Intelligent Management System and Method for the Rehabilitation of Patients after Spinal Surgery

Abstract

The present invention discloses an intelligent management system and method for the rehabilitation of patients after spinal surgery, which relates to the technical field of postoperative rehabilitation management of spinal surgery. The real-time analysis of the displacement characteristics of the implant by this system can early warn of the potential deviation of the implant. The first analysis module can calculate the implant risk index Fx, so as to prompt timely correction or adjustment, reduce the risk of implant deviation, and reduce the incidence of postoperative complications. The second analysis module is specifically used to extract the wear characteristics of the implant, construct the implant wear index Ms, and help doctors intervene in a timely manner by real-time monitoring the wear condition of the implant, so as to avoid the dysfunction or risk caused by the wear of the implant. The third analysis module calculates the comprehensive fusion index ZRh by extracting the change characteristics of the bone graft area, bone density and rehabilitation bone healing information, which helps doctors understand the bone density change and rehabilitation condition of the patient, so as to adjust the treatment plan and improve the rehabilitation effect.

Description

Technical Field

[0001] The present invention relates to the technical field of spinal surgery postoperative rehabilitation management, and specifically to an intelligent management system and method for the rehabilitation of patients after spinal surgery. Background Art

[0002] Spinal fractures and lumbar disc herniation are common spinal diseases that usually require surgery to restore the stability and function of the spine. During these operations, doctors often need to implant various implants (such as screws, metal rods, artificial vertebrae, etc.) to help fix the fracture site and correct the normal position of the spine. The use of implants is intended to provide adequate support, promote bone healing, and relieve patients' symptoms. However, traditional technologies face some challenges in this process. Although the use of implants can provide the necessary stability, it also brings the risk of uneven bone fusion quality. Bone fusion is a key step in postoperative rehabilitation of the spine, and its quality directly affects the patient's rehabilitation effect. The fixation and bone healing process of implants in the body is complex and affected by many factors, including the material and position of the implant, and the patient's physiological conditions. Traditional technologies are usually difficult to accurately evaluate the quality of bone fusion, which may lead to uneven fusion between the implant and the bone, affecting the final rehabilitation treatment effect.

[0003] Implants may shift after surgery due to the patient's bad exercise habits or improper postoperative rehabilitation. For example, strenuous exercise or improper posture may cause the implant to loosen or shift after surgery. This shift will not only affect the correction effect of the spine, but may also cause further pain, nerve damage or surgical failure. Traditional technologies have limited ability to monitor and warn of these potential risks, and often rely on regular follow-up examinations and subjective feedback from patients, making it difficult to detect and deal with problems in a timely manner. In traditional spinal surgery rehabilitation management, risk assessment and management mainly rely on the doctor's experience and the patient's self-perception. Although doctors can judge the condition of the implant based on postoperative imaging examinations and clinical symptoms, this method often cannot provide sufficient data support and real-time warnings. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent management system and method for the rehabilitation of patients after spinal surgery to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent management system for postoperative rehabilitation of spinal surgery patients, including a surgery process module, a postoperative rehabilitation data acquisition module, a first analysis module, a second analysis module and a third analysis module;

[0006] The surgical process module is used to collect medical information of spinal correction patients before and during surgery and input it into the hospital backend operating system, and compile it into a patient surgical data set, which is finally distributed to the spinal correction patient terminal;

[0007] The postoperative rehabilitation data acquisition module is used to collect rehabilitation data sets of spinal correction patients and transmit them to the terminal, wherein the rehabilitation data sets include spinal correction surgery information, spinal alignment, alignment, bone density rehabilitation, bone healing information and fixed structure displacement information of spinal correction patients;

[0008] The first analysis module is used to extract the displacement characteristics of implant screws in the corrective spinal surgery information, the alignment and alignment characteristics of the patient's spine, so as to obtain the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz; and perform calculation analysis, establish model training, and obtain the implant risk index Fx through training calculation. If the implant risk index Fx exceeds the preset first risk threshold value X, the first warning instruction is triggered, indicating that the spinal correction patient needs to be corrected again;

[0009] The recommended steps for model training are: Collect a large amount of labeled data, including implant screw displacement features in corrective spinal surgery information. These data are collected in real time through CT imaging medical equipment, sensors, and intelligent monitoring systems. Clean, integrate, transform, and normalize the collected raw data. Select the most informative features for the prediction task through statistical methods, correlation analysis, or feature importance evaluation such as tree-based feature importance. Select appropriate deep learning models based on the nature of the task such as classification, regression, or sequence prediction, the characteristics and scale of the data, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), or transformer networks (Transformer). Based on the selected model architecture, build model training on the rehabilitation data set. This includes defining the hierarchy, setting parameters, and choosing loss functions and optimizers. Input the prepared data into the model, and optimize the model parameters through backpropagation and optimization algorithms such as stochastic gradient descent to enable it to more accurately predict the target variable. And adjust the model's hyperparameters, such as learning rate, batch size, and number of iterations, to improve the model's generalization ability and training efficiency. Choose appropriate evaluation metrics such as accuracy, precision, recall, and F1 score to evaluate the performance of the model on the test set. Use cross-validation techniques such as K-fold cross-validation to verify the stability and generalization ability of the model on different data subsets. Deploy the trained model to actual applications, whether it is deployed on a cloud server, local server, or edge device;

[0010] The second analysis module is used to extract implant wear characteristics from the rehabilitation data set and construct an implant wear index Ms. If the implant wear index Ms exceeds a wear threshold M, a second warning instruction is issued;

[0011] The third analysis module is used to extract the bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz, and associate the bone graft area density change index mdbhz with the implant risk index Fx and the implant wear index Ms. After dimensionless processing, the comprehensive fusion index ZRh is calculated by the following formula:

[0012] ;

[0013] Wherein, Gxstj represents the volume of the bone absorption area, Gzstj represents the volume of the bone regeneration area, s, j, t, z and f represent the preset proportional coefficients of the volume of the bone absorption area Gxstj, the volume of the bone regeneration area Gzstj, the density change index of the bone grafting area mdbhz, the implant risk index Fx and the implant wear index Ms, respectively. Expressed as the first correction constant;

[0014] The comprehensive fusion index ZRh is evaluated to obtain corresponding evaluation results and strategies, which are transmitted to the spinal correction patient terminal and the hospital backend operating system.

[0015] Preferably, the first analysis module comprises a pre-processing unit and an implant displacement analysis unit;

[0016] The implant displacement analysis unit is used to pre-process the rehabilitation data set, the pre-processing steps include data integrity processing, data consistency processing and data standardization processing, and image data standardization, so that the resolution and format of all CT / MRI images and X-ray films are consistent;

[0017] The rehabilitation data set includes patient CT image data, anteroposterior and lateral spinal X-rays, clinical examination data, implant details, detailed scans of different parts of the spine, and physiological and biochemical test results data;

[0018] The implant offset analysis unit is used to perform in-depth analysis based on the rehabilitation data set to calculate: the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz and the implant relative displacement value xdwy.

[0019] Preferably, the screw trajectory angle change difference Jdcz is obtained by extracting the three-dimensional position of the screw implanted after surgery in the patient's CT image data, and using the image processing software 3D-Slicer or Mimics to extract the initial screw trajectory angle , the initial trajectory angles are relative to the sagittal and coronal planes;

[0020] After a fixed rehabilitation period, the patient's CT image data is acquired again and the screw secondary trajectory angle is extracted ; and calculate the screw trajectory angle change difference Jdcz using the following formula:

[0021] ;

[0022] The method for obtaining the intervertebral disc height difference Lc is as follows: extract the anterior intervertebral disc height after surgery in the patient's CT image data, select the minimum distance between adjacent vertebrae, and obtain the first intervertebral disc height value After a fixed rehabilitation period, the patient's CT image data is acquired again to obtain the second intervertebral disc height value. , calculate the intervertebral disc height difference Lcz according to the following formula;

[0023] ;

[0024] The vertebral rotation angle change difference Ztcz is obtained by extracting the three-dimensional position of the cone implanted after surgery in the patient's CT image data, and using the image processing software 3DSlicer or Mimics to extract the initial rotation angle of the cone After a fixed recovery period, the patient's CT image data is acquired again to obtain the secondary cone rotation angle. ; Calculate the vertebral rotation angle change difference Ztcz according to the following formula:

[0025] ;

[0026] The relative displacement value xdwy of the implant is obtained by using the image processing software 3DSlicer or Mimics to extract the three-dimensional coordinates of the implant placed after the first surgery and the three-dimensional coordinate features of the implant monitored after the second surgery, and the relative displacement value xdwy of the implant is calculated by the following formula;

[0027] ;

[0028] In the formula, , , represents the three-dimensional coordinate value of the implant placed after the first operation. , , Represents the three-dimensional coordinate value of the implant during the second postoperative monitoring.

[0029] Preferably, the first analysis module further includes a finite element analysis unit, which is used to calculate and analyze the adjacent cone stress change value Ylbhz based on the patient's CT image data, and the steps of obtaining the adjacent cone stress change value Ylbhz are as follows:

[0030] The patient's CT image data set was converted into a three-dimensional model using the image processing software Mimics. The corresponding material quantity was assigned to the spine and adjacent structural tissues. The three-dimensional model was divided into finite element meshes. The stress distribution in the mesh was deeply calculated using the finite element analysis software. The stress distribution value was calculated using the following formula :

[0031] ;

[0032] In the formula, is the stress value along the x-axis, is the stress value along the y-axis, is the stress value along the z-axis, is the shear stress between the x-axis and the y-axis, is the shear stress between the y-axis and the z-axis, is the shear stress between the z-axis and the x-axis;

[0033] And according to the stress distribution value , the adjacent stress change value Ylbhz of the cone is calculated by the following formula:

[0034] ;

[0035] ;

[0036] In the formula, represents the stress change difference at the i-th cone position, represents the stress value of the ith measurement point after surgery, represents the stress value of the ith measurement point before surgery, and n is the number of measurement points.

[0037] Preferably, the first analysis module further includes a first association unit and a first evaluation unit;

[0038] The first association unit is used to generate an implant risk index Fx by associating the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz through the following association formula:

[0039] ;

[0040] In the formula, , , and Indicates the weight value, the specific value is adjusted and set by the user. Expressed as the second correction constant.

[0041] Preferably, the first evaluation unit is used to preset a first risk threshold value X, and compare and analyze the implant risk index Fx with the first risk threshold value X to analyze whether the implant needs to be re-corrected, including:

[0042] When the implant risk index Fx is less than the first risk threshold X, it is judged that the patient's implant position is normal after surgery, and there are no abnormal complications affecting the patient's stress and spinal torsion function.

[0043] When the implant risk index Fx ≥ the first risk threshold X, it is judged that the patient's implant position is abnormal; it is judged that the patient's implant has the risk of displacement and loosening after surgery, and a first warning instruction is generated.

[0044] Preferably, the second analysis module includes an implant wear calculation unit and a second evaluation unit, wherein the implant wear calculation unit is used to extract implant wear characteristics from the patient's CT image data to obtain the number of implant surface cracks lhsl and the crack depth value lhsd, and generate the implant wear index Ms by calculating the following formula after dimensionless processing:

[0045] ;

[0046] Among them, E and represents the weight value, , ,and , the specific value is set by the user. Expressed as the third correction constant;

[0047] The second evaluation unit is used to preset a wear threshold M, and compare and evaluate the implant wear index Ms with the wear threshold M, including a second evaluation result, including:

[0048] When the implant wear index Ms is less than the wear threshold M, it is judged that the implant wear is normal after surgery and there is no abnormal complication on the patient's stress and spinal torsion function.

[0049] When the implant wear index Ms ≥ the wear threshold M, it is judged that the patient's implant wear is abnormal; it is judged that the patient's implant is at risk of cracks and breakage after surgery, and a second warning instruction is generated.

[0050] Preferably, the third analysis module includes a comprehensive calculation unit and a third evaluation unit;

[0051] The comprehensive calculation unit is used to extract bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz. The bone graft area density change index mdbhz is calculated and obtained by the following formula:

[0052] ;

[0053] In the formula, Indicates the change in bone density in the second stage of bone grafting area. It indicates the bone density value of the bone grafting area in the first stage after surgery. Changes in bone mass in the second stage of bone grafting area, Indicates the bone quality of the first stage of bone grafting after surgery. represents the second stage bone healing rate; w1, w2 and w3 represent weight values, and , , , whose specific value is set by the user, and ; and the bone graft area density change index mdbhz is associated with the implant risk index Fx and the implant wear index Ms to obtain the comprehensive fusion index ZRh.

[0054] Preferably, the third evaluation unit is used to preset a fusion threshold Y, and compare and analyze the comprehensive fusion index ZRh with the fusion threshold Y to obtain a third evaluation result, including:

[0055] When the comprehensive fusion index ZRh is less than the fusion threshold Y, it is judged that the bone fusion quality is abnormal during the patient's postoperative rehabilitation stage, and a third warning instruction is generated for treatment intervention, including increasing the intensity of core muscle training and flexibility exercise physical therapy time by 20%, adjusting the diet plan, modifying the rehabilitation plan, and drug-assisted treatment to promote bone healing;

[0056] When the comprehensive fusion index ZRh ≥ fusion threshold Y, it is judged that the quality of bone fusion has reached the standard during the patient's postoperative rehabilitation stage, and the current rehabilitation plan should be continued for training.

[0057] An intelligent management method for rehabilitation of patients after spinal surgery, comprising:

[0058] Collect medical information of spinal correction patients before and during surgery, and compile surgical data to form a patient surgery data set;

[0059] Collect postoperative rehabilitation data of spinal correction patients and establish a rehabilitation data set, which includes spinal correction surgery information, spinal alignment, bone density rehabilitation, bone healing information and fixed structure displacement information of spinal correction patients;

[0060] Extract the displacement characteristics of implant screws in the corrective spinal surgery information, the alignment and alignment characteristics of the patient's spine, so as to obtain the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz; perform calculation analysis, establish model training, and obtain the implant risk index Fx through training calculation. If the implant risk index Fx exceeds the preset first risk threshold X, the first warning instruction is triggered, indicating that the spinal correction patient needs to be corrected again;

[0061] Extract implant wear features from the rehabilitation data set and construct an implant wear index Ms. If the implant wear index Ms exceeds the wear threshold M, issue a second warning instruction;

[0062] The bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics are extracted from the rehabilitation data set to obtain the bone graft area density change index mdbhz, and the bone graft area density change index mdbhz is associated with the implant risk index Fx and the implant wear index Ms to obtain the comprehensive fusion index ZRh and evaluate it, and obtain the corresponding evaluation results and strategies, which are transmitted to the spinal correction patient terminal and the hospital background operating system.

[0063] The present invention provides an intelligent management system and method for the rehabilitation of patients after spinal surgery. It has the following beneficial effects:

[0064] This intelligent management method for postoperative rehabilitation of spinal surgery patients collects comprehensive medical information before, during and after surgery and during rehabilitation to form a detailed patient surgical data set and rehabilitation data set. This integration ensures the accurate summary of all relevant information and provides a solid data foundation for subsequent analysis. Extracting and analyzing the characteristics of screw displacement, intervertebral height change, vertebral rotation angle change and implant relative displacement, combined with finite element analysis to calculate stress changes, can accurately evaluate the implant risk index Fx. If the implant risk index Fx exceeds the preset first risk threshold X, the first warning instruction will be triggered, indicating that the spinal correction patient needs to be corrected again, and potential problems will be discovered and dealt with in time to reduce the risk of postoperative complications. By extracting the wear characteristics of the implant and calculating the implant wear index Ms, the damage of the implant can be identified in time. If the implant wear index Ms exceeds the wear threshold M, the second warning instruction is issued to ensure the effectiveness of the implant and the safety of the patient and prevent further problems caused by wear. Extracting the change characteristics of the bone graft area, bone density and bone healing information, and evaluating the quality of bone healing through the comprehensive fusion index ZRh. This comprehensive evaluation can adjust the rehabilitation plan in real time and provide personalized intervention measures, such as adjusting the training intensity, to promote the optimization of rehabilitation effects. The analysis results and evaluation strategies are transmitted to the patient terminal and the hospital back-end operating system to ensure real-time monitoring and feedback. This real-time feedback mechanism helps doctors and patients understand the rehabilitation status in a timely manner and make adjustments as needed, thereby improving rehabilitation efficiency and overall patient satisfaction.

[0065] Traditional technology has limitations in evaluating the quality of bone fusion, while this system can monitor changes in the bone fusion process in real time and provide a more accurate assessment of the quality of bone fusion through comprehensive collection and analysis of postoperative rehabilitation data. This real-time feedback helps to promptly detect uneven bone fusion or other problems, thereby optimizing the rehabilitation plan, ensuring uniform levels of bone fusion, and improving rehabilitation effects. Traditional technology relies on regular inspections and subjective feedback from patients to monitor changes in the position of implants, while this system can provide early warning of potential implant deviations through real-time analysis of implant displacement characteristics. The first analysis module of the system can calculate the implant risk index Fx and issue a warning instruction when it exceeds the preset threshold, thereby prompting timely correction or adjustment, reducing the risk of implant deviation, and reducing the incidence of postoperative complications.

[0066] The second analysis module of this system is specifically used to extract the wear characteristics of implants and construct the implant wear index Ms. By monitoring the wear of implants in real time, the system can issue an early warning when the wear exceeds the threshold, helping doctors to intervene in time and avoid functional disorders or risks caused by implant wear.

[0067] The third analysis module calculates the comprehensive fusion index ZRh by extracting the characteristics of bone graft area changes, bone density and rehabilitation bone healing information. This comprehensive evaluation can fully reflect the relationship between the density changes in the bone graft area and the implant-related indexes, and provide more accurate rehabilitation evaluation and strategy recommendations. The evaluation of the comprehensive fusion index helps doctors understand the patient's bone density changes and rehabilitation status, so as to adjust the treatment plan and improve the rehabilitation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a schematic diagram of the flowchart of the intelligent management system for postoperative rehabilitation of patients with spinal surgery of the present invention;

[0069] Figure 2 This is a schematic diagram of the steps of the intelligent management method for rehabilitation of patients after spinal surgery of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Example 1, please refer to Figure 1 , the present invention provides an intelligent management system for the rehabilitation of patients after spinal surgery, including a surgery process module, a postoperative rehabilitation data acquisition module, a first analysis module, a second analysis module and a third analysis module;

[0072] The surgical process module is used to collect medical information of spinal correction patients before and during surgery and input it into the hospital backend operating system, and compile it into a patient surgical data set, which is finally distributed to the spinal correction patient terminal;

[0073] The postoperative rehabilitation data collection module is used to collect rehabilitation data sets of spinal correction patients and transmit them to the terminal, wherein the rehabilitation data sets include spinal correction surgery information, spinal alignment, alignment, bone density rehabilitation, bone healing information and fixed structure displacement information of spinal correction patients;

[0074] The first analysis module is used to extract the displacement characteristics of implant screws in the corrective spinal surgery information, the alignment and alignment characteristics of the patient's spine, so as to obtain the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz; and perform calculation analysis, establish model training, and obtain the implant risk index Fx through training calculation. If the implant risk index Fx exceeds the preset first risk threshold value X, the first warning instruction is triggered, indicating that the spinal correction patient needs to be corrected again;

[0075] The second analysis module is used to extract implant wear characteristics from the rehabilitation data set and construct an implant wear index Ms. If the implant wear index Ms exceeds a wear threshold M, a second warning instruction is issued;

[0076] The third analysis module is used to extract the bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz, and associate the bone graft area density change index mdbhz with the implant risk index Fx and the implant wear index Ms. After dimensionless processing, the comprehensive fusion index ZRh is calculated by the following formula:

[0077] ;

[0078] Wherein, Gxstj represents the volume of the bone absorption area, Gzstj represents the volume of the bone regeneration area, s, j, t, z and f represent the preset proportional coefficients of the volume of the bone absorption area Gxstj, the volume of the bone regeneration area Gzstj, the density change index of the bone grafting area mdbhz and the implant risk index Fx and the implant wear index Ms, respectively, which are adjusted and set by the user, and the sum of the preset proportional coefficients is 1. Expressed as the first correction constant;

[0079] The comprehensive fusion index ZRh is evaluated to obtain corresponding evaluation results and strategies, which are transmitted to the spinal correction patient terminal and the hospital backend operating system.

[0080] In this embodiment, traditional technology has limitations in evaluating the quality of bone fusion, and this system can monitor the changes in the bone fusion process in real time and provide a more accurate assessment of the quality of bone fusion through comprehensive collection and analysis of postoperative rehabilitation data. This real-time feedback helps to timely detect uneven bone fusion or other problems, thereby optimizing the rehabilitation plan, ensuring uniform levels of bone fusion, and improving the rehabilitation effect. Traditional technology relies on regular inspections and subjective feedback from patients to monitor changes in the position of implants, while this system can provide early warning of potential implant deviations through real-time analysis of implant displacement characteristics. The first analysis module of the system can calculate the implant risk index Fx and issue a warning instruction when it exceeds a preset threshold, thereby prompting timely correction or adjustment, reducing the risk of implant deviation, and reducing the incidence of postoperative complications.

[0081] The second analysis module of this system is specifically used to extract the wear characteristics of implants and construct the implant wear index Ms. By monitoring the wear of implants in real time, the system can issue an early warning when the wear exceeds the threshold, helping doctors to intervene in time and avoid functional disorders or risks caused by implant wear.

[0082] The third analysis module calculates the comprehensive fusion index ZRh by extracting the change characteristics of the bone graft area, bone density and rehabilitation bone healing information. This comprehensive evaluation can fully reflect the relationship between the density changes in the bone graft area and the implant-related indexes, and provide more accurate rehabilitation evaluation and strategic recommendations. The evaluation of the comprehensive fusion index helps doctors understand the patient's bone density changes and rehabilitation status, so as to adjust the treatment plan and improve the rehabilitation effect. Through systematic data collection and analysis, doctors can make more accurate decisions based on the data. This data-driven approach not only improves the scientific nature of the treatment, but also reduces the interference of human factors and improves the overall level of treatment.

[0083] Example 2, please refer to Figure 1 , the first analysis module includes a preprocessing unit and an implant displacement analysis unit;

[0084] The implant deviation analysis unit is used to pre-process the rehabilitation data set, wherein the pre-processing includes data integrity processing, data consistency processing and data standardization processing, and image data standardization makes the resolution and format of all CT / MRI images and X-ray films consistent;

[0085] The rehabilitation data set includes patient CT image data, anteroposterior and lateral spinal X-rays, clinical examination data, implant details, detailed scans of different parts of the spine, and physiological and biochemical test results data;

[0086] The implant offset analysis unit is used to perform in-depth analysis based on the rehabilitation data set to calculate: the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz and the implant relative displacement value xdwy.

[0087] The screw trajectory angle change difference Jdcz is obtained by extracting the three-dimensional position of the screw implanted after surgery from the patient's CT image data, and using the image processing software 3D-Slicer or Mimics to extract the initial screw trajectory angle , the initial trajectory angles are relative to the sagittal and coronal planes;

[0088] After a fixed rehabilitation period, the patient's CT image data is acquired again and the screw secondary trajectory angle is extracted The screw trajectory angle difference Jdcz is calculated by the following formula:

[0089] ;

[0090] The method for obtaining the intervertebral disc height difference Lc is as follows: extract the anterior intervertebral disc height after surgery in the patient's CT image data, select the minimum distance between adjacent vertebrae, and obtain the first intervertebral disc height value After a fixed rehabilitation period, the patient's CT image data is acquired again to obtain the second intervertebral disc height value. , calculate the intervertebral disc height difference Lcz according to the following formula;

[0091] ;

[0092] The vertebral rotation angle change difference Ztcz is obtained by extracting the three-dimensional position of the cone implanted after surgery in the patient's CT image data, and using the image processing software 3DSlicer or Mimics to extract the initial rotation angle of the cone After a fixed recovery period, the patient's CT image data is acquired again to obtain the secondary cone rotation angle. ; Calculate the vertebral rotation angle change difference Ztcz according to the following formula:

[0093] ;

[0094] The relative displacement value xdwy of the implant is obtained by using the image processing software 3DSlicer or Mimics to extract the three-dimensional coordinates of the implant placed after the first surgery and the three-dimensional coordinate features of the implant monitored after the second surgery, and the relative displacement value xdwy of the implant is calculated by the following formula;

[0095] ;

[0096] In the formula, , , represents the three-dimensional coordinate value of the implant placed after the first operation, , , Represents the three-dimensional coordinate value of the implant during the second postoperative monitoring.

[0097] The first analysis module further includes a finite element analysis unit, which is used to calculate and analyze the adjacent cone stress change value Ylbhz based on the patient's CT image data, wherein the steps of obtaining the adjacent cone stress change value Ylbhz are as follows:

[0098] The patient's CT image data set was converted into a three-dimensional model using the image processing software Mimics. The corresponding material quantity was assigned to the spine and adjacent structural tissues. The three-dimensional model was divided into finite element meshes. The stress distribution in the mesh was deeply calculated using the finite element analysis software. The stress distribution value was calculated using the following formula :

[0099] ;

[0100] In the formula, is the stress value along the x-axis, is the stress value along the y-axis, is the stress value along the z-axis, is the shear stress between the x-axis and the y-axis, is the shear stress between the y-axis and the z-axis, is the shear stress between the z-axis and the x-axis;

[0101] And according to the stress distribution value , the adjacent stress change value Ylbhz of the cone is calculated by the following formula:

[0102] ;

[0103] ;

[0104] In the formula, represents the stress change difference at the i-th cone position, represents the stress value of the ith measurement point after surgery, represents the stress value of the ith measurement point before surgery, and n is the number of measurement points.

[0105] In this embodiment, the calculation of the screw trajectory angle change difference Jdcz can monitor the stability of the implant after surgery in real time. If the screw trajectory changes significantly, it may indicate that the implant is displaced or loose, so that timely measures can be taken to adjust or reset it to reduce the risk of postoperative complications. The calculation of the intervertebral height change difference Lcz reflects the bone healing between the vertebrae. By comparing the intervertebral height after surgery and during the rehabilitation period, it can be evaluated whether the bone fusion is normal, thereby optimizing the rehabilitation plan and improving the quality of postoperative recovery. The calculation of the vertebral rotation angle change difference Ztcz can help doctors determine whether spinal correction has achieved the expected effect. If the vertebral rotation angle changes abnormally, the treatment plan may need to be re-evaluated and adjusted. The calculation of the relative displacement value xdwy of the implant can accurately reflect whether the implant has shifted after surgery. This helps to identify and deal with the offset of the implant in a timely manner, reduce the impact on spinal stability, and reduce patient postoperative discomfort and complications. Calculation of the adjacent cone stress change value Ylbhz Calculation of the adjacent cone stress change value by finite element analysis can understand the stress distribution of the implant on the surrounding bone tissue. Abnormal stress changes may indicate that the implant is not distributing load on the bone evenly, which could lead to fractures or inflammation problems.

[0106] Example 3, please refer to Figure 1 , the first analysis module further includes a first association unit and a first evaluation unit;

[0107] The first association unit is used to generate an implant risk index Fx by associating the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz through the following association formula:

[0108] ;

[0109] In the formula, , , and Indicates the weight value, the specific value is adjusted by the user, and the sum of the weight values ​​is 1. Expressed as the second correction constant.

[0110] The first evaluation unit is used to preset a first risk threshold value X, and compare and analyze the implant risk index Fx with the first risk threshold value X to analyze whether the implant needs to be corrected again, including:

[0111] When the implant risk index Fx is less than the first risk threshold X, it is judged that the patient's implant position is normal after surgery, and there are no abnormal complications affecting the patient's stress and spinal torsion function.

[0112] When the implant risk index Fx ≥ the first risk threshold X, it is judged that the patient's implant position is abnormal; it is judged that the patient's implant has the risk of displacement and loosening after surgery, and a first warning instruction is generated.

[0113] In this embodiment, the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the relative displacement value xdwy of the implant and the adjacent stress change value Ylbhz of the cone are comprehensively generated to generate the implant risk index Fx, which can provide a comprehensive postoperative risk assessment. This comprehensive assessment method can more accurately reflect the overall state of the implant than considering a certain indicator alone. By adjusting the weight value of each indicator, the risk assessment standard can be customized according to the specific situation and the different needs of the patient. This flexibility allows application in different clinical scenarios, enhancing the adaptability and accuracy of the system. By setting the first risk threshold value X and comparing it with the implant risk index Fx, the abnormal situation of the implant can be effectively identified. If the risk index exceeds the preset threshold, the system will generate a first warning instruction, prompting the doctor to further check or adjust the implant. This timely warning mechanism can significantly reduce the risk of postoperative complications. When the implant risk index is lower than the first risk threshold value X, the system will judge that the implant position is normal, avoiding unnecessary intervention. This precise risk management reduces the patient's additional examination and treatment, reduces medical costs, and improves the patient's postoperative comfort.

[0114] Example 4, please refer to Figure 1 The second analysis module includes an implant wear calculation unit and a second evaluation unit. The implant wear calculation unit is used to extract implant wear characteristics from the patient's CT image data to obtain the number of implant surface cracks lhsl and the crack depth value lhsd. After dimensionless processing, the implant wear index Ms is calculated by the following formula:

[0115] ;

[0116] Among them, E and represents the weight value, , ,and , the specific value is set by the user. Expressed as the third correction constant;

[0117] The second evaluation unit is used to preset a wear threshold M, and compare and evaluate the implant wear index Ms with the wear threshold M, including a second evaluation result, including:

[0118] When the implant wear index Ms is less than the wear threshold M, it is judged that the patient's implant wear is normal after surgery, and there are no abnormal complications on the patient's stress and spinal torsion function, thus avoiding unnecessary examinations and interventions. This helps reduce unnecessary burdens on patients and reduces medical costs.

[0119] When the implant wear index Ms ≥ the wear threshold M, it is judged that the patient's implant wear is abnormal; it is judged that the patient's implant is at risk of cracks and breakage after surgery, and a second warning instruction is generated.

[0120] In this embodiment, by extracting the number of cracks on the implant surface lhsl and the crack depth value lhsd, and using dimensionless processing to calculate the implant wear index Ms, the surface state of the implant can be accurately evaluated. This detailed analysis can accurately monitor the actual wear of the implant. By comparing the implant wear index Ms with the preset wear threshold M, the wear of the implant can be discovered in time. If the implant wear index Ms exceeds the wear threshold M, the system will generate a second early warning instruction, prompting the doctor to conduct further inspection and intervention. This early warning mechanism can effectively avoid complications caused by implant wear. The data support provided by the second evaluation unit can help doctors make more accurate postoperative management decisions. By detecting the wear of the implant, the doctor can adjust the rehabilitation plan according to the actual situation to ensure the long-term stability and effectiveness of the implant.

[0121] Example 5, please refer to Figure 1 , the third analysis module includes a comprehensive calculation unit and a third evaluation unit;

[0122] The comprehensive calculation unit is used to extract bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz, wherein the bone graft area density change index mdbhz is calculated and obtained by the following formula:

[0123] ;

[0124] In the formula, Indicates the change in bone density in the second stage of bone grafting area. It indicates the bone density value of the bone grafting area in the first stage after surgery. Changes in bone mass in the second stage of bone grafting area, Indicates the bone quality of the first stage of bone grafting after surgery. represents the second stage bone healing rate; w1, w2 and w3 represent weight values, and , , , whose specific value is set by the user, and ; and the bone graft area density change index mdbhz is associated with the implant risk index Fx and the implant wear index Ms to obtain the comprehensive fusion index ZRh.

[0125] The third evaluation unit is used to preset a fusion threshold Y, and compare and analyze the comprehensive fusion index ZRh with the fusion threshold Y to obtain a third evaluation result, including:

[0126] When the comprehensive fusion index ZRh is less than the fusion threshold Y, the patient's bone fusion quality is judged to be abnormal during the postoperative rehabilitation stage, and a third warning instruction is generated for therapeutic intervention, including increasing the intensity of core muscle training and flexibility exercise physical therapy time by 20%, adjusting the diet plan, modifying the rehabilitation plan, and using drug-assisted therapy to promote bone healing; this dynamic adjustment mechanism helps to optimize the rehabilitation plan and improve the rehabilitation effect.

[0127] When the comprehensive fusion index ZRh ≥ fusion threshold Y, it is judged that the bone fusion quality of the patient has reached the standard during the postoperative rehabilitation stage, and the current rehabilitation plan should be continued for training. The system will recommend continuing the current rehabilitation plan to avoid unnecessary adjustments. This personalized intervention measure helps reduce unnecessary intervention and burden on patients and ensure the smooth progress of the rehabilitation plan.

[0128] In this embodiment, by extracting the bone density and bone healing information characteristics of the bone graft area and calculating the density change index mdbhz of the bone graft area, the density change during the bone healing process can be accurately monitored. This helps to evaluate the rehabilitation effect of the bone graft area and ensure that the bone healing process meets expectations. The comprehensive fusion index ZRh is obtained by correlating the bone graft area density change index mdbhz with the implant risk index Fx and the implant wear index Ms. This comprehensive evaluation method can fully reflect the overall situation of bone healing and provide a more accurate assessment of the rehabilitation status.

[0129] Example 6, please refer to Figure 2 , a method for intelligent management of rehabilitation of patients after spinal surgery, comprising the following steps:

[0130] S1: Collect medical information of spinal correction patients before and during surgery, and compile surgical data to form a patient surgery data set;

[0131] S2: Collect postoperative rehabilitation data of spinal correction patients and establish a rehabilitation data set, which includes spinal correction surgery information, spinal alignment, bone density rehabilitation, bone healing information and fixed structure displacement information of spinal correction patients;

[0132] S3: Extract the displacement characteristics of implant screws in the corrective spinal surgery information, the alignment and alignment characteristics of the patient's spine, to obtain the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz; perform calculation analysis, establish model training, and obtain the implant risk index Fx through training calculation. If the implant risk index Fx exceeds the preset first risk threshold X, the first warning instruction is triggered, indicating that the spinal correction patient needs to be corrected again;

[0133] S4: extracting implant wear features from the rehabilitation data set, constructing an implant wear index Ms, and issuing a second warning instruction if the implant wear index Ms exceeds a wear threshold M;

[0134] S5: Extract the bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz, and associate the bone graft area density change index mdbhz with the implant risk index Fx and the implant wear index Ms, obtain the comprehensive fusion index ZRh and evaluate it, obtain the corresponding evaluation results and strategies, and transmit them to the spinal correction patient terminal and the hospital background operating system.

[0135] In this embodiment, S1-S5, by collecting comprehensive medical information before, during and after surgery and during rehabilitation, a detailed patient surgical data set and rehabilitation data set are formed. This integration ensures the accurate summary of all relevant information and provides a solid data foundation for subsequent analysis. Extracting and analyzing the characteristics of screw displacement, intervertebral height change, vertebral rotation angle change and implant relative displacement, combined with finite element analysis to calculate stress changes, the implant risk index Fx can be accurately evaluated. If the implant risk index Fx exceeds the preset first risk threshold X, the first warning instruction will be triggered, indicating that the spinal correction patient needs to be corrected again, and potential problems will be discovered and dealt with in time to reduce the risk of postoperative complications. By extracting the wear characteristics of the implant and calculating the implant wear index Ms, the damage of the implant can be identified in time. If the implant wear index Ms exceeds the wear threshold M, a second warning instruction is issued to ensure the effectiveness of the implant and the safety of the patient, and prevent further problems caused by wear. Extract the change characteristics, bone density and bone healing information of the bone graft area, and evaluate the quality of bone healing through the comprehensive fusion index ZRh. This comprehensive evaluation can adjust the rehabilitation plan in real time, provide personalized intervention measures, such as adjusting the training intensity, and promote the optimization of rehabilitation effects. The analysis results and evaluation strategies are transmitted to the patient terminal and the hospital back-end operating system to ensure real-time monitoring and feedback. This real-time feedback mechanism helps doctors and patients understand the rehabilitation status in a timely manner and make adjustments as needed, thereby improving rehabilitation efficiency and overall patient satisfaction.

[0136] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0137] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. An intelligent management system for the rehabilitation of patients after spinal surgery, characterized in that: It includes a surgery process module, a postoperative rehabilitation data acquisition module, a first analysis module, a second analysis module and a third analysis module; The surgical process module is used to collect medical information of spinal correction patients before and during surgery and input it into the hospital backend operating system, and compile it into a patient surgical data set, which is finally distributed to the spinal correction patient terminal; The postoperative rehabilitation data collection module is used to collect rehabilitation data sets of spinal correction patients and transmit them to the terminal, wherein the rehabilitation data sets include spinal correction surgery information, spinal alignment, alignment, bone density rehabilitation, bone healing information and fixed structure displacement information of spinal correction patients; The first analysis module is used to extract the displacement characteristics of implant screws in the corrective spinal surgery information, the alignment and alignment characteristics of the patient's spine, so as to obtain the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz; and perform calculation analysis, establish model training, and obtain the implant risk index Fx through training calculation. If the implant risk index Fx exceeds the preset first risk threshold value X, the first warning instruction is triggered, indicating that the spinal correction patient needs to be corrected again; The second analysis module is used to extract implant wear characteristics from the rehabilitation data set and construct an implant wear index Ms. If the implant wear index Ms exceeds a wear threshold M, a second warning instruction is issued; The second analysis module includes an implant wear calculation unit and a second evaluation unit. The implant wear calculation unit is used to extract implant wear features from the patient's CT image data to obtain the number of implant surface cracks lhsl and the crack depth value lhsd, and generate an implant wear index Ms by calculating the following formula after dimensionless processing: ； in, and represents the weight value, Expressed as the third correction constant; The second evaluation unit is used to preset a wear threshold M, and compare and evaluate the implant wear index Ms with the wear threshold M, including a second evaluation result, including: When the implant wear index Ms is less than the wear threshold M, it is judged that the implant wear is normal after surgery and there is no abnormal complication on the patient's stress and spinal torsion function. When the implant wear index Ms ≥ the wear threshold M, it is determined that the patient's implant wear is abnormal; it is determined that the patient's implant has a risk of cracks and fractures after surgery, and a second warning instruction is generated; The third analysis module is used to extract the bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz, and associate the bone graft area density change index mdbhz with the implant risk index Fx and the implant wear index Ms. After dimensionless processing, the comprehensive fusion index ZRh is calculated by the following formula: ； Wherein, Gxstj represents the volume of the bone absorption area, Gzstj represents the volume of the bone regeneration area, s, j, t, z and f represent the preset proportional coefficients of the volume of the bone absorption area Gxstj, the volume of the bone regeneration area Gzstj, the density change index of the bone grafting area mdbhz, the implant risk index Fx and the implant wear index Ms, respectively. Expressed as the first correction constant; The comprehensive fusion index ZRh is evaluated to obtain corresponding evaluation results and strategies, which are transmitted to the spinal correction patient terminal and the hospital backend operating system.

2. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 1 is characterized in that: The first analysis module includes a pre-processing unit and an implant deviation analysis unit; The implant deviation analysis unit is used to pre-process the rehabilitation data set, wherein the pre-processing includes data integrity processing, data consistency processing and data standardization processing, and image data standardization makes the resolution and format of all CT / MRI images and X-ray films consistent; The rehabilitation data set includes patient CT image data, anteroposterior and lateral spinal X-rays, clinical examination data, implant details, detailed scans of different parts of the spine, and physiological and biochemical test results data; The implant offset analysis unit is used to perform in-depth analysis based on the rehabilitation data set to calculate: the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz and the implant relative displacement value xdwy.

3. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 2 is characterized in that: The screw trajectory angle change difference Jdcz is obtained by extracting the three-dimensional position of the screw implanted after surgery from the patient's CT image data, and using the image processing software 3D-Slicer or Mimics to extract the initial screw trajectory angle , the initial trajectory angles are relative to the sagittal and coronal planes; After a fixed rehabilitation period, the patient's CT image data is acquired again and the screw secondary trajectory angle is extracted The screw trajectory angle difference Jdcz is calculated by the following formula: ； The method for obtaining the intervertebral disc height difference Lc is as follows: extract the anterior intervertebral disc height after surgery in the patient's CT image data, select the minimum distance between adjacent vertebrae, and obtain the first intervertebral disc height value After a fixed rehabilitation period, the patient's CT image data is acquired again to obtain the second intervertebral disc height value. , calculate the intervertebral disc height difference Lcz according to the following formula; ； The vertebral rotation angle change difference Ztcz is obtained by extracting the three-dimensional position of the cone implanted after surgery in the patient's CT image data, and using the image processing software 3DSlicer or Mimics to extract the initial rotation angle of the cone After a fixed recovery period, the patient's CT image data is acquired again to obtain the secondary cone rotation angle. , calculate the vertebral rotation angle change difference Ztcz according to the following formula: ； The relative displacement value xdwy of the implant is obtained by using the image processing software 3DSlicer or Mimics to extract the three-dimensional coordinates of the implant placed after the first surgery and the three-dimensional coordinate features of the implant monitored after the second surgery, and the relative displacement value xdwy of the implant is calculated by the following formula; ； In the formula, , , represents the three-dimensional coordinate value of the implant placed after the first operation. , , Represents the three-dimensional coordinate value of the implant during the second postoperative monitoring.

4. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 2, characterized in that: The first analysis module further includes a finite element analysis unit, which is used to calculate and analyze the adjacent cone stress change value Ylbhz based on the patient's CT image data, wherein the steps of obtaining the adjacent cone stress change value Ylbhz are as follows: The patient's CT image data set was converted into a three-dimensional model using the image processing software Mimics. The corresponding material quantity was assigned to the spine and adjacent structural tissues. The three-dimensional model was divided into finite element meshes. The stress distribution in the mesh was deeply calculated using the finite element analysis software. The stress distribution value was calculated using the following formula : ； In the formula, is the stress value along the x-axis, is the stress value along the y-axis, is the stress value along the z-axis, is the shear stress between the x-axis and the y-axis, is the shear stress between the y-axis and the z-axis, is the shear stress between the z-axis and the x-axis; And according to the stress distribution value , the adjacent stress change value Ylbhz of the cone is calculated by the following formula: ； ； In the formula, represents the stress change difference at the i-th cone position, represents the stress value of the ith measurement point after surgery, represents the stress value of the ith measurement point before surgery, and n is the number of measurement points.

5. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 1, characterized in that: The first analysis module also includes a first association unit and a first evaluation unit; The first association unit is used to generate an implant risk index Fx by associating the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz through the following association formula: ； In the formula, , , and Indicates the weight value, the specific value is adjusted and set by the user. Expressed as the second correction constant.

6. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 5, characterized in that: The first evaluation unit is used to preset a first risk threshold value X, and compare and analyze the implant risk index Fx with the first risk threshold value X to analyze whether the implant needs to be corrected again, including: When the implant risk index Fx is less than the first risk threshold X, it is judged that the patient's implant position is normal after surgery, and there are no abnormal complications affecting the patient's stress and spinal torsion function. When the implant risk index Fx ≥ the first risk threshold X, it is judged that the patient's implant position is abnormal; it is judged that the patient's implant has the risk of displacement and loosening after surgery, and a first warning instruction is generated.

7. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 1, characterized in that: The third analysis module includes a comprehensive calculation unit and a third evaluation unit; The comprehensive calculation unit is used to extract bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics from the rehabilitation data set to obtain the bone graft area density change index mdbhz, wherein the bone graft area density change index mdbhz is calculated and obtained by the following formula: ； In the formula, Indicates the change in bone density in the second stage of bone grafting area. It indicates the bone density value of the bone grafting area in the first stage after surgery. Changes in bone mass in the second stage of bone grafting area, Indicates the bone quality of the first stage of bone grafting after surgery. represents the second stage bone healing rate; w1, w2 and w3 represent weight values, and , , , whose specific value is set by the user, and ; and the bone graft area density change index mdbhz is associated with the implant risk index Fx and the implant wear index Ms to obtain the comprehensive fusion index ZRh.

8. The intelligent management system for postoperative rehabilitation of spinal surgery patients according to claim 7, characterized in that: The third evaluation unit is used to preset a fusion threshold Y, and compare and analyze the comprehensive fusion index ZRh with the fusion threshold Y to obtain a third evaluation result, including: When the comprehensive fusion index ZRh is less than the fusion threshold Y, it is judged that the bone fusion quality is abnormal during the patient's postoperative rehabilitation stage, and a third warning instruction is generated for treatment intervention, including increasing the intensity of core muscle training and flexibility exercise physical therapy time by 20%, adjusting the diet plan, modifying the rehabilitation plan, and drug-assisted treatment to promote bone healing; When the comprehensive fusion index ZRh ≥ fusion threshold Y, it is judged that the quality of bone fusion has reached the standard during the patient's postoperative rehabilitation stage, and the current rehabilitation plan should be continued for training.

9. A method for intelligent management of postoperative rehabilitation of patients with spinal surgery, applied to the intelligent management system for postoperative rehabilitation of patients with spinal surgery according to any one of claims 1 to 8, characterized in that: include: Collect medical information of spinal correction patients before and during surgery, and compile surgical data to form a patient surgery data set; Collect postoperative rehabilitation data of spinal correction patients and establish a rehabilitation data set, which includes spinal correction surgery information, spinal alignment, bone density rehabilitation, bone healing information and fixed structure displacement information of spinal correction patients; Extract the displacement characteristics of implant screws in the corrective spinal surgery information, the alignment and alignment characteristics of the patient's spine, so as to obtain the screw trajectory angle change difference Jdcz, the intervertebral height change difference Lcz, the vertebral rotation angle change difference Ztcz, the implant relative displacement value xdwy and the cone adjacent stress change value Ylbhz; perform calculation analysis, establish model training, and obtain the implant risk index Fx through training calculation. If the implant risk index Fx exceeds the preset first risk threshold X, the first warning instruction is triggered, indicating that the spinal correction patient needs to be corrected again; Extract implant wear features from the rehabilitation data set and construct an implant wear index Ms. If the implant wear index Ms exceeds the wear threshold M, issue a second warning instruction; The bone graft area change characteristics, bone density and rehabilitation bone healing information characteristics are extracted from the rehabilitation data set to obtain the bone graft area density change index mdbhz, and the bone graft area density change index mdbhz is associated with the implant risk index Fx and the implant wear index Ms to obtain the comprehensive fusion index ZRh and evaluate it, and obtain the corresponding evaluation results and strategies, which are transmitted to the spinal correction patient terminal and the hospital background operating system.

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