Spine implant design method and system using self-sensing characteristic of piezoelectric material

Abstract

The invention relates to the field of biomedicine, and discloses a spine implant design method and system using the self-sensing characteristic of a piezoelectric material, and the method comprises the steps: collecting spine three-dimensional image data and clinical diagnosis information, recognizing the spine lesion characteristic and the spine implantation part of a target patient, and determining the basic structure size of a spine implant; setting a perception sensitivity level of the spine implant in the body of the target patient to generate perception performance judgment conditions of the spine implant; analyzing the biocompatibility requirement of the spine implantation part, and identifying the biological adaptation mode of the piezoelectric material in the body of the target patient; and embedding a piezoelectric signal processing module in the spine implant, and executing parameter optimization processing of the spine implant in combination with the biological adaptation mode and the perception performance judgment condition to obtain a target spine implant. According to the invention, the working state of the postoperative implant and the rehabilitation progress of the spine can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to a spinal implant design method and system utilizing the self-sensing characteristics of piezoelectric materials, and belongs to the field of biomedicine. Background Art

[0002] Spinal implant design is a process of adaptive implant device planning and structural research and development based on multidisciplinary knowledge such as biomedical engineering and materials science, tailored to the patient's spinal structure. This process combines the patient's spinal imaging data with biomechanical analysis results, and utilizes computer-aided design technology to construct and optimize the implant's three-dimensional model to meet the patient's individual needs. However, during the spinal implant design process, if individual patient differences are not adequately considered or mechanical analysis is not accurate enough, it may trigger rejection reactions in patients, thereby affecting surgical outcomes and patient recovery. Therefore, the scientific nature and precision of spinal implant design are crucial to improving surgical success rates and protecting patient health.

[0003] Traditional spinal implant designs mainly use conventional biomaterials such as titanium alloy and polyetheretherketone (PEEK). Although these materials can be used in simple spinal repair scenarios, they can only provide static mechanical support or structural replacement. They lack the ability to sense stress and deformation state and cannot provide real-time feedback on the interaction between the implant and spinal tissue, making it difficult to accurately evaluate the working status of the implant and the recovery progress of the spine after surgery. Summary of the Invention

[0004] The present invention provides a spinal implant design method and system utilizing the self-sensing properties of piezoelectric materials, the main purpose of which is to accurately evaluate the working status of the implant and the rehabilitation progress of the spine after surgery.

[0005] To achieve the above objectives, the present invention provides a spinal implant design method utilizing the self-sensing properties of piezoelectric materials, comprising:

[0006] Acquiring a target patient in whom a spinal implant is to be implanted, collecting three-dimensional spinal imaging data and clinical diagnostic information of the target patient, identifying spinal lesion characteristics of the target patient based on the three-dimensional spinal imaging data and the clinical diagnostic information, and determining a spinal implantation site for the target patient;

[0007] defining a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the three-dimensional spinal imaging data, and determining a basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold;

[0008] setting a perception sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics, calculating a signal transmission efficiency and an energy consumption rate of the spinal implant according to the perception sensitivity level, and setting a perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate;

[0009] screening a piezoelectric material corresponding to the spinal implant based on the basic structure dimensions and the clinical diagnostic information, analyzing the biocompatibility requirements of the spinal implant site, and identifying a biocompatible pattern of the piezoelectric material in the target patient based on the biocompatibility requirements;

[0010] Based on the perception sensitivity level, the energy consumption rate and the bioadaptation pattern, a piezoelectric signal processing module is embedded in the spinal implant. Based on the piezoelectric signal processing module, combined with the bioadaptation pattern and the perception performance judgment conditions, parameter optimization processing of the spinal implant is performed to obtain a target spinal implant.

[0011] Optionally, identifying the spinal lesion characteristics of the target patient based on the spinal three-dimensional imaging data and the clinical diagnosis information includes:

[0012] Segmenting the vertebral regions of the target patient based on the three-dimensional spinal image data, and calculating bone density values ​​of different vertebral regions;

[0013] generating a vertebral bone density gradient distribution of the target patient using the bone density value;

[0014] identifying adjacent cones in the vertebral region and calculating relative displacements and rotation angles of the adjacent cones under the patient's motion state to obtain dynamic stability parameters;

[0015] Segmenting the nerve-peripheral tissue in the three-dimensional spinal column image data and simulating the stress distribution of the nerve-peripheral tissue;

[0016] generating a compression intensity heat map of the target patient according to the stress distribution;

[0017] extracting the serum test data of the target patient from the clinical diagnostic information to determine the inflammatory biomarkers and their concentrations of the target patient;

[0018] Constructing a spinal lesion topology map of the target patient by combining the vertebral bone density gradient distribution, the dynamic stability parameter, the compression intensity heat map, the inflammatory biomarker and its concentration;

[0019] The spinal lesion characteristics of the target patient are identified through the spinal lesion topology map.

[0020] Optionally, defining a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the spinal three-dimensional imaging data includes:

[0021] Extracting core anatomical parameters of the spinal implant site from the spinal three-dimensional image data;

[0022] Extracting key lesion indicators and serum marker data from the clinical diagnosis information;

[0023] Calculating weighted correlation values ​​between the core anatomical parameters and the key lesion indicators;

[0024] generating a dynamic correlation coefficient matrix between the core anatomical parameters and the key lesion indicators according to the weighted correlation values;

[0025] outputting an initial adaptation threshold between the spinal implant and the spinal implant site based on the dynamic correlation coefficient matrix;

[0026] extracting serum bone metabolism markers and inflammatory indicators from the serum marker data;

[0027] setting a real-time adaptation correction variable between the spinal implant and the spinal implant site according to the serum bone metabolism marker and the inflammatory index;

[0028] The real-time adaptation correction variable and the initial adaptation threshold are integrated to define a spinal adaptation threshold between the spinal implant and the spinal implant site.

[0029] Optionally, determining the basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold comprises:

[0030] Based on the spinal adaptation threshold, identifying a critical spinal region in the target patient;

[0031] Extracting key threshold parameters from the spinal adaptation threshold and acquiring CT image data of the key spinal region;

[0032] Executing parameter fusion processing of the key threshold parameters and the CT image data to generate a personalized parameter association data set;

[0033] Establishing a digital twin model of the spine of the target patient based on the personalized parameter association dataset;

[0034] Calculating a quantitative index of stress distribution uniformity and a quantitative index of micro-friction loss of the spinal implant in the target patient through the spinal digital twin model;

[0035] According to the quantitative index of stress distribution uniformity and the quantitative index of fretting friction loss, the biocompatibility adaptation coefficient between the spinal implant and the target patient is calculated by the following formula:

[0036]

[0037] Where R represents the biocompatibility coefficient between the spinal implant and the target patient, represents the standard deviation of the quantitative index of stress distribution uniformity, represents the average value of the quantitative index of stress distribution uniformity, The loss coefficient represents the quantitative index of fretting friction loss, Indicates the critical threshold corresponding to the loss coefficient of the quantitative indicator of micro-friction loss, represents the attenuation coefficient, represents the local bone elastic modulus of the target patient, Indicates the elastic modulus of the spinal implant material;

[0038] Based on the biocompatibility adaptation coefficient, screening out the optimal size combination of the spinal implant in the target patient;

[0039] The basic structural size of the spinal implant in the target patient is determined according to the optimal size combination.

[0040] Optionally, the step of setting a sensing sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics includes:

[0041] Locating the target patient's diseased vertebral segment according to the spinal lesion characteristics, and extracting a lesion activity index of the diseased vertebral segment;

[0042] calculating a mechanical fragility score of the diseased vertebral segment, and dividing the perception dimension of the spinal implant according to the mechanical fragility score;

[0043] calibrating a minimum signal capture threshold of the spinal implant according to the lesion activity index and the mechanical fragility score;

[0044] Determining the differential regulatory weights of the perceptual dimensions through principal component analysis;

[0045] The differential adjustment weight, the minimum signal capture threshold, and the perception dimension are combined to generate a perception sensitivity level of the spinal implant.

[0046] Optionally, screening out a piezoelectric material corresponding to the spinal implant according to the basic structure size and the clinical diagnosis information includes:

[0047] Calculating the maximum allowable volume and load-bearing contact area of ​​the spinal implant based on the basic structure dimensions;

[0048] determining a material thickness range and an elastic modulus threshold of the spinal implant based on the maximum allowable volume and the load-bearing contact area;

[0049] Extracting the spinal lesion type and the patient's daily activity level classification from the clinical diagnosis information;

[0050] Establishing a correlation matrix between the spinal lesion type and the patient's daily activity level classification to output the piezoelectric response coefficient range and fatigue life threshold of the spinal implant;

[0051] setting material screening conditions for the spinal implant according to the piezoelectric response coefficient range and the fatigue life threshold;

[0052] Based on the material screening conditions, piezoelectric materials corresponding to the spinal implant are screened out.

[0053] Optionally, the identifying a biocompatible mode of the piezoelectric material in the target patient based on the biocompatibility requirement includes:

[0054] obtaining the spinal implant site and biological characteristics of the target patient;

[0055] Positioning the contact area of ​​the piezoelectric material with a predetermined vertebral endplate to obtain an implant-bone interface;

[0056] determining an elastic modulus threshold and an immune response threshold of the piezoelectric material according to the biological characteristics, and measuring a stiffness parameter of the piezoelectric material;

[0057] calculating a dynamic contact pressure at the implant-bone interface based on the elastic modulus threshold;

[0058] constructing a stiffness-inflammation coupling matrix of the implant-bone interface according to the immune response threshold and the stiffness parameter;

[0059] calculating the bioelectric efficiency of the implant-bone interface using the stiffness-inflammation coupling matrix;

[0060] Based on the bioelectric efficiency, a biocompatible mode of the piezoelectric material in the target patient is identified.

[0061] Optionally, embedding a piezoelectric signal processing module in the spinal implant based on the sensing sensitivity level, the energy consumption rate, and the bioadaptation mode includes:

[0062] Identifying a bioelectric signal threshold range corresponding to the perception sensitivity level and a stimulation parameter interval of the bioadaptation mode;

[0063] Analyzing the correlation between the perception sensitivity level and the bioadaptation mode based on the bioelectric signal threshold range and the stimulation parameter interval;

[0064] extracting spinal region signal characteristics and spinal anatomical parameters corresponding to the spinal implant;

[0065] According to the association relationship, the signal features of the spinal region are sorted in the time and frequency domains to obtain a signal capture priority sequence;

[0066] Setting a fluctuation tolerance threshold of the spinal implant using the energy consumption dynamic curve of the energy consumption efficiency;

[0067] parsing the impedance spectrum characteristics and characteristic frequencies in the bioadaptive mode to generate a collaborative optimization strategy for the spinal implant;

[0068] Combining the signal capture priority sequence, the fluctuation tolerance threshold, and the collaborative optimization strategy to construct an adaptive signal processing engine for the spinal implant;

[0069] A piezoelectric signal processing module is embedded in the spinal implant based on the spinal anatomical parameters and the adaptive signal processing engine.

[0070] Optionally, the performing of parameter optimization processing of the spinal implant based on the piezoelectric signal processing module in combination with the bioadaptation mode and the sensing performance judgment condition to obtain a target spinal implant includes:

[0071] Collecting multi-dimensional piezoelectric signals from the piezoelectric signal module in real time;

[0072] identifying a current adaptation state of the spinal implant based on the multi-dimensional piezoelectric signal;

[0073] According to the bio-adaptation mode, matching the parameter optimization strategy corresponding to the current adaptation state;

[0074] Based on the parameter optimization strategy, converting the perception performance judgment condition into specific control parameters of the piezoelectric signal processing module;

[0075] Based on the specific control parameters, performing parameter optimization processing of the piezoelectric signal processing module, and collecting feedback data of the spinal implant during the parameter optimization processing in real time;

[0076] Calculating the biocompatibility index and perceived performance improvement rate of the spinal implant under the parameter optimization strategy based on the feedback data;

[0077] According to the biocompatibility index and the perception performance improvement rate, dynamically adjusting the bioadaptation mode to generate an optimized bioadaptation strategy;

[0078] The optimized biofit strategy is fed back to the piezoelectric signal processing module to update the specific control parameters and obtain a target spinal implant.

[0079] In order to solve the above problems, the present invention further provides a spinal implant design system utilizing the self-sensing characteristics of piezoelectric materials, the system comprising:

[0080] a patient feature identification module, configured to obtain a target patient for whom a spinal implant is to be implanted, collect three-dimensional spinal imaging data and clinical diagnostic information of the target patient, identify the characteristics of the target patient's spinal lesions based on the three-dimensional spinal imaging data and the clinical diagnostic information, and determine a spinal implantation site for the target patient;

[0081] a spinal body adaptation module, configured to define a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the three-dimensional spinal imaging data, and determine a basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold;

[0082] a material screening condition definition module, configured to set a perception sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics, calculate a signal transmission efficiency and an energy consumption rate of the spinal implant based on the perception sensitivity level, and set a perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate;

[0083] a compatible material determination module, configured to select a piezoelectric material corresponding to the spinal implant based on the basic structure dimensions and the clinical diagnostic information, analyze the biocompatibility requirements of the spinal implant site, and identify a biocompatible pattern of the piezoelectric material in the target patient based on the biocompatibility requirements;

[0084] A target generation module is configured to embed a piezoelectric signal processing module in the spinal implant based on the perception sensitivity level, the energy consumption rate, and the bioadaptation pattern, and to perform parameter optimization processing of the spinal implant based on the piezoelectric signal processing module in combination with the bioadaptation pattern and the perception performance judgment condition to obtain a target spinal implant.

[0085] Compared with the problems described in the background technology, the embodiments of the present invention can ensure that the designed implant can not only effectively support the diseased spine, but also accurately capture the abnormal state of the diseased part through the self-sensing characteristics by identifying the spinal lesion characteristics of the target patient based on the three-dimensional spinal imaging data and the clinical diagnosis information. Furthermore, the embodiments of the present invention can ensure that the size and shape of the implant are accurately adapted to the implant site by determining the spinal implant site of the target patient based on the three-dimensional spinal imaging data and the clinical diagnosis information; the embodiments of the present invention can provide a method for benefit by defining the spinal adaptation threshold between the spinal implant and the spinal implant site according to the clinical diagnosis information and the three-dimensional spinal imaging data. The design of the spinal implant using the self-sensing characteristics of piezoelectric materials provides a clear adaptation standard to ensure that the implant matches the spinal implant site to achieve the expected effect. Furthermore, the embodiment of the present invention determines the basic structural dimensions of the spinal implant in the target patient's body based on the spinal adaptation threshold, thereby ensuring that the spinal implant matches the physiological structure of the patient's spine. The embodiment of the present invention sets the perception sensitivity level of the spinal implant in the target patient's body based on the spinal lesion characteristics, thereby enabling the spinal implant to perceive the abnormal mechanical changes of the patient's spinal lesion site in a targeted manner, thereby avoiding misjudgment due to improper perception sensitivity, and ensuring the patient's safety and rehabilitation effect after surgery. The embodiment of the present invention calculates the signal transmission efficiency and energy consumption rate of the spinal implant according to the perception sensitivity level, thereby ensuring that the spinal implant meets the patient's personalized monitoring needs while avoiding the loss of key data due to inefficient signal transmission. For example, at a high perception sensitivity level, by clarifying the signal transmission efficiency, it can ensure that the pressure, displacement and other signals collected at high frequency in the lesion area are accurately transmitted to the in vitro monitoring device; the embodiment of the present invention sets the perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate, thereby improving the utilization efficiency of the piezoelectric material's self-perception signal, and at the same time ensuring that the perception performance of the spinal implant is compatible with the characteristics of the piezoelectric material, thereby improving the perception performance of the spinal implant. The clinical practicality of the implant is improved. Furthermore, the embodiments of the present invention screen the piezoelectric material corresponding to the spinal implant based on the basic structure size and the clinical diagnostic information, thereby improving the compatibility of the piezoelectric material with the implant structure, and at the same time ensuring that the performance of the selected piezoelectric material meets clinical monitoring requirements, thereby improving the perceived reliability of the implant. The embodiments of the present invention ensure the stable function of the spinal implant by analyzing the biocompatibility requirements of the spinal implant site. Furthermore, the embodiments of the present invention identify the biocompatible mode of the piezoelectric material in the target patient based on the biocompatibility requirements, thereby achieving precise adaptation of the piezoelectric material performance to the individual biological characteristics of the patient.By embedding a piezoelectric signal processing module in the spinal implant based on the sensory sensitivity level, the energy consumption rate, and the bioadaptation pattern, the embodiment of the present invention can accurately correlate the piezoelectric material's self-sensing signal with the physiological activity of the spine, ensuring real-time monitoring of the mechanical state of the implant and the response of surrounding tissues. Furthermore, by performing parameter optimization processing on the spinal implant based on the piezoelectric signal processing module, combined with the bioadaptation pattern and the sensory performance judgment conditions, the embodiment of the present invention obtains a target spinal implant. This can enhance the comprehensiveness of spinal implant functionality and significantly reduce the limitations of traditional spinal implants that can only provide static support. It can also improve the sensory sensitivity of the interaction between the spinal implant and spinal tissue, thereby promoting the adjustment of personalized postoperative rehabilitation plans and improving the accuracy of spinal treatment. Therefore, the spinal implant design method and system utilizing the self-sensing characteristics of piezoelectric materials provided by the embodiment of the present invention can accurately assess the working status of the postoperative implant and the rehabilitation progress of the spine. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A schematic flow chart of a spinal implant design method utilizing the self-sensing characteristics of piezoelectric materials according to an embodiment of the present invention;

[0087] Figure 2 A schematic diagram of a spinal mechanical loading mode for a spinal implant design method utilizing the self-sensing characteristics of piezoelectric materials provided in one embodiment of the present invention;

[0088] Figure 3 A schematic diagram of a module for implementing a spinal implant design system utilizing the self-sensing characteristics of piezoelectric materials, provided in one embodiment of the present invention.

[0089] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0090] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0091] The present embodiment provides a spinal implant design method that utilizes the self-sensing properties of piezoelectric materials. The execution entity of the spinal implant design method that utilizes the self-sensing properties of piezoelectric materials includes, but is not limited to, at least one of electronic devices such as a server or a terminal that can be configured to execute the method provided by the present embodiment. In other words, the spinal implant design method that utilizes the self-sensing properties of piezoelectric materials can be executed by software or hardware installed on a terminal device or a server device. The server device includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0092] Reference Figure 1 FIG2 is a flow chart of a spinal implant design method utilizing the self-sensing properties of piezoelectric materials according to an embodiment of the present invention. In this embodiment, the spinal implant design method utilizing the self-sensing properties of piezoelectric materials includes:

[0093] S1. Acquire a target patient in whom a spinal implant is to be implanted, collect the target patient's spinal three-dimensional imaging data and clinical diagnostic information, identify the target patient's spinal lesion characteristics based on the spinal three-dimensional imaging data and the clinical diagnostic information, and determine the target patient's spinal implant site.

[0094] The embodiments of the present invention can ensure the targeted design direction of the spinal implant by obtaining the target patient for the spinal implant to be implanted. The spinal implant refers to a medical device that is implanted into the spine through surgery and is used to treat spinal diseases, injuries or deformities. Its main function is to provide structural support for the patient, correct deformities, stabilize the spine or promote bone healing. The target patient refers to an individual who has spinal-related lesions or injuries and needs to be treated or rehabilitated by implanting a spinal implant.

[0095] Furthermore, the embodiments of the present invention can provide data support for the subsequent precise design of spinal implants that utilize the self-sensing properties of piezoelectric materials by collecting the target patient's three-dimensional spinal imaging data and clinical diagnostic information. The three-dimensional spinal imaging data refers to digital image data obtained through imaging technologies such as CT and magnetic resonance imaging (MRI), which can three-dimensionally and clearly display the patient's entire spine and various vertebrae, intervertebral discs, spinal cord and other structures. The clinical diagnostic information refers to the sum of information about the patient's spinal disease formed by the doctor during the diagnosis and treatment of the target patient, including diagnosis results, cause analysis, symptom manifestations, disease progression, past medical history, surgical history and various examination results, etc., which can be obtained by reviewing the patient's electronic medical records and paper medical records.

[0096] The embodiment of the present invention identifies the spinal lesion characteristics of the target patient based on the three-dimensional spinal imaging data and the clinical diagnostic information, thereby ensuring that the designed implant can not only effectively support the diseased spine, but also accurately capture the abnormal state of the lesion site through self-sensing characteristics. The spinal lesion characteristics refer to the specific characteristics of the lesions in the target patient's spine in terms of anatomical structure, pathological mechanism, functional impact, etc., including the lesion site, morphological and structural changes, the cause and pathological process of the lesion, and the impact of the lesion on the spinal function and surrounding tissues.

[0097] As an embodiment of the present invention, the identifying the spinal lesion characteristics of the target patient based on the spinal three-dimensional image data and the clinical diagnosis information includes:

[0098] Segmenting the vertebral regions of the target patient based on the three-dimensional spinal image data, and calculating bone density values ​​of different vertebral regions;

[0099] generating a vertebral bone density gradient distribution of the target patient using the bone density value;

[0100] identifying adjacent cones in the vertebral region and calculating relative displacements and rotation angles of the adjacent cones under the patient's motion state to obtain dynamic stability parameters;

[0101] Segmenting the nerve-peripheral tissue in the three-dimensional spinal column image data and simulating the stress distribution of the nerve-peripheral tissue;

[0102] generating a compression intensity heat map of the target patient according to the stress distribution;

[0103] extracting the serum test data of the target patient from the clinical diagnostic information to determine the inflammatory biomarkers and their concentrations of the target patient;

[0104] Constructing a spinal lesion topology map of the target patient by combining the vertebral bone density gradient distribution, the dynamic stability parameter, the compression intensity heat map, the inflammatory biomarker and its concentration;

[0105] The spinal lesion characteristics of the target patient are identified through the spinal lesion topology map.

[0106] The vertebral region refers to the spatial range occupied by a single vertebra that can be clearly identified in a three-dimensional image of the spine, including structural regions such as the cortical bone and cancellous bone of the vertebra. The bone density value refers to the content of minerals (primarily calcium and phosphorus) in the vertebral bone per unit volume. It is an important indicator of bone strength and is usually expressed in grams per cubic centimeter. The vertebral bone density gradient distribution refers to the spatial distribution of bone density presented in the form of gradient changes after spatial interpolation of the bone density values ​​of different vertebral regions. It can intuitively demonstrate the differences and changing trends in bone density in different parts of the vertebra. Adjacent vertebrae refer to two adjacent vertebrae in the spinal sequence and connected by intervertebral discs and ligaments. The patient's movement state refers to the patient's body posture during spinal movements such as flexion, extension, lateral flexion, and rotation. The relative displacement refers to the positional change of adjacent vertebrae in the anterior-posterior, left-right, and up-down directions during movement. The rotation angle refers to the relative rotation angle of adjacent vertebrae around the longitudinal axis of the spine during movement. The dynamic stability parameter is an indicator that combines the relative displacement and rotation angle of adjacent vertebrae to quantitatively assess the stability of the spine during movement. Larger values ​​indicate poorer spinal stability. The "nerve-peripheral tissue" refers to the neural structures surrounding the spine (such as the spinal cord and nerve roots), as well as the vertebral bodies, intervertebral discs, ligaments, muscles, and other tissues adjacent to the nerves. The "stress distribution" refers to the spatial distribution of the interaction forces between the nerves and the peripheral tissues, obtained through simulation calculations, reflecting the magnitude and direction of forces acting on different parts of the body. The "compression intensity heat map" refers to a visualization of the stress distribution of the nerve-peripheral tissues in the form of a heat map. The "serum test data" refers to the levels and related indicators of various substances in the serum obtained through laboratory testing of patient blood samples. The "inflammatory biomarkers" and their concentrations refer to specific biomolecules in the serum (such as IL-6 and TNF-α) associated with spinal inflammatory responses and their levels in the serum. Their concentrations reflect the level of inflammatory activity. The "spinal lesion topology map" refers to a structured graph that comprehensively reflects the spatial distribution characteristics and interrelationships of spinal lesions by integrating multi-dimensional information such as vertebral bone density gradient distribution, dynamic stability parameters, compression intensity heat maps, and inflammatory biomarkers and their concentrations. This map clearly presents the overall condition of the lesions and the relationships between their characteristics.

[0107] Optionally, the vertebral bone density gradient distribution of the target patient can be generated using a spatial interpolation algorithm through the bone density value, the relative displacement and rotation angle of adjacent cones under the patient's motion state can be calculated by dynamic X-ray or 4D-CT imaging, the nerve-peripheral tissue in the three-dimensional spinal imaging data can be segmented by MRIT2 weighted images, and the stress distribution of the nerve-peripheral tissue can be simulated using finite element analysis. Combined with the vertebral bone density gradient distribution, the dynamic stability parameters, the compression intensity heat map, the inflammatory biomarkers and their concentrations, the spinal lesion topology map of the target patient can be constructed by multimodal registration technology.

[0108] Furthermore, the embodiments of the present invention can ensure that the size and shape of the implant are precisely matched to the implant site by determining the spinal implant site of the target patient based on the three-dimensional spinal imaging data and the clinical diagnostic information. The spinal implant site refers to the specific spinal segment or position where the spinal implant needs to be implanted, determined based on the spinal lesion condition, anatomical structure characteristics and treatment needs of the target patient, including specific vertebrae, intervertebral spaces or damaged areas of the spine, such as lumbar segments with vertebral compression fractures, cervical intervertebral spaces corresponding to intervertebral disc herniation, etc.

[0109] S2. Define a spinal adaptation threshold between the spinal implant and the spinal implant site based on the clinical diagnosis information and the spinal three-dimensional imaging data, and determine the basic structural dimensions of the spinal implant in the target patient based on the spinal adaptation threshold.

[0110] The embodiment of the present invention defines the spinal adaptation threshold between the spinal implant and the spinal implant site based on the clinical diagnostic information and the three-dimensional spinal imaging data, so as to provide a clear adaptation standard for the design of spinal implants that utilize the self-sensing characteristics of piezoelectric materials, thereby ensuring that the matching of the implant and the spinal implant site achieves the expected effect. The spinal adaptation threshold refers to the allowable error range and compliance standard for the spinal implant to match the spinal implant site in terms of size, shape, mechanical properties, etc., which is set based on factors such as the severity of the patient's spinal lesions and spinal functional requirements.

[0111] As an embodiment of the present invention, defining a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the spinal three-dimensional image data includes:

[0112] Extracting core anatomical parameters of the spinal implant site from the spinal three-dimensional image data;

[0113] Extracting key lesion indicators and serum marker data from the clinical diagnosis information;

[0114] Calculating weighted correlation values ​​between the core anatomical parameters and the key lesion indicators;

[0115] generating a dynamic correlation coefficient matrix between the core anatomical parameters and the key lesion indicators according to the weighted correlation values;

[0116] outputting an initial adaptation threshold between the spinal implant and the spinal implant site based on the dynamic correlation coefficient matrix;

[0117] extracting serum bone metabolism markers and inflammatory indicators from the serum marker data;

[0118] setting a real-time adaptation correction variable between the spinal implant and the spinal implant site according to the serum bone metabolism marker and the inflammatory index;

[0119] The real-time adaptation correction variable and the initial adaptation threshold are integrated to define a spinal adaptation threshold between the spinal implant and the spinal implant site.

[0120] Among them, the core anatomical parameters refer to parameters that can reflect the key structural characteristics of the spinal implant site and have a significant impact on the adaptability of the implant, including the minimum diameter of the pedicle, the inclination angle of the vertebral ultimate plate, and the bone density distribution gradient. The key lesion indicators refer to key parameters that can reflect the severity of the target patient's spinal lesions and the demand for implants, including the degree of nerve compression and the dynamic instability index. The weighted correlation value refers to the correlation value obtained after weighted calculation of the core anatomical parameters and the key lesion indicators, wherein the weight of the correlation value can be determined by the expert Delphi method, such as the pedicle diameter weight 0.4, the nerve compression degree weight 0.3, the dynamic correlation coefficient matrix refers to a matrix composed of weighted correlation values ​​between multiple core anatomical parameters and multiple key lesion indicators, and the initial adaptation threshold refers to the size, angle, fixation method, etc. of the spinal implant preliminarily determined based on the dynamic correlation coefficient matrix. The basic range and standards for spinal implant site adaptation are as follows: the serum marker data refers to the content data of biological molecules related to spinal lesions and bone metabolism obtained by testing the serum of the target patient. The serum bone metabolism marker refers to a biological molecule present in the serum that can reflect the state of bone metabolism, such as β-CTX (cross-linked carboxyl terminal peptide of type I collagen), and its level changes can reflect the active level of bone resorption. The inflammatory index refers to an index related to spinal inflammatory response in the serum, such as IL-6 (interleukin-6), and its concentration can reflect the activity of inflammation. The real-time adaptation correction variable refers to a variable that adjusts the initial adaptation threshold based on the specific values ​​of serum bone metabolism markers and inflammatory indicators. For example, when β-CTX>0.8ng / mL, the bone-implant micro-motion threshold is tightened by 20%; when IL-6>10pg / mL, the use of an implant with an anti-inflammatory coating is recommended.

[0121] Optionally, the extraction of the core anatomical parameters of the spinal implant site from the three-dimensional spinal imaging data can be achieved using a deep learning segmentation algorithm, such as the 3DU-Net algorithm. The weighted correlation values ​​of the core anatomical parameters and the key indicators of the lesion are calculated using a random forest model. The serum bone metabolism markers in the serum marker data can be detected by electrochemiluminescence, and the inflammatory indicators can be detected using enzyme-linked immunosorbent assay (ELISA).

[0122] Furthermore, the embodiments of the present invention can ensure that the spinal implant matches the physiological structure of the patient's spine by determining the basic structural dimensions of the spinal implant in the target patient's body based on the spinal adaptation threshold. The basic structural dimensions refer to the most basic structural parameters that the spinal implant must have to adapt to the specific physiological characteristics of the target patient's spine, including but not limited to the length, width, height, curvature radius, aperture size, etc. of the implant.

[0123] As an embodiment of the present invention, determining the basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold includes:

[0124] Based on the spinal adaptation threshold, identifying a critical spinal region in the target patient;

[0125] Extracting key threshold parameters from the spinal adaptation threshold and acquiring CT image data of the key spinal region;

[0126] Executing parameter fusion processing of the key threshold parameters and the CT image data to generate a personalized parameter association data set;

[0127] Establishing a digital twin model of the spine of the target patient based on the personalized parameter association dataset;

[0128] Calculating a quantitative index of stress distribution uniformity and a quantitative index of micro-friction loss of the spinal implant in the target patient through the spinal digital twin model;

[0129] Calculating a biocompatibility adaptation coefficient between the spinal implant and the target patient based on the quantitative index of stress distribution uniformity and the quantitative index of fretting friction loss;

[0130] Based on the biocompatibility adaptation coefficient, screening out the optimal size combination of the spinal implant in the target patient;

[0131] The basic structural size of the spinal implant in the target patient is determined according to the optimal size combination.

[0132] Among them, the critical spinal area refers to a specific area in the target patient's spine where the pressure value exceeds the intervertebral pressure threshold, such as the area around the intervertebral space where pressure is concentrated due to intervertebral disc degeneration. The critical threshold parameters refer to core parameters extracted from the spinal adaptation threshold and closely related to the design of spinal implants, including dynamic strain amplitude (the strain change amplitude generated by the spine during movement) and frequency response characteristics (the response law of the spine to external forces of different frequencies). The CT image data refers to high-resolution CT scan data with a layer thickness of 0.5 mm in the critical area of ​​the target patient's spine. The parameter fusion processing refers to the process of multi-dimensional integration and correlation analysis of the critical threshold parameters. The personalized parameter association data set refers to a data set for the target patient individual formed after parameter fusion processing, which contains correlation information between mechanical properties (such as dynamic strain, frequency response) and anatomical structure (such as vertebral size, intervertebral space width). The spinal digital twin model refers to a virtual model constructed based on the personalized parameter association data set, which is highly consistent with the target patient's spine in terms of geometric structure and physical properties, and contains nonlinear characteristics of vertebrae, intervertebral discs and ligaments. The material properties of the spinal implant are designed to accurately simulate the mechanical behavior of the spine (such as stress deformation, motion trajectory, etc.) under different physiological activity states. The stress distribution uniformity quantification index is calculated using the spine digital twin model and is used to evaluate the uniformity of force distribution on the spinal implant within the target patient. A higher value indicates more uniform force distribution across the implant. The fretting friction loss quantification index reflects the degree of friction loss generated between the spinal implant and surrounding bone tissue or adjacent structures during fretting. A lower value indicates less friction and wear between the implant and surrounding tissue. The biocompatibility fit coefficient is calculated by combining the stress distribution uniformity quantification index and the fretting friction loss quantification index to measure the degree of fit between the spinal implant and the physiological environment of the target patient's spine. A higher coefficient indicates that the implant more closely meets the individual patient's needs in terms of mechanical compatibility and tissue adaptability. The optimal size combination refers to a set of dimensional parameter combinations that, after screening based on the biocompatibility fit coefficient, optimally matches the spinal implant to the target patient's spine. These include key dimensions such as the implant's length, width, height, and curvature.

[0133] Optionally, the CT image data of the key area of ​​the spine can be acquired by a 64-row spiral CT scanner, the parameter fusion processing of the key threshold parameters and the CT image data can be achieved using a spatial registration algorithm, the digital twin model of the spine of the target patient based on the personalized parameter association data set can be established using finite element analysis software, such as ANSYS software, and the optimal size combination of the spinal implant in the target patient based on the biocompatibility adaptation coefficient can be screened using the NSGA-II multi-objective optimization algorithm.

[0134] As another embodiment of the present invention, the biocompatibility adaptation coefficient between the spinal implant and the target patient is calculated by the following formula:

[0135]

[0136] Where R represents the biocompatibility coefficient between the spinal implant and the target patient, represents the standard deviation of the quantitative index of stress distribution uniformity, represents the average value of the quantitative index of stress distribution uniformity, The loss coefficient represents the quantitative index of fretting friction loss, Indicates the critical threshold corresponding to the loss coefficient of the quantitative indicator of micro-friction loss, represents the attenuation coefficient, represents the local bone elastic modulus of the target patient, Indicates the elastic modulus of the spinal implant material.

[0137] It should be noted that the above formula can combine the three elements of stress distribution, micro-motion loss, and stiffness matching to effectively cover the dynamic load conditions of the biocompatibility of the spinal implant and the target patient and maintain long-term stability. It is particularly important to explain that all terms in the formula are dimensionless. To avoid design failure, and Need to meet ≤ , It is used here to quantify the stiffness matching between spinal implants and target patients, and the ratio must meet 0.001≤ ≤0.1, the biocompatibility adaptation coefficient R is a dimensionless parameter with a value range of R∈[0,2], where the closer R is to 1, the better the biocompatibility. For example, if R , the spinal implant can achieve ideal biocompatibility with the target patient; if R≤1, it can be understood as stress concentration or too high implant stiffness, requiring design optimization; if R>1, it may indicate that the design over-optimizes a certain indicator, such as sacrificing stiffness matching in pursuit of extremely low stress, which will cause other problems, such as insufficient implant strength. In addition, The critical threshold corresponding to the loss coefficient, which represents the quantitative index of micro-friction loss, can be taken as 1.2 and is usually determined based on clinical statistical data. It can be determined by CT grayscale value calibration, Available through the material library.

[0138] S3. Based on the spinal lesion characteristics, set the perception sensitivity level of the spinal implant in the target patient's body, calculate the signal transmission efficiency and energy consumption rate of the spinal implant according to the perception sensitivity level, and set the perception performance judgment conditions of the spinal implant based on the signal transmission efficiency and the energy consumption rate.

[0139] By setting the perception sensitivity level of the spinal implant in the target patient based on the characteristics of the spinal lesion, the embodiment of the present invention can achieve targeted perception of abnormal mechanical changes in the patient's spinal lesion site by the spinal implant, avoid misjudgment due to inappropriate perception sensitivity, and ensure the patient's postoperative safety and rehabilitation effect. The perception sensitivity level refers to a grading standard for the spinal implant's perception of physical stimuli and physiological signals from the surrounding environment, set according to the characteristics of the patient's spinal lesion. For example, for patients with mild degenerative spondylitis, the perception sensitivity level can be set to a basic level (such as Level 2), requiring only perception of pressure changes ≥5 kPa, and the temperature monitoring accuracy can be relaxed to ±1°C.

[0140] As an embodiment of the present invention, setting the sensing sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics includes:

[0141] Locating the target patient's diseased vertebral segment according to the spinal lesion characteristics, and extracting a lesion activity index of the diseased vertebral segment;

[0142] calculating a mechanical fragility score of the diseased vertebral segment, and dividing the perception dimension of the spinal implant according to the mechanical fragility score;

[0143] calibrating a minimum signal capture threshold of the spinal implant according to the lesion activity index and the mechanical fragility score;

[0144] Determining the differential regulatory weights of the perceptual dimensions through principal component analysis;

[0145] The differential adjustment weight, the minimum signal capture threshold, and the perception dimension are combined to generate a perception sensitivity level of the spinal implant.

[0146] The diseased vertebral segment refers to the anatomical location of the spinal lesion determined based on the characteristics of the spinal lesion through medical imaging (such as CT / MRI) and biomechanical analysis. The lesion activity index refers to a parameter that quantifies the dynamic mechanical or biochemical activity of the lesion area, including the micro-displacement variation coefficient and the inflammatory factor gradient. The micro-displacement variation coefficient can be measured by an implantable piezoelectric sensor, and the inflammatory factor gradient can be obtained through body fluid testing. The mechanical fragility score refers to a quantitative value that comprehensively evaluates the mechanical stability of the diseased vertebral segment using bone density, stress distribution, and microstructural integrity. The calculation formula is: ,in, Indicates the peak stress of the diseased vertebral segment, It represents the bone yield strength of the diseased vertebral segment, S∈[0,10]. When S>5, it can be determined as a high-risk fragile area. The perception dimension refers to the physical quantity direction or frequency band that can be monitored by the sensor of the spinal implant. Among them, the perception dimension includes axial strain and acoustic emission signal. The corresponding dimension can be activated according to the mechanical fragility score, for example, the mechanical fragility score Multi-dimensional monitoring is enabled at 5:00. The minimum signal capture threshold refers to the minimum signal amplitude that the spinal implant sensor can effectively identify. It must be higher than system noise and cover early lesion signals. The principal component analysis method refers to extracting signal features with the largest variance in the perception dimension through dimensionality reduction. For example, the principal component analysis method can select principal components with a contribution rate greater than 85% as the basis for weight allocation after performing covariance matrix decomposition on historical monitoring data (such as axial strain and acoustic emission amplitude of 100 patients). The differentiated adjustment weight refers to the normalized value of the variance contribution rate of each perception dimension in the principal component analysis.

[0147] Optionally, based on the characteristics of the spinal lesion, the diseased vertebral segment of the target patient can be located using a three-dimensional image registration algorithm.

[0148] Furthermore, embodiments of the present invention calculate the signal transmission efficiency and energy consumption rate of the spinal implant based on the perception sensitivity level, thereby ensuring that the spinal implant meets the patient's personalized monitoring needs while avoiding the loss of critical data due to inefficient signal transmission. For example, at a high perception sensitivity level, by clarifying the signal transmission efficiency, it is possible to ensure that pressure, displacement, and other signals acquired at high frequency in the lesion area are accurately transmitted to an external monitoring device. The signal transmission efficiency refers to the efficiency with which the spinal implant effectively transmits acquired physiological / mechanical signals to an external receiving device. The energy consumption rate refers to the total energy consumed by the spinal implant to complete signal perception, processing, and transmission within a unit of time (e.g., one hour).

[0149] Optionally, according to the perception sensitivity level, the energy consumption rate of the spinal implant can be calculated by a linear power consumption model, and the calculation formula is: P= , (unit: μW), where P represents the energy consumption rate of the spinal implant, represents the baseline power consumption of the spinal implant, k represents the sensitivity coefficient corresponding to the perception sensitivity level, which can be calibrated through in vitro experiments, and L represents the perception sensitivity level.

[0150] As an embodiment of the present invention, calculating the signal transmission efficiency of the spinal implant according to the perception sensitivity level includes:

[0151] determining a monitoring distance of the spinal implant based on the sensing sensitivity level and calculating a transmission duty cycle of the spinal implant;

[0152] calculating a path loss of the spinal implant according to the monitoring distance;

[0153] identifying a transmit power of the spinal implant and determining a signal-to-noise ratio and a bit error rate of the spinal implant in combination with the transmit power and the path loss;

[0154] The signal transmission efficiency of the spinal implant is calculated based on the transmission duty cycle, the bit error rate, and the signal-to-noise ratio.

[0155] The monitoring distance refers to the spatial distance at which the spinal implant can effectively monitor and transmit signals, which can be calculated by the formula d=2-0.5L (unit: m), where d represents the monitoring distance and L represents the level of perception sensitivity. For example, when the level of perception sensitivity L is 2, the monitoring distance d=2-0.5×2=1. The transmission duty cycle refers to the ratio of the time the spinal implant uses to transmit signals per unit time to the total time, which can be adjusted according to the patient's spinal mobility. The transmission duty cycle can be calculated by the formula Z=0.2L, where Z represents the transmission duty cycle and L represents the level of perception sensitivity. For example, when When the perception sensitivity level L is 3, the transmission duty cycle Z = 0.2 × 3 = 0.6, that is, 60% of the time per unit time is used for signal transmission. The path loss refers to the signal power attenuation caused by factors such as absorption and reflection by obstacles on the propagation path (such as human tissue, bones, etc.) during the transmission process. The transmission power refers to the power output by the spinal implant when transmitting the signal, and the unit is usually microwatts (μW) or milliwatts (mW). The signal-to-noise ratio refers to the ratio of the useful signal power received by the receiving end to the noise power, including the muscle electrical noise interference term, and the unit is decibel (dB). The formula SNR = , where SNR represents the signal-to-noise ratio, P represents the transmit power, represents the path loss, represents the noise power at the receiving end, represents the myoelectric noise power, where It can be calibrated by electromyography (EMG) before surgery. The bit error rate refers to the ratio of the number of erroneous bits to the total number of transmitted bits in the transmitted binary data, reflecting the probability of data errors during signal transmission. The lower the bit error rate, the higher the accuracy of signal transmission. The formula B= Calculate, where B represents the bit error rate and SNR represents the signal-to-noise ratio.

[0156] Optionally, based on the monitoring distance, the path loss of the spinal implant can be calculated using the Friis formula, and the transmission power of the spinal implant can be determined by the rated output power of the spinal implant communication module.

[0157] The embodiment of the present invention sets the perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate, thereby improving the utilization efficiency of the self-perception signal of the piezoelectric material, and at the same time ensuring that the perception performance of the spinal implant is compatible with the characteristics of the piezoelectric material, thereby improving the clinical practicality of the implant. The perception performance judgment condition refers to a quantitative standard pre-set based on the signal transmission efficiency and energy consumption rate of the spinal implant to judge whether the perception performance of the spinal implant matches the actual application requirements. It can be set through a threshold judgment method. For example, when the signal transmission efficiency is ≥80% and the energy consumption rate is ≤250μW, the perception performance is judged to be up to standard; if the signal transmission efficiency is <80% or the energy consumption rate is >250μW, the perception performance is judged to be not up to standard.

[0158] S4. Based on the basic structure size and the clinical diagnosis information, screen the piezoelectric material corresponding to the spinal implant, analyze the biocompatibility requirements of the spinal implant site, and identify the biofit pattern of the piezoelectric material in the target patient based on the biocompatibility requirements.

[0159] The embodiment of the present invention screens the piezoelectric material corresponding to the spinal implant based on the basic structure size and the clinical diagnostic information, thereby improving the compatibility of the piezoelectric material with the implant structure, and at the same time ensuring that the performance of the selected piezoelectric material meets clinical monitoring requirements, thereby improving the sensing reliability of the implant. The piezoelectric material refers to a functional material that can realize the mutual conversion of mechanical energy and electrical energy. In the spinal implant, the piezoelectric material can convert the mechanical signals generated during spinal movement (such as pressure changes between vertebrae, deformation of the implant, etc.) into electrical signals to monitor the movement state, force conditions and mechanical changes of the spine in the diseased area. For example, when the patient moves the spine, the implant is subjected to pressure, and the piezoelectric material generates an electrical signal. By detecting the intensity and change pattern of the electrical signal, the force magnitude and movement amplitude of the spine can be indirectly known.

[0160] As an embodiment of the present invention, screening the piezoelectric material corresponding to the spinal implant according to the basic structure size and the clinical diagnosis information includes:

[0161] Calculating the maximum allowable volume and load-bearing contact area of ​​the spinal implant based on the basic structure dimensions;

[0162] determining a material thickness range and an elastic modulus threshold of the spinal implant based on the maximum allowable volume and the load-bearing contact area;

[0163] Extracting the spinal lesion type and the patient's daily activity level classification from the clinical diagnosis information;

[0164] Establishing a correlation matrix between the spinal lesion type and the patient's daily activity level classification to output the piezoelectric response coefficient range and fatigue life threshold of the spinal implant;

[0165] setting material screening conditions for the spinal implant according to the piezoelectric response coefficient range and the fatigue life threshold;

[0166] Based on the material screening conditions, piezoelectric materials corresponding to the spinal implant are screened out.

[0167] Among them, the maximum allowable volume refers to the maximum space volume that the piezoelectric material can occupy inside the implant, determined based on the basic structural dimensions of the spinal implant (such as the overall shape, the size of the internal cavity, etc.). For example, for vertebral fusion implants, the maximum allowable volume is limited to within 30% of the total volume of the implant. The load-bearing contact area refers to the surface area of ​​the piezoelectric material in contact with the internal load-bearing structure of the spinal implant or the vertebral bone tissue. When subjected to intervertebral pressure, a larger load-bearing contact area can enable the material to more stably sense pressure changes and reduce signal distortion caused by local stress concentration. The material thickness range refers to a reasonable range of piezoelectric material thickness calculated based on the maximum allowable volume and the load-bearing contact area. Among them, the material thickness range t∈[0.5mm,min(3.0mm, )],in, Indicates the maximum allowable volume, Represents the minimum load-bearing contact area. The elastic modulus threshold refers to the critical value of the material elastic modulus set to ensure the mechanical compatibility of the piezoelectric material with the spinal implant and the surrounding bone tissue. Among them, the elastic modulus threshold E≥3GPa can control the strain within 2%, which meets the dynamic stability requirements of the intervertebral space. The spinal lesion type refers to the specific category of pathological changes in the spine, including degeneration, fracture or deformity. The patient's daily activity level classification refers to the classification of the intensity of the patient's daily spinal activity (such as mild, moderate, severe) based on the patient's age, occupation, exercise habits and other information. The association matrix refers to the spinal lesion type and the patient's daily activity level classification as two dimensions. A two-dimensional matrix is ​​established to quantify the correlation between the performance requirements of the two for piezoelectric materials, where the horizontal axis is the type of lesion (such as degeneration, fracture), and the vertical axis is the activity level (low / medium / high). The piezoelectric response coefficient range refers to the ratio of the charge generated by the piezoelectric material when it is subjected to force to the stress it receives. For example, patients with fractures need materials that can capture tiny stress changes, and their piezoelectric response coefficient range can be set to 30-50pC / N, while patients with degenerative changes can be 15-30pC / N. The fatigue life threshold refers to the critical value of the maximum number of cycles at which the piezoelectric material can maintain stable piezoelectric performance under repeated stress generated by spinal activity. For example, the fatigue life threshold corresponding to patients with heavy activity needs to be ≥ To ensure the long-term effective work of the material, the material screening conditions refer to the standard system for accurately screening piezoelectric materials for spinal implants, including piezoelectric properties, mechanical properties, durability and biocompatibility, such as mechanical properties E ≥ 3GPa and thickness t∈[0.5mm,min(3.0mm, )],in, Indicates the maximum allowable volume, Indicates the minimum load-bearing contact area; durability ≥ Second cycle.

[0168] Optionally, according to the basic structure dimensions, the maximum allowable volume and load-bearing contact area of ​​the spinal implant can be calculated by the intervertebral space height and end plate contact surface dimensions in the basic structure dimensions, such as the maximum allowable volume ≤ intervertebral space height × end plate area × 0.8. Based on the maximum allowable volume and the load-bearing contact area, the material thickness range and elastic modulus threshold of the spinal implant can be determined by a material mechanics model.

[0169] Furthermore, the embodiments of the present invention can ensure the stable function of the spinal implant by analyzing the biocompatibility requirements of the spinal implant site. The biocompatibility requirements refer to the biological response requirements of the local microenvironment of the human spine to the implant, which are determined by the unique anatomical and physiological characteristics of the spine. For example, the spinal epidural space is rich in immune cells (such as macrophages and dendritic cells), and the implant must avoid triggering a chronic inflammatory cascade reaction; the cyclic shear force (approximately 0.1–5 MPa) generated during lumbar spine movement requires that the implant interface can withstand micro-motion wear, and the wear particles must not activate osteoclasts (particle size must be <10 μm).

[0170] Optionally, the biocompatibility requirements of the spinal implant site can be analyzed through animal model testing, such as constructing a spinal implant model using animals with a similar spinal structure to that of humans (such as pigs and sheep), simulating the implantation process in the animal, and regularly sampling and analyzing the histological changes at the implant site (such as observing the degree of inflammatory cell infiltration and the integration of new bone tissue with the material through pathological sections), and blood and body fluid indicators (such as hemolysis rate and cytokine levels).

[0171] The embodiments of the present invention can achieve precise adaptation of the piezoelectric material performance to the individual biological characteristics of the patient by identifying the biocompatibility pattern of the piezoelectric material in the target patient based on the biocompatibility requirements. The biocompatibility pattern refers to the multi-dimensional mechanism of action of the piezoelectric material in a specific patient to achieve a dynamic balance between biocompatibility requirements and therapeutic functions.

[0172] As an embodiment of the present invention, identifying a biocompatible mode of the piezoelectric material in the target patient based on the biocompatibility requirement includes:

[0173] obtaining the spinal implant site and biological characteristics of the target patient;

[0174] Positioning the contact area of ​​the piezoelectric material with a predetermined vertebral endplate to obtain an implant-bone interface;

[0175] determining an elastic modulus threshold and an immune response threshold of the piezoelectric material according to the biological characteristics, and measuring a stiffness parameter of the piezoelectric material;

[0176] calculating a dynamic contact pressure at the implant-bone interface based on the elastic modulus threshold;

[0177] constructing a stiffness-inflammation coupling matrix of the implant-bone interface according to the immune response threshold and the stiffness parameter;

[0178] calculating the bioelectric efficiency of the implant-bone interface using the stiffness-inflammation coupling matrix;

[0179] Based on the bioelectric efficiency, a biocompatible mode of the piezoelectric material in the target patient is identified.

[0180] Among them, the spinal implant site refers to the specific segment or position in the target patient's spine where the piezoelectric material needs to be implanted, such as the lumbar L4-L5 segment, the thoracic T12-L1 segment, etc. The biological characteristics refer to the physiological and biochemical characteristics of the tissues surrounding the spinal implant site, including the elastic modulus of the vertebral cancellous bone, the porosity of the endplate cartilage, the local baseline inflammatory factor concentration, etc. The preset vertebral endplate refers to the cartilage endplate on the upper and lower surfaces of the vertebra related to the implantation position of the piezoelectric material, which is an important structure between the vertebral body and the intervertebral disc. The implant-bone interface refers to the area where the piezoelectric material contacts the spinal bone tissue, which is the key site for mechanical transmission and biological interaction between the material and the human bone tissue. Among them, the implant-bone interface The spatial coordinates must satisfy the following requirements: contact area Ainterface ≥ 0.6 × total area of ​​the endplate contact area. The elastic modulus threshold refers to the upper and lower limits of the reasonable range of the elastic modulus of the piezoelectric material set to ensure that the piezoelectric material matches the spinal bone tissue in mechanical properties. The threshold calculation rule of the elastic modulus threshold can be based on the average elastic modulus of the normal bone tissue around the implant site and determined by statistical methods (such as 95% confidence interval). For example, if the average elastic modulus of normal bone tissue is 15 GPa, the elastic modulus threshold can be set to 10-20 GPa in combination with clinical data. The immune response threshold refers to the local tissue set to avoid excessive immune response caused by the piezoelectric material. The critical value of the degree of immune response is usually measured by indicators such as the concentration of inflammatory factors (such as TNF-α, IL-6, etc.) in local tissues and the number of infiltrating immune cells (such as macrophages). The upper limit of the normal concentration range of inflammatory factors and the normal number range of immune cells in the corresponding parts of the healthy human spine is mainly used as the immune response threshold. For example, if the normal range of TNF-α concentration in healthy tissue is 5-15pg / ml, the immune response threshold can be set to 15pg / ml. The stiffness parameter refers to the ability parameter of the piezoelectric material to resist deformation. The dynamic contact pressure refers to the dynamically changing pressure on the implant-bone interface when the spine performs physiological activities, such as when the lumbar spine is flexed 30°. , the dynamic contact pressure when rotated 5° is close to the common movements of the human body in daily activities, and can reflect the mechanical interaction between the piezoelectric material and the bone tissue interface in actual physiological activities. The stiffness-inflammation coupling matrix refers to a matrix used to describe the correlation between the stiffness parameters of the piezoelectric material and the degree of local tissue inflammatory response at the implant-bone interface. The elements in the matrix represent the quantitative values ​​of the degree of inflammatory response corresponding to different stiffness values. Among them, the matrix elements of the stiffness-inflammation coupling matrix can be calibrated through in vitro organ chip experiments. The bioelectric efficiency refers to the ability of the piezoelectric material to convert mechanical stimuli (such as dynamic contact pressure) into bioelectric signals at the implant-bone interface and the conduction efficiency of the bioelectric signals at the interface.

[0181] Optionally, the spinal implant site and biological characteristics of the target patient can be obtained through a medical imaging analysis system, such as the Mimics21.0 system, the contact area between the piezoelectric material and the preset vertebral endplate can be located through preoperative CT three-dimensional reconstruction and intraoperative navigation system, the stiffness parameters of the piezoelectric material can be measured by a nanoindenter, such as AgilentG200, and the stiffness-inflammation coupling matrix of the implant-bone interface can be constructed based on the COMSOL multi-physics field simulation system.

[0182] To visually demonstrate the changes in dynamic contact pressure at the implant-bone interface under different physiological activities, refer to Figure 2 The figure shows a schematic diagram of the spinal mechanical loading mode of a spinal implant design method using the self-sensing characteristics of piezoelectric materials provided by an embodiment of the present invention. The figure simulates the real force of the spine through four typical scenarios: the central compression (AC) scenario is used to simulate axial compression scenarios such as standing and sitting for a long time. The force is applied along the midline of the spine to restore the vertical "up and down squeezing" mechanical environment, providing a basis for analyzing the static / dynamic pressure baseline of the interface; the flexion (Flex) scenario is used to simulate flexion movements such as bending over to pick up objects. The force is applied from the front to bend the lumbar spine forward. Under the flexion working condition, the interface The coupling relationship between dynamic pressure and immune inflammation (stiffness-inflammation interaction mechanism) clarifies the impact of mechanical stimulation on biological response; the extension (Ext) scenario is used to simulate the backward extension movement, and the force applied from the rear drives the lumbar spine to extend, reproduce the force characteristics of the spine when leaning back, and assist in observing the relationship between the interface pressure distribution and the stability of the implant; the lateral flexion (LB) scenario is used to simulate lateral flexion movements such as reaching for objects from the side, and the oblique force is applied to induce lateral bending of the lumbar spine, verifying the bioelectric efficiency of piezoelectric materials under lateral mechanical input, matching the patient's lateral flexion needs, and supporting the accurate identification of bio-adaptive patterns.

[0183] S5. Based on the perception sensitivity level, the energy consumption rate and the bioadaptation pattern, a piezoelectric signal processing module is embedded in the spinal implant. Based on the piezoelectric signal processing module, combined with the bioadaptation pattern and the perception performance judgment condition, parameter optimization processing of the spinal implant is performed to obtain a target spinal implant.

[0184] The embodiment of the present invention embeds a piezoelectric signal processing module in the spinal implant based on the perception sensitivity level, the energy consumption rate and the bioadaptation mode, thereby accurately correlating the self-perception signal of the piezoelectric material with the physiological activity of the spine, thereby ensuring real-time monitoring of the mechanical state of the implant and the response of the surrounding tissues. The piezoelectric signal processing module refers to an electronic unit integrated in the spinal implant, which is mainly composed of a signal acquisition circuit, a filtering component, an amplification module, a data conversion unit and a control chip, and can convert the mechanical-electrical conversion signal of the piezoelectric material into effective information that can be used to monitor the mechanical state of the implant and the response of the surrounding tissues.

[0185] As an embodiment of the present invention, the piezoelectric signal processing module is embedded in the spinal implant based on the sensing sensitivity level, the energy consumption rate, and the bioadaptive mode, including:

[0186] Identifying a bioelectric signal threshold range corresponding to the perception sensitivity level and a stimulation parameter interval of the bioadaptation mode;

[0187] Analyzing the correlation between the perception sensitivity level and the bioadaptation mode based on the bioelectric signal threshold range and the stimulation parameter interval;

[0188] extracting spinal region signal characteristics and spinal anatomical parameters corresponding to the spinal implant;

[0189] According to the association relationship, the signal features of the spinal region are sorted in the time and frequency domains to obtain a signal capture priority sequence;

[0190] Setting a fluctuation tolerance threshold of the spinal implant using the energy consumption dynamic curve of the energy consumption efficiency;

[0191] parsing the impedance spectrum characteristics and characteristic frequencies in the bioadaptive mode to generate a collaborative optimization strategy for the spinal implant;

[0192] Combining the signal capture priority sequence, the fluctuation tolerance threshold, and the collaborative optimization strategy to construct an adaptive signal processing engine for the spinal implant;

[0193] A piezoelectric signal processing module is embedded in the spinal implant based on the spinal anatomical parameters and the adaptive signal processing engine.

[0194] The bioelectric signal threshold range refers to the amplitude-frequency range of the electrical signal that can trigger an effective neural response. The identification conditions of the bioelectric signal threshold range are: sampling rate ≥ 1kHz, bandpass filtering 0.1-150Hz, signal-to-noise ratio SNR>15dB, the stimulation parameter range refers to the parameter value range in which the piezoelectric material can produce effective bioelectric stimulation without causing abnormal tissue reaction during the interaction with the spinal tissue, including the reasonable range of parameters such as stimulation intensity, stimulation frequency, and stimulation duration. The correlation relationship refers to the intrinsic connection between the bioelectric signal threshold range corresponding to the perception sensitivity level and the stimulation parameter range of the bioadaptive mode. The correlation strength between the threshold range and the stimulation parameter can be calculated based on the Pearson correlation coefficient. The spinal region signal feature refers to the unique EMG signal (sampling rate 2kHz) presented by the L1-L5 segment surface collected by the intervertebral foramen electrode array. The characteristics include the amplitude, frequency component, waveform change, etc. of the signal. The spinal anatomical parameters refer to quantitative indicators that describe the characteristics of the spinal anatomical structure, including the diameter of the intervertebral foramen (d∈[5,15]mm) and the distance between the dura mater sacs (h∈[2,8]mm). The time-frequency domain sorting processing refers to the process of analyzing the signal characteristics of the spinal region in the time domain and frequency domain respectively, extracting the characteristic values ​​of the signal at different time points and different frequency components, and then sorting them according to the importance and relevance of these characteristic values. The signal capture priority sequence refers to the capture order list determined for the signal characteristics of different spinal regions based on the results of the time-frequency domain sorting processing. The energy consumption dynamic curve refers to the pressure The curve of the energy consumption rate of the electrical signal processing module changing with time is obtained by performing exponentially weighted moving average filtering on the energy consumption rate. The fluctuation tolerance threshold refers to the upper and lower limits of the energy consumption rate allowed to fluctuate according to the energy consumption dynamic curve. When the energy consumption rate fluctuates within the threshold range, the module does not need to make large-scale energy consumption adjustments to ensure the stability of the module's operation. The impedance spectrum characteristics refer to the impedance change characteristics of the contact interface between the piezoelectric material and the spinal tissue in the bioadaptive mode at different frequencies. The characteristic frequency refers to a specific frequency value that is representative in the impedance spectrum characteristics. The impedance characteristics at this frequency can significantly reflect the interaction state between the piezoelectric material and the spinal tissue. The collaborative optimization strategy This refers to a solution that comprehensively optimizes the piezoelectric signal processing module's signal transmission path, energy supply method, and adaptation state with spinal tissue based on the impedance spectrum characteristics and characteristic frequencies in the bioadaptive mode. The adaptive signal processing engine is a core signal processing unit that can automatically adjust its operating mode and parameters based on changes in the spinal physiological state. It includes a feedforward channel, a feedback channel, and a bypass unit. The feedforward channel is used to predict and process potential signal changes in advance; the feedback channel is used to modify and optimize the processing process based on actual processing results; and the bypass unit is used to skip certain processing steps in specific situations, increasing processing speed and thus achieving flexible response to different spinal states.

[0195] Optionally, the time-frequency domain sorting processing of the spinal region signal characteristics can be implemented using Morlet wavelet transform, the impedance spectrum characteristics in the bioadaptive mode can be analyzed by a fourth-order Butterworth filter, the characteristic frequency in the bioadaptive mode can be extracted by a Cole-Cole model, and the collaborative optimization strategy of the spinal implant can be generated using a multi-objective adaptive genetic algorithm. For example, the multi-objective adaptive genetic algorithm can use the curve data of the impedance value changing with frequency and the characteristic frequency in the impedance spectrum characteristics as input, generate an initial optimization scheme population through encoding, select, crossover and mutate the population based on a preset fitness function (such as signal transmission loss rate, energy utilization rate, tissue inflammatory response prediction value, etc.), and dynamically adjust the genetic operator parameters according to the optimization effect in the iterative process, and finally screen out the Pareto optimal solution set as the basic scheme of the collaborative optimization strategy.

[0196] Furthermore, embodiments of the present invention utilize the piezoelectric signal processing module, combined with the biofit model and the perceptual performance judgment criteria, to perform parameter optimization processing on the spinal implant to obtain a target spinal implant. This can enhance the comprehensiveness of spinal implant functionality and significantly reduce the limitations of conventional spinal implants, which can only provide static support. Furthermore, the implant can improve the sensitivity of the interaction between the spinal implant and spinal tissue, thereby facilitating the adjustment of personalized postoperative rehabilitation plans and enhancing the accuracy of spinal treatment. The parameter optimization process refers to the process of adjusting and optimizing key parameters of the spinal implant, focusing on the performance requirements of the piezoelectric signal processing module, in combination with the material-to-tissue compatibility characteristics reflected by the biofit model and the perceptual performance judgment criteria. The target spinal implant is a spinal implant obtained after the parameter optimization process. The implant integrates a piezoelectric material and a piezoelectric signal processing module, meeting the mechanical support requirements for spinal repair while also possessing real-time perception of its own stress, deformation, and interaction with surrounding tissues, and adapting well to the patient's internal biological environment.

[0197] As an embodiment of the present invention, the piezoelectric signal processing module is used to perform parameter optimization processing on the spinal implant in combination with the bioadaptation mode and the sensing performance judgment condition to obtain a target spinal implant, including:

[0198] Collecting multi-dimensional piezoelectric signals from the piezoelectric signal module in real time;

[0199] identifying a current adaptation state of the spinal implant based on the multi-dimensional piezoelectric signal;

[0200] According to the bio-adaptation mode, matching the parameter optimization strategy corresponding to the current adaptation state;

[0201] Based on the parameter optimization strategy, converting the perception performance judgment condition into specific control parameters of the piezoelectric signal processing module;

[0202] Based on the specific control parameters, performing parameter optimization processing of the piezoelectric signal processing module, and collecting feedback data of the spinal implant during the parameter optimization processing in real time;

[0203] Calculating the biocompatibility index and perceived performance improvement rate of the spinal implant under the parameter optimization strategy based on the feedback data;

[0204] According to the biocompatibility index and the perception performance improvement rate, dynamically adjusting the bioadaptation mode to generate an optimized bioadaptation strategy;

[0205] The optimized biofit strategy is fed back to the piezoelectric signal processing module to update the specific control parameters and obtain a target spinal implant.

[0206] Among them, the multi-dimensional piezoelectric signal refers to a signal set collected by a piezoelectric sensor array distributed at different positions and directions of the spinal implant, wherein the signal set includes spatial dimension signals, time dimension signals, frequency dimension signals and physical dimension signals. The current adaptation state refers to the mechanical, biological and functional matching state between the spinal implant and the surrounding biological tissue. The parameter optimization strategy refers to a piezoelectric signal processing parameter adjustment scheme automatically generated according to the current adaptation state. For example, if mechanical looseness (high vibration frequency signal) is detected, the strategy is to increase the stiffness of the piezoelectric material; if an inflammatory response (low pH value signal) is detected, the strategy is to reduce the signal gain to reduce local stimulation. The specific control parameters refer to converting the parameter optimization strategy into a quantitative instruction executable by the piezoelectric signal processing module, such as the charge amplifier gain instruction (unit: dB); the filter cutoff frequency instruction (unit: Hz); the piezoelectric material driving voltage instruction (unit: V). The parameter optimization processing refers to the process of the piezoelectric signal processing module dynamically adjusting its own working mode according to the specific control parameters, such as real-time adjustment of the charge amplifier. gain to eliminate noise; the driving voltage is corrected by the PID algorithm to maintain signal stability; the feedback data refers to the multimodal monitoring data collected during the parameter optimization process, which is used to evaluate the optimization effect; the biocompatibility index refers to a composite index that quantitatively evaluates the degree of biomechanical matching between the implant and the surrounding bone tissue, and the specific calculation formula is: biocompatibility index = α × mechanical compatibility + β × tissue compatibility + γ × long-term stability, where α, β, and γ are weight coefficients determined according to clinical needs; the perceived performance improvement rate refers to an index that quantitatively evaluates the degree of improvement in signal acquisition quality after parameter optimization, and the specific calculation formula is: perceived performance improvement rate = (performance index after optimization - performance index before optimization) / performance index before optimization × 100%; the dynamic adjustment processing refers to the process of real-time correction of the biofit mode based on the biocompatibility index and the perceived performance improvement rate; the optimized biofit strategy refers to the optimal parameter optimization scheme generated after dynamic adjustment processing to adapt to the current biomechanical environment, including the optimal adaptation mode, optimization parameter range, dynamic adjustment rules, safety boundary conditions, etc.

[0207] Optionally, based on the multi-dimensional piezoelectric signal, the current adaptation state of the spinal implant can be identified by a multi-classifier of a support vector machine, and the parameter optimization processing of the piezoelectric signal processing module can be implemented using a gradient descent optimization algorithm.

[0208] Compared with the problems described in the background technology, the embodiments of the present invention can ensure that the designed implant can not only effectively support the diseased spine, but also accurately capture the abnormal state of the diseased part through the self-sensing characteristics by identifying the spinal lesion characteristics of the target patient based on the three-dimensional spinal imaging data and the clinical diagnosis information. Furthermore, the embodiments of the present invention can ensure that the size and shape of the implant are accurately adapted to the implant site by determining the spinal implant site of the target patient based on the three-dimensional spinal imaging data and the clinical diagnosis information; the embodiments of the present invention can provide a method for benefit by defining the spinal adaptation threshold between the spinal implant and the spinal implant site according to the clinical diagnosis information and the three-dimensional spinal imaging data. The design of the spinal implant using the self-sensing characteristics of piezoelectric materials provides a clear adaptation standard to ensure that the implant matches the spinal implant site to achieve the expected effect. Furthermore, the embodiment of the present invention determines the basic structural dimensions of the spinal implant in the target patient's body based on the spinal adaptation threshold, thereby ensuring that the spinal implant matches the physiological structure of the patient's spine. The embodiment of the present invention sets the perception sensitivity level of the spinal implant in the target patient's body based on the spinal lesion characteristics, thereby enabling the spinal implant to perceive the abnormal mechanical changes of the patient's spinal lesion site in a targeted manner, thereby avoiding misjudgment due to improper perception sensitivity, and ensuring the patient's safety and rehabilitation effect after surgery. The embodiment of the present invention calculates the signal transmission efficiency and energy consumption rate of the spinal implant according to the perception sensitivity level, thereby ensuring that the spinal implant meets the patient's personalized monitoring needs while avoiding the loss of key data due to inefficient signal transmission. For example, at a high perception sensitivity level, by clarifying the signal transmission efficiency, it can ensure that the pressure, displacement and other signals collected at high frequency in the lesion area are accurately transmitted to the in vitro monitoring device; the embodiment of the present invention sets the perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate, thereby improving the utilization efficiency of the piezoelectric material's self-perception signal, and at the same time ensuring that the perception performance of the spinal implant is compatible with the characteristics of the piezoelectric material, thereby improving the perception performance of the spinal implant. The clinical practicality of the implant is improved. Furthermore, the embodiments of the present invention screen the piezoelectric material corresponding to the spinal implant based on the basic structure size and the clinical diagnostic information, thereby improving the compatibility of the piezoelectric material with the implant structure, and at the same time ensuring that the performance of the selected piezoelectric material meets clinical monitoring requirements, thereby improving the perceived reliability of the implant. The embodiments of the present invention ensure the stable function of the spinal implant by analyzing the biocompatibility requirements of the spinal implant site. Furthermore, the embodiments of the present invention identify the biocompatible mode of the piezoelectric material in the target patient based on the biocompatibility requirements, thereby achieving precise adaptation of the piezoelectric material performance to the individual biological characteristics of the patient.By embedding a piezoelectric signal processing module in the spinal implant based on the sensory sensitivity level, the energy consumption rate, and the bioadaptation pattern, the embodiment of the present invention can accurately correlate the piezoelectric material's self-sensing signal with the physiological activity of the spine, ensuring real-time monitoring of the mechanical state of the implant and the response of surrounding tissues. Furthermore, by performing parameter optimization processing on the spinal implant based on the piezoelectric signal processing module, combined with the bioadaptation pattern and the sensory performance judgment conditions, the embodiment of the present invention obtains a target spinal implant. This can enhance the comprehensiveness of spinal implant functionality and significantly reduce the limitations of traditional spinal implants that can only provide static support. It can also improve the sensory sensitivity of the interaction between the spinal implant and spinal tissue, thereby promoting the adjustment of personalized postoperative rehabilitation plans and improving the accuracy of spinal treatment. Therefore, the spinal implant design method and system utilizing the self-sensing characteristics of piezoelectric materials provided by the embodiment of the present invention can accurately assess the working status of the postoperative implant and the rehabilitation progress of the spine.

[0209] like Figure 3 FIG. 1 is a functional module diagram of a spinal implant design system utilizing the self-sensing characteristics of piezoelectric materials according to the present invention.

[0210] The spinal implant design system 200 utilizing the self-sensing properties of piezoelectric materials described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the spinal implant design system utilizing the self-sensing properties of piezoelectric materials can include a patient feature recognition module 201, a spinal body adaptation module 202, a material screening condition definition module 203, a suitable material determination module 204, and a target generation module 205. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These modules are stored in the electronic device's memory.

[0211] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0212] The patient feature identification module 201 is used to obtain a target patient for whom a spinal implant is to be implanted, collect the target patient's spinal three-dimensional imaging data and clinical diagnosis information, identify the target patient's spinal lesion characteristics based on the spinal three-dimensional imaging data and the clinical diagnosis information, and determine the target patient's spinal implant location;

[0213] The spinal body adaptation module 202 is configured to define a spinal adaptation threshold between the spinal implant and the spinal implant site based on the clinical diagnosis information and the three-dimensional spinal imaging data, and determine the basic structural dimensions of the spinal implant in the target patient based on the spinal adaptation threshold;

[0214] The material screening condition definition module 203 is configured to set a perception sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics, calculate the signal transmission efficiency and energy consumption rate of the spinal implant based on the perception sensitivity level, and set a perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate;

[0215] The compatible material determination module 204 is configured to select a piezoelectric material corresponding to the spinal implant based on the basic structure dimensions and the clinical diagnosis information, analyze the biocompatibility requirements of the spinal implant site, and identify a biocompatible pattern of the piezoelectric material in the target patient based on the biocompatibility requirements;

[0216] The target generation module 205 is used to embed a piezoelectric signal processing module in the spinal implant based on the perception sensitivity level, the energy consumption rate and the bioadaptation pattern, and perform parameter optimization processing of the spinal implant based on the piezoelectric signal processing module in combination with the bioadaptation pattern and the perception performance judgment condition to obtain a target spinal implant.

[0217] In detail, the modules in the spinal implant design system 200 using the self-sensing characteristics of piezoelectric materials in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means as the spinal implant design method using the self-sensing characteristics of piezoelectric materials described in the text can produce the same technical effects, which will not be repeated here.

[0218] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0219] Finally, it should be noted that among the above-mentioned multiple embodiments, each embodiment can be combined with each other or be independent, and deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solution of the present invention and are not limiting. Although the present invention is described in detail with reference to the preferred embodiments, ordinary technicians in this field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A spinal implant design method utilizing the self-sensing properties of piezoelectric materials, characterized in that: The method comprises: Acquiring a target patient in whom a spinal implant is to be implanted, collecting three-dimensional spinal imaging data and clinical diagnostic information of the target patient, identifying spinal lesion characteristics of the target patient based on the three-dimensional spinal imaging data and the clinical diagnostic information, and determining a spinal implantation site for the target patient; defining a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the three-dimensional spinal imaging data, and determining a basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold; setting a perception sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics, calculating a signal transmission efficiency and an energy consumption rate of the spinal implant according to the perception sensitivity level, and setting a perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate; screening a piezoelectric material corresponding to the spinal implant based on the basic structure dimensions and the clinical diagnostic information, analyzing the biocompatibility requirements of the spinal implant site, and identifying a biocompatible pattern of the piezoelectric material in the target patient based on the biocompatibility requirements; Based on the perception sensitivity level, the energy consumption rate and the bioadaptation pattern, a piezoelectric signal processing module is embedded in the spinal implant. Based on the piezoelectric signal processing module, combined with the bioadaptation pattern and the perception performance judgment conditions, parameter optimization processing of the spinal implant is performed to obtain a target spinal implant.

2. The spinal implant design method using the self-sensing characteristics of piezoelectric materials according to claim 1, characterized in that: The identifying the spinal lesion characteristics of the target patient based on the spinal three-dimensional image data and the clinical diagnosis information includes: Segmenting the vertebral regions of the target patient based on the three-dimensional spinal image data, and calculating bone density values ​​of different vertebral regions; generating a vertebral bone density gradient distribution of the target patient using the bone density value; identifying adjacent cones in the vertebral region and calculating relative displacements and rotation angles of the adjacent cones under the patient's motion state to obtain dynamic stability parameters; Segmenting the nerve-peripheral tissue in the three-dimensional spinal column image data and simulating the stress distribution of the nerve-peripheral tissue; generating a compression intensity heat map of the target patient according to the stress distribution; extracting the serum test data of the target patient from the clinical diagnostic information to determine the inflammatory biomarkers and their concentrations of the target patient; Constructing a spinal lesion topology map of the target patient by combining the vertebral bone density gradient distribution, the dynamic stability parameter, the compression intensity heat map, the inflammatory biomarker and its concentration; The spinal lesion characteristics of the target patient are identified through the spinal lesion topology map.

3. The spinal implant design method using the self-sensing characteristics of piezoelectric materials according to claim 1, characterized in that: Defining a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the spinal three-dimensional image data includes: Extracting core anatomical parameters of the spinal implant site from the spinal three-dimensional image data; Extracting key lesion indicators and serum marker data from the clinical diagnosis information; Calculating weighted correlation values ​​between the core anatomical parameters and the key lesion indicators; generating a dynamic correlation coefficient matrix between the core anatomical parameters and the key lesion indicators according to the weighted correlation values; outputting an initial adaptation threshold between the spinal implant and the spinal implant site based on the dynamic correlation coefficient matrix; extracting serum bone metabolism markers and inflammatory indicators from the serum marker data; setting a real-time adaptation correction variable between the spinal implant and the spinal implant site according to the serum bone metabolism marker and the inflammatory index; The real-time adaptation correction variable and the initial adaptation threshold are integrated to define a spinal adaptation threshold between the spinal implant and the spinal implant site.

4. The spinal implant design method using the self-sensing characteristics of piezoelectric materials according to claim 1, characterized in that: Determining the basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold comprises: Based on the spinal adaptation threshold, identifying a critical spinal region in the target patient; Extracting key threshold parameters from the spinal adaptation threshold and acquiring CT image data of the key spinal region; Executing parameter fusion processing of the key threshold parameters and the CT image data to generate a personalized parameter association data set; Establishing a digital twin model of the spine of the target patient based on the personalized parameter association dataset; Calculating a quantitative index of stress distribution uniformity and a quantitative index of micro-friction loss of the spinal implant in the target patient through the spinal digital twin model; According to the quantitative index of stress distribution uniformity and the quantitative index of fretting friction loss, the biocompatibility adaptation coefficient between the spinal implant and the target patient is calculated by the following formula: ； Where R represents the biocompatibility coefficient between the spinal implant and the target patient, represents the standard deviation of the quantitative index of stress distribution uniformity, represents the average value of the quantitative index of stress distribution uniformity, The loss coefficient represents the quantitative index of fretting friction loss, Indicates the critical threshold corresponding to the loss coefficient of the quantitative indicator of micro-friction loss, represents the attenuation coefficient, represents the local bone elastic modulus of the target patient, Indicates the elastic modulus of the spinal implant material; Based on the biocompatibility adaptation coefficient, screening out the optimal size combination of the spinal implant in the target patient; The basic structural size of the spinal implant in the target patient is determined according to the optimal size combination.

5. The spinal implant design method using the self-sensing characteristics of piezoelectric materials according to claim 1, characterized in that: The step of setting the sensing sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics includes: Locating the target patient's diseased vertebral segment according to the spinal lesion characteristics, and extracting a lesion activity index of the diseased vertebral segment; calculating a mechanical fragility score of the diseased vertebral segment, and dividing the perception dimension of the spinal implant according to the mechanical fragility score; calibrating a minimum signal capture threshold of the spinal implant according to the lesion activity index and the mechanical fragility score; Determining the differential regulatory weights of the perceptual dimensions through principal component analysis; The differential adjustment weight, the minimum signal capture threshold, and the perception dimension are combined to generate a perception sensitivity level of the spinal implant.

6. The method for designing a spinal implant using the self-sensing characteristics of piezoelectric materials according to claim 1, wherein: The step of selecting a piezoelectric material corresponding to the spinal implant according to the basic structure size and the clinical diagnosis information includes: Calculating the maximum allowable volume and load-bearing contact area of ​​the spinal implant based on the basic structure dimensions; determining a material thickness range and an elastic modulus threshold of the spinal implant based on the maximum allowable volume and the load-bearing contact area; Extracting the spinal lesion type and the patient's daily activity level classification from the clinical diagnosis information; Establishing a correlation matrix between the spinal lesion type and the patient's daily activity level classification to output the piezoelectric response coefficient range and fatigue life threshold of the spinal implant; setting material screening conditions for the spinal implant according to the piezoelectric response coefficient range and the fatigue life threshold; Based on the material screening conditions, piezoelectric materials corresponding to the spinal implant are screened out.

7. The spinal implant design method using the self-sensing characteristics of piezoelectric materials according to claim 1, characterized in that: The identifying, based on the biocompatibility requirement, a biocompatible mode of the piezoelectric material in the target patient's body, includes: obtaining the spinal implant site and biological characteristics of the target patient; Positioning the contact area of ​​the piezoelectric material with a predetermined vertebral endplate to obtain an implant-bone interface; determining an elastic modulus threshold and an immune response threshold of the piezoelectric material according to the biological characteristics, and measuring a stiffness parameter of the piezoelectric material; calculating a dynamic contact pressure at the implant-bone interface based on the elastic modulus threshold; constructing a stiffness-inflammation coupling matrix of the implant-bone interface according to the immune response threshold and the stiffness parameter; calculating the bioelectric efficiency of the implant-bone interface using the stiffness-inflammation coupling matrix; Based on the bioelectric efficiency, a biocompatible mode of the piezoelectric material in the target patient is identified.

8. The spinal implant design method using the self-sensing characteristics of piezoelectric materials according to claim 1, characterized in that: The method of embedding a piezoelectric signal processing module in the spinal implant based on the sensing sensitivity level, the energy consumption rate, and the bioadaptation mode includes: Identifying a bioelectric signal threshold range corresponding to the perception sensitivity level and a stimulation parameter interval of the bioadaptation mode; Analyzing the correlation between the perception sensitivity level and the bioadaptation mode based on the bioelectric signal threshold range and the stimulation parameter interval; extracting spinal region signal characteristics and spinal anatomical parameters corresponding to the spinal implant; According to the association relationship, the signal features of the spinal region are sorted in the time and frequency domains to obtain a signal capture priority sequence; Setting a fluctuation tolerance threshold of the spinal implant using the energy consumption dynamic curve of the energy consumption efficiency; parsing the impedance spectrum characteristics and characteristic frequencies in the bioadaptive mode to generate a collaborative optimization strategy for the spinal implant; Combining the signal capture priority sequence, the fluctuation tolerance threshold, and the collaborative optimization strategy to construct an adaptive signal processing engine for the spinal implant; A piezoelectric signal processing module is embedded in the spinal implant based on the spinal anatomical parameters and the adaptive signal processing engine.

9. The method for designing a spinal implant using the self-sensing characteristics of piezoelectric materials according to claim 1, wherein: The method of performing parameter optimization processing of the spinal implant based on the piezoelectric signal processing module in combination with the bioadaptation mode and the sensing performance judgment condition to obtain a target spinal implant includes: Collecting multi-dimensional piezoelectric signals from the piezoelectric signal module in real time; identifying a current adaptation state of the spinal implant based on the multi-dimensional piezoelectric signal; According to the bio-adaptation mode, matching the parameter optimization strategy corresponding to the current adaptation state; Based on the parameter optimization strategy, converting the perception performance judgment condition into specific control parameters of the piezoelectric signal processing module; Based on the specific control parameters, performing parameter optimization processing of the piezoelectric signal processing module, and collecting feedback data of the spinal implant during the parameter optimization processing in real time; Calculating the biocompatibility index and perceived performance improvement rate of the spinal implant under the parameter optimization strategy based on the feedback data; According to the biocompatibility index and the perception performance improvement rate, dynamically adjusting the bioadaptation mode to generate an optimized bioadaptation strategy; The optimized biofit strategy is fed back to the piezoelectric signal processing module to update the specific control parameters and obtain a target spinal implant.

10. A spinal implant design system utilizing the self-sensing properties of piezoelectric materials, characterized in that: The system comprises: a patient feature identification module, configured to obtain a target patient for whom a spinal implant is to be implanted, collect three-dimensional spinal imaging data and clinical diagnostic information of the target patient, identify the characteristics of the target patient's spinal lesions based on the three-dimensional spinal imaging data and the clinical diagnostic information, and determine a spinal implantation site for the target patient; a spinal body adaptation module, configured to define a spinal adaptation threshold between the spinal implant and the spinal implantation site based on the clinical diagnosis information and the three-dimensional spinal imaging data, and determine a basic structural size of the spinal implant in the target patient based on the spinal adaptation threshold; a material screening condition definition module, configured to set a perception sensitivity level of the spinal implant in the target patient based on the spinal lesion characteristics, calculate a signal transmission efficiency and an energy consumption rate of the spinal implant based on the perception sensitivity level, and set a perception performance judgment condition of the spinal implant based on the signal transmission efficiency and the energy consumption rate; a compatible material determination module, configured to select a piezoelectric material corresponding to the spinal implant based on the basic structure dimensions and the clinical diagnostic information, analyze the biocompatibility requirements of the spinal implant site, and identify a biocompatible pattern of the piezoelectric material in the target patient based on the biocompatibility requirements; A target generation module is configured to embed a piezoelectric signal processing module in the spinal implant based on the perception sensitivity level, the energy consumption rate, and the bioadaptation pattern, and to perform parameter optimization processing of the spinal implant based on the piezoelectric signal processing module in combination with the bioadaptation pattern and the perception performance judgment condition to obtain a target spinal implant.

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