to any one of claims 1 to 7 is implemented.

Images