Human body pain evaluation method, device and equipment based on finite element simulation and medium

Abstract

The invention relates to the technical field of medical information, and provides a human body pain evaluation method and device based on finite element simulation, equipment and a medium, and the method comprises the steps: building an individual three-dimensional tissue finite element model of a target object; finite element simulation is carried out through the individual three-dimensional tissue finite element model of the target object, and a stress-strain distribution result is obtained; calculating a mechanical pain stimulation index based on the mechanical pain threshold value and the stress-strain distribution result of each type of tissue; obtaining an established threshold mapping relation between the mechanical pain stimulation index and subjective pain evaluation of the target object; calculating an individual pain stimulation index of the target object based on the individual pain threshold and the stress-strain distribution result; performing visualization processing on the spatial distribution result of the individual pain stimulation index to generate a pain heat map; the pain heat map is used for reflecting the spatial distribution and intensity grade of the pain risk. According to the invention, objective evaluation of pain can be realized without depending on continuous subjective input.

Description

Technical Field

[0001] This application relates to the field of medical information technology, and in particular to a human pain evaluation method based on finite element simulation, a human pain evaluation device based on finite element simulation, a corresponding electronic device, and a corresponding computer-readable storage medium. Background Technology

[0002] Pain is a subjective experience, and current clinical assessment mainly relies on subjective tools such as the Visual Analogue Scale (VAS), Numerical Rating Scale (NRS), or Behavioral Pain Scale (BPS). While these methods are simple, they are greatly affected by individual expressive abilities, cognitive levels, and emotional states, and their reliability is significantly insufficient in unconscious patients, children, or individuals with cognitive impairment. Furthermore, they are difficult to use for continuous and objective pain monitoring.

[0003] Existing research has attempted to objectify pain assessment methods: autonomic nervous system indicators (such as heart rate variability, skin conductance, pupillary changes, etc.) can reflect pain-related physiological responses, but are easily affected by anesthesia, drugs, and psychological factors, and have limited specificity; electroencephalography, evoked potentials, and fMRI (functional magnetic resonance imaging) can reveal central pain responses, but the technology is costly, has poor real-time performance, and the specificity of pain is controversial; facial expression recognition, speech, physiological signal fusion + machine learning methods are valuable in some scenarios, but mostly remain at the level of "pain response", making it difficult to locate the source of pain and distinguish individual threshold differences.

[0004] In summary, existing methods are mostly based on indirect inferences from pain responses, making it difficult to quantify the stress on peripheral tissues from the physical mechanisms of pain generation, and they also lack individualized threshold mapping mechanisms. Finite Element Analysis (FEA) can be used to simulate the stress-strain distribution of human tissues under external stimuli, reflecting the spatial distribution and intensity changes of mechanical pain-inducing stimuli at the tissue level. However, without combining simulation models with subjective pain information, it is impossible to determine individual differences in mechanical pain thresholds, and there is a lack of evidence to verify the reliability of its assessment results. Summary of the Invention

[0005] This application provides a method, device, equipment, and medium for human pain evaluation based on finite element simulation. It can quantify the mechanism of pain stimuli and establish evaluation standards through stress-strain simulation, pain threshold mapping, and subjective rating verification, laying the foundation for subsequent objective pain assessment without subjective input.

[0006] In one aspect, this application provides a method for evaluating human pain based on finite element simulation, the method comprising:

[0007] Establish an individual three-dimensional finite element model of the target object;

[0008] Finite element simulation was performed using an individual three-dimensional finite element model of the target object to obtain stress-strain distribution results.

[0009] The mechanical pain threshold of various tissues is obtained, and the mechanical pain stimulation index is calculated based on the mechanical pain threshold of various tissues and the stress-strain distribution results.

[0010] Obtain the established threshold mapping relationship between the mechanical pain stimulation index and the subjective pain evaluation of the target object; the threshold mapping relationship is used to map and calibrate to obtain the individual pain threshold of the target object;

[0011] Based on the individual pain threshold and the stress-strain distribution results, the individual pain stimulus index of the target object is calculated;

[0012] The spatial distribution of the individual pain stimulus index is visualized to generate a pain heatmap; the pain heatmap is used to reflect the spatial distribution and intensity level of pain risk.

[0013] On the other hand, this application provides a human pain assessment device based on finite element simulation, the device comprising:

[0014] The finite element model creation module is used to create individual three-dimensional finite element models of the target object.

[0015] The finite element simulation module is used to perform finite element simulation using the individual three-dimensional finite element model of the target object to obtain stress-strain distribution results.

[0016] The mechanical pain stimulation index calculation module is used to obtain the mechanical pain threshold of various tissues and calculate the mechanical pain stimulation index based on the mechanical pain threshold of various tissues and the stress-strain distribution results.

[0017] The threshold mapping relationship acquisition module is used to acquire the established threshold mapping relationship between the mechanical pain stimulation index and the subjective pain evaluation of the target object; the threshold mapping relationship is used to map and calibrate to obtain the individual pain threshold of the target object;

[0018] The individual pain stimulus index calculation module is used to calculate the individual pain stimulus index of the target object based on the individual mechanical pain threshold and the stress-strain distribution results;

[0019] The pain heatmap generation module is used to visualize the spatial distribution of the individual's pain stimulus index and generate a pain heatmap of the target object; the pain heatmap is used to reflect the spatial distribution and intensity level of pain risk.

[0020] In another aspect, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements any of the finite element simulation-based human pain assessment methods described above.

[0021] In another aspect, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the finite element simulation-based human pain assessment methods described in the present invention.

[0022] In another aspect, this application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the human pain assessment method based on finite element simulation described in the above aspects.

[0023] This application provides a finite element simulation-based method, device, equipment, and medium for human pain assessment. It establishes an individual three-dimensional finite element model of the target object's tissues and performs finite element simulation using this model to obtain stress-strain distribution results. Then, it acquires the mechanical pain thresholds for various tissues and calculates a mechanical pain stimulation index based on these thresholds and the stress-strain distribution results. By obtaining the established mapping relationship between the mechanical pain stimulation index and the target object's subjective pain evaluation threshold, this mapping relationship can be used to calibrate and obtain the target object's individual pain threshold. At this point, based on the individual mechanical pain threshold and stress-strain distribution results, the individual pain stimulation index of the target object can be calculated. The spatial distribution of the individual pain stimulation index is then visualized to generate a pain heatmap of the target object. The generated pain can reflect the spatial distribution and intensity level of pain risk, thereby achieving an objective assessment of the target object's human pain. By establishing an individual three-dimensional finite element model of the tissues, performing stress-strain simulation, and combining a small amount of subjective pain scores for threshold calibration and verification to calculate the pain stimulation index, objective pain assessment can be achieved without relying on continuous subjective input, making it applicable to various clinical scenarios. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the steps of a human pain evaluation method based on finite element simulation provided in an embodiment of this application.

[0025] Figure 2This is a flowchart of the human pain evaluation method based on finite element simulation provided in the embodiments of this application;

[0026] Figure 3 This is a structural block diagram of a human pain assessment device based on finite element simulation provided in an embodiment of this application;

[0027] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application;

[0028] Figure 5 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] This application embodiment establishes an individual three-dimensional finite element model of tissue, performs stress-strain simulation, and combines a small number of subjective pain scores for threshold calibration and verification to calculate the pain stimulus index. This achieves objective pain evaluation without relying on continuous subjective input, making it applicable to various clinical scenarios. Specifically, by establishing an individualized three-dimensional finite element model of the patient, the stress-strain distribution of the postoperative incision area under different body positions, external forces, or muscle tension is simulated. This quantitatively describes the mechanical stimuli that may trigger pain from a physical mechanism perspective, overcoming the shortcomings of traditional subjective assessments in revealing the pain mechanism and achieving quantification of pain stimuli based on mechanical mechanisms. Furthermore, by combining a limited number of subjective pain scores, the mechanical pain threshold in the simulation results is individually calibrated, establishing a patient-specific "stress-pain" mapping relationship, significantly improving the accuracy and clinical interpretability of pain risk prediction, and achieving mechanical pain threshold mapping and individualized correction. In addition, in the complete... Once a threshold is calibrated, the system can predict the high-risk areas and intensity levels of postoperative pain in the incision area under different body positions or operational scenarios, based on finite element simulation and pain stimulus index calculation. This reduces reliance on continuous subjective reports from patients and is particularly suitable for risk assessment of patients who are sedated, have impaired consciousness, or have limited expression. The distribution of pain stimuli can be presented intuitively in the form of a heat map, clearly marking the location and extent of high-risk areas. This provides medical staff with directly applicable decision support information for operations such as body positioning, pressure fixation, and analgesia intervention, which helps optimize pain management, improve postoperative comfort, visualize risk distribution, and assist in clinical decision-making.

[0031] Reference Figure 1The diagram illustrates a flowchart of a human pain evaluation method based on finite element simulation provided in an embodiment of this application, which may specifically include the following steps:

[0032] Step S101: Establish an individual three-dimensional finite element model of the target object.

[0033] In the embodiments of this application, an individualized geometric and mechanical carrier for pain biomechanics analysis can be built, providing a basic model support for subsequent simulation of the stress state of local tissues under different working conditions, and ensuring that the simulation analysis fits the actual anatomical structural characteristics of the target object.

[0034] In some embodiments of this application, finite element modeling of local human tissues can be performed. Finite element modeling of local human tissues is a process of digitizing and structuring the geometric structure, hierarchical features, and mechanical properties of biological tissues. Its purpose is to establish a computational model that can accurately reflect the differences in individual anatomical features and tissue structures. In practical applications, its basic principle is to use medical imaging and computer modeling technology to transform complex physiological tissues into discretized models that can be used for mechanical analysis.

[0035] Optional, such as Figure 2 As shown, individual 3D tissue modeling can be achieved by obtaining geometric information of the target area through medical imaging, then performing geometric modeling based on the geometric information of the target area, and finally meshing after completing the geometric modeling.

[0036] Step S102: Perform finite element simulation using the individual three-dimensional finite element model of the target object to obtain the stress-strain distribution results.

[0037] In some embodiments of this application, the constructed individual three-dimensional tissue finite element model can be used for finite element analysis. In the finite element analysis of individualized local human tissues, this is specifically manifested in the calculation of stress-strain distribution results. The core of stress-strain calculation lies in obtaining the real mechanical response under specific loads based on accurate geometric structure and tissue properties. This enables the quantitative acquisition of the stress-strain distribution law of the target area (such as postoperative incision) under different body positions, external forces, or muscle tension from the physical mechanism level. This provides core data support for the quantitative description of the mechanical stimulation that causes pain, overcoming the shortcomings of traditional subjective assessment in revealing the mechanism of pain.

[0038] Optional, such as Figure 2 As shown, the entire process of finite element analysis can include setting clinical scenario loads and boundary conditions, assigning material parameters and constitutive models, modeling contact behavior, numerical solution and convergence analysis, model validation, and stress-strain field and statistical analysis. This methodology, based on anatomical accuracy, physical plausibility, and numerical stability, aims to achieve highly reliable tissue mechanical characterization.

[0039] The loads set are mainly used to simulate the various external forces and corresponding stress characteristics that tissues actually experience under physiological or pathological conditions; the boundary conditions set are mainly used to simulate the anatomical support and constraint states that tissues are actually in; the constitutive model constructed is mainly used to indicate the inherent mechanical properties of different types of tissues; the contact conditions set are mainly used to indicate the interactions between different tissue layers, between tissues and bone surfaces, and between tissues and external support surfaces; the generated stress-strain distribution results are mainly used to indicate the degree of deformation and energy concentration characteristics of tissues under external loads, which can include stress field distribution and strain field distribution, where stress field distribution is used to indicate the stress magnitude of each element and strain field distribution is used to indicate the strain magnitude of each element.

[0040] Step S103: Obtain the mechanical pain threshold of various tissues, and calculate the mechanical pain stimulation index based on the mechanical pain threshold and stress-strain distribution results of various tissues.

[0041] In some embodiments of this application, the inherent mechanical pain threshold of tissue can be combined with stress-strain simulation data to calculate a preliminary mechanical pain stimulation index, thereby establishing a preliminary correlation bridge between mechanical stimulation parameters and pain-related indicators and realizing the preliminary quantification of pain stimulation based on tissue mechanical properties.

[0042] Specifically, after obtaining the stress-strain distribution through finite element simulation, the calculation results can be further transformed into a quantitative indicator of pain risk, thereby achieving a mapping from "mechanical response" to "pain perception." For example... Figure 2 As shown, the basic idea can be expressed as identifying threshold regions based on the mechanical pain threshold of the tissue, specifically identifying pain risk areas within the tissue that reach or exceed the threshold, and calculating a pain stimulus index accordingly to establish a correlation between stress-strain distribution and pain or injury threshold. Furthermore, pain heatmaps can be visualized to intuitively reflect the spatial distribution and intensity level of pain risk.

[0043] It should be noted that the mechanical pain threshold varies for different tissue types, and a stratified threshold setting can be used in practical applications. Furthermore, the pain stimulus index calculated in this step is the pain stimulus index obtained during the simulation phase and can be called the mechanical pain stimulus index, which differs from the pain stimulus index calculated subsequently by combining the mechanical simulation results with the physiological mechanisms of pain. This application does not impose any limitations on this aspect.

[0044] Step S104: Obtain the established threshold mapping relationship between the mechanical pain stimulation index and the subjective pain evaluation of the target subject.

[0045] In some embodiments of this application, a target-specific "stress-pain" threshold mapping relationship can be established by matching a small number of subjective pain assessments with mechanical pain stimulation indices, thereby completing the individualized correction of simulation results and significantly improving the accuracy and clinical interpretability of subsequent pain risk prediction.

[0046] Optionally, the core idea of ​​subjective pain assessment and threshold calibration is to correlate the subject's subjective pain experience with the pain stimulus index obtained from finite element simulation, thereby achieving individualized model calibration and making the numerical results closer to the patient's actual pain experience. For example... Figure 2 As shown, the process of subjective pain assessment and threshold calibration may include steps such as subjective pain score collection, standardized stimulus design, simulation data correlation analysis, and establishment of threshold mapping relationships.

[0047] Among them, the subjective pain evaluation of the target object can be obtained by collecting data based on performing several standardized actions or postural changes on the target object as pain induction conditions of different intensities; the mechanical pain stimulation index can be obtained by using an established individual three-dimensional tissue finite element model, inputting the corresponding boundary conditions and external force parameters, calculating the stress and strain distribution of the tissue, and then calculating the corresponding mechanical pain stimulation index.

[0048] In practical applications, after completing a one-time threshold calibration, the high-risk areas and intensity levels of postoperative pain in the incision region can be predicted under different body positions or operational scenarios, relying on finite element simulation and pain stimulus index calculation. Specifically, at this point, the established threshold mapping relationship between the mechanical pain stimulus index and the subjective pain evaluation of the target subject can be directly obtained. Based on the threshold mapping relationship, the individual pain threshold of the target subject can be obtained through mapping calibration, realizing an integrated mapping from subjective perception to objective simulation.

[0049] Step S105: Calculate the individual pain stimulus index of the target object based on the individual pain threshold and stress-strain distribution results.

[0050] The core idea of ​​objective pain assessment is to combine finite element simulation results with individualized pain thresholds to form a quantifiable, visualized, and interpretable pain assessment system for clinical pain early warning and intervention guidance.

[0051] Optional, such as Figure 2 As shown, the system outputs results at three levels: pain stimulus index, pain distribution heatmap, and pain risk grading, enabling dynamic prediction from local mechanical response to overall pain risk.

[0052] In some embodiments of this application, an individual pain stimulus index specific to the target subject is calculated based on individualized pain threshold and stress-strain distribution data after individualized correction, so as to achieve objective and quantitative evaluation of pain, get rid of the dependence on continuous subjective reports from patients, and is especially suitable for special patients who are sedated, have impaired consciousness or limited expression.

[0053] Step S106: Visualize the spatial distribution results of the individual pain stimulus index to generate a pain heatmap of the target object.

[0054] Pain thermograms are primarily used to reflect the spatial distribution and intensity level of pain risk.

[0055] Optionally, the spatial distribution results of individual pain stimulus indices can be visualized, thereby transforming the abstract individual pain stimulus indices into intuitive spatial distribution heatmaps. This can clearly indicate the location and extent of high-risk pain areas, providing visual decision support for clinical operations such as postural adjustments, pressure fixation, and analgesia interventions by medical staff, and helping to optimize postoperative pain management plans.

[0056] Furthermore, based on threshold mapping relationships, the pain risk level of the target object can be output. This is achieved by comparing the individual pain stimulus index with the individual pain threshold and outputting the pain risk level. In practical applications, the individual pain threshold includes thresholds for different pain risk levels, in which case the pain risk level can be output directly.

[0057] In some embodiments of this application, the individualized finite element modeling process in step S101, such as Figure 2 As shown, geometric information of the target area can be obtained through medical images, then geometric modeling can be performed based on the geometric information of the target area, and finally mesh generation can be performed after the geometric modeling is completed.

[0058] Specifically, step S101 may include the following sub-steps:

[0059] Sub-step S11: Acquire medical images and obtain the outlines of different types of tissues by performing tissue segmentation on the medical images; the outlines of different types of tissues can be used to form a multi-level tissue segmentation model.

[0060] In practical applications, firstly, high-resolution medical imaging techniques, such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging), ultrasound, or optical scanning, can be used to obtain geometric information of the target area.

[0061] Different imaging modalities focus on different tissue types. For example, CT can clearly depict bone tissue boundaries, while MRI can reflect soft tissue layers such as muscle, cartilage, fat, and fascia. Optionally, after the image data is imported into professional medical image processing software, automatic or semi-automatic segmentation algorithms (such as thresholding, region growing, and boundary detection) can be used to extract the contours of different tissue types, forming a multi-level tissue segmentation model. Optionally, to ensure the individualization and accuracy of the model, manual intervention can be performed, such as noise removal, repair of defective areas, and artifact elimination, to make the geometric structure conform to physiological and anatomical characteristics.

[0062] Sub-step 12 converts the multi-level tissue segmentation model into a three-dimensional surface model and performs geometric optimization on the three-dimensional surface model to obtain the target three-dimensional surface model; the obtained target three-dimensional surface model is a high-quality three-dimensional surface model that is geometrically continuous, topologically correct and conforms to individual anatomical characteristics.

[0063] Then, the segmentation results, i.e., the contours of different types of tissues, can be converted into three-dimensional surface models. These models undergo geometric optimization, including smoothing, denoising, and topological repair, to ensure their closure and continuity. For thinner tissue layers (such as skin, fascia, and cartilage), thickness reconstruction can be performed based on image thickness or literature data to restore their volumetric characteristics.

[0064] Optionally, after tissue segmentation is completed in medical imaging, different types of tissues typically exist in the form of voxelized labeled data, which can then be further transformed into continuous geometric models suitable for mechanical analysis. Specifically, for the segmented three-dimensional volume data, isosurface extraction algorithms (such as Marching Cubes and its improved forms) can be used to generate initial three-dimensional surface models composed of triangular facets, realizing the transformation from discrete voxel boundaries to continuous surface geometry.

[0065] However, due to limitations in image resolution, noise interference, and segmentation errors, initial surface models often suffer from problems such as stepped boundaries, high-frequency geometric oscillations, isolated small facets, and local discontinuities. These defects can lead to mesh distortion and non-physical computation results in subsequent finite element analysis. Therefore, systematic geometric optimization of the surface model is necessary. Specifically, this involves first reducing high-frequency noise introduced by voxel discretization using surface smoothing methods (such as Laplacian or Taubin smoothing), improving surface continuity under finite iterations and controlled smoothing intensity, while avoiding significant non-physiological shrinkage of tissue volume. Subsequently, denoising is combined to remove isolated facets and anomalous geometric protrusions caused by missegmentation or artifacts, and normal consistency constraints are used to improve the overall geometric coherence of the surface. Furthermore, for potential holes, open boundaries, self-intersections, and non-manifold topologies in the model, automatic or semi-automatic topology repair techniques can be used to reconstruct and close defective regions, ensuring that the geometric model meets closure, topological consistency, and manifold conditions. This is a crucial prerequisite for the stability of finite element mesh generation and numerical computation.

[0066] For thin tissues (such as skin, fascia, or cartilage) whose thickness is difficult to discern directly in medical images, thickness reconstruction can be performed by equidistant offsets along the normal direction based on the extracted surface, combined with image resolution or physiological thickness ranges reported in the literature, thereby restoring their reasonable volumetric characteristics. This application does not limit this approach.

[0067] Through the above geometric transformation and optimization process, a high-quality three-dimensional surface model that is geometrically continuous, topologically correct, and conforms to individual anatomical characteristics can be obtained, laying a solid geometric foundation for subsequent finite element mesh generation and reliable biomechanical analysis.

[0068] Sub-step S13 involves meshing the target 3D surface model to obtain a finite element mesh.

[0069] After completing the geometric modeling, mesh generation can be performed, which involves discretizing the continuous geometric model into a finite element mesh composed of nodes and elements. Typically, second-order tetrahedral elements are used for complex shapes, while hexahedral elements are used for regular or layered structures to improve computational stability and accuracy. It should be noted that mesh generation must ensure good element shape, reasonable size, and smooth transitions, and appropriate mesh refinement should be applied in areas of structural abrupt changes, contact interfaces, or stress concentrations to ensure more accurate subsequent physical analysis.

[0070] Optionally, in the process of finite element modeling of local human tissues, mesh generation is usually carried out in sections after the high-quality three-dimensional geometric model is built, in order to balance geometric adaptability and numerical accuracy.

[0071] Specifically, the geometric model can be divided into regions based on the anatomical characteristics and mechanical properties of different tissues, treating areas with complex shapes, dramatic curvature changes, and multiple tissue intersections as separate sub-regions. For these regions, second-order tetrahedral mesh generation algorithms (such as those based on the Delaunay method) are preferred to achieve accurate fitting of complex boundaries and surface geometry. Simultaneously, by controlling the control unit size field, the mesh can be appropriately refined in areas of geometric abrupt changes, contact interfaces, and potential stress concentration areas to improve local computational accuracy.

[0072] For regions with regular geometric shapes or obvious layered structures, geometric decomposition or layering can be performed first, followed by the use of a hexahedral mesh generation method to arrange regular element layers along the tissue thickness direction, accurately describing the deformation gradient and mechanical response at different levels. In mixed mesh regions, transition zones can be set to achieve smooth connections between tetrahedral and hexahedral elements, avoiding abrupt changes in element size and shape. After mesh generation, the overall mesh quality is systematically checked, including element aspect ratio, twist, Jacobian value, and transition between adjacent element sizes. If necessary, local areas are re-meshed or smoothed to ensure that the resulting finite element mesh meets the stability and accuracy requirements for complex human tissue mechanical analysis.

[0073] Sub-step S14: Based on the finite element mesh, process the boundary relationships of different tissues in the tissue segmentation model to obtain the individual three-dimensional tissue finite element model.

[0074] After the mesh is generated, the boundary relationships between different tissues can be processed, such as the connection interface between bone and soft tissue, the sliding contact surface between muscle and fascia, etc. By defining the contact surface or coupling relationship, reasonable interaction between different tissues can be achieved.

[0075] Optionally, after the finite element mesh is generated, it can be refined based on the boundary relationships between different structures to accurately describe their mechanical interaction mechanisms.

[0076] Specifically, depending on the anatomical features of the tissue and the form of relative motion, different types of contact or coupling algorithms are typically used to model interfaces. For interfaces between bone and soft tissue, such as bone-tendon, bone-ligament, or bone and tightly attached soft tissue, where relative slippage is minimal under physiological conditions, tied contact or constraint coupling algorithms are commonly used. These algorithms enforce consistency in displacement or velocity between nodes on both sides of the interface, achieving continuous and stable load transfer and preventing interface detachment or penetration. These algorithms are suitable for rigid or semi-rigid attachment areas and can effectively improve computational convergence and numerical stability. In contrast, for tissue interfaces that allow relative slippage, such as muscle-fascia or fascia-subcutaneous fat, explicit or implicit contact algorithms are typically used, such as node-to-segment contact or surface-to-surface contact methods. In these methods, one surface is designated as the master surface, and the nodes or patches on the other side are designated as slave surfaces. Contact search and constraint equations prevent normal penetration while allowing tangential relative slippage. Contact constraints are often implemented using the penalty method or the Lagrange multiplier method, ensuring numerical stability while meeting contact accuracy requirements. For soft tissue interfaces, frictionless or low-friction models can be further defined to reflect actual physiological lubrication characteristics. To improve the robustness of contact calculations, local mesh refinement and good element quality are typically performed in the contact region.

[0077] It should be noted that a consistency check can be performed on the model at the end, including geometric integrity, topological continuity, and mesh quality, to ensure that the model can be used for subsequent mechanical analysis and parameter assignment. Through the above process, a finite element model of a local human tissue based on individual medical images, with realistic structure, clear layering, and reasonable mesh is established, laying the foundation for further stress, strain, and tissue response analysis.

[0078] In some embodiments of this application, the finite element calculation process in step S102 can be achieved by setting clinical scenario loads and boundary conditions, assigning material parameters and constitutive models, modeling contact behavior, numerical solution and convergence analysis, model verification, and stress-strain field and statistical analysis.

[0079] Specifically, step S102 may include the following sub-steps:

[0080] Sub-step S21: In the individual three-dimensional tissue finite element model, set the load and boundary conditions according to the physiological or pathological state of the tissue, and establish the corresponding constitutive model and set the contact conditions according to the tissue characteristics of different types of tissues.

[0081] In the process of finite element simulation, firstly, the load and boundary conditions can be reasonably set according to the physiological or pathological state of the tissue in order to reproduce the state of the local tissue under actual stress environment.

[0082] Optionally, the boundary conditions can be set by treating the bony structures or deep supporting tissues as reference boundaries in the geometric model and applying necessary translational or rotational constraints.

[0083] For load setting, external forces are applied to the surface or nodes of the model in the form of force, pressure, displacement, or acceleration to simulate conditions such as compression, friction, heavy load, or muscle tension. For slow loading processes, steady-state or quasi-static conditions should be used to match low-rate stress scenarios; while for rapid response problems involving gait or impact, time-history loads can be applied to capture the dynamic response of the tissue.

[0084] It should be noted that the definitions of loads and boundary conditions can be consistent with the actual anatomical structure and stress characteristics to ensure the physiological significance and interpretability of the simulation results.

[0085] Secondly, the individual three-dimensional tissue finite element model only obtains the geometric model by scanning the tissue and then performing finite element mesh generation. In order to perform finite element calculations, it is also necessary to assign constitutive models to the mesh elements, that is, to establish corresponding constitutive models according to the characteristics of different types of tissues.

[0086] Optionally, soft tissues typically exhibit nonlinearity, anisotropy, and viscoelasticity, and their tensile properties can be described using a fiber-reinforced hyperelastic model, with viscoelastic branches superimposed to reflect time-dependent effects. Directional tissues such as muscles, fascia, and ligaments should have their anisotropy described using fiber orientation parameters. Subcutaneous fat and loose connective tissue can be described using a compressible hyperelastic model. Bone tissue can use anisotropic linear elastic or elastoplastic models. Cartilage and intervertebral discs can use biphasic or pore elastic models to describe solid-fluid coupling behavior. This application does not impose any limitations on these aspects.

[0087] Setting contact conditions is a crucial step in local tissue finite element modeling. The interactions between different tissue layers, between tissue and bone surfaces, and between tissue and external support surfaces need to be described using contact algorithms.

[0088] Optionally, cartilage-cartilage interfaces typically employ non-penetrating, low-friction contact; while frictional contact is used between soft tissue and the external load body, with the friction coefficient set based on surface roughness and material properties. To obtain accurate contact stress and shear strain distributions, the contact region should undergo local mesh refinement, and sensitivity analyses should be performed on contact stiffness, friction parameters, and slip conditions. In the contact solution, a stepwise loading and penalty factor adjustment strategy can be employed to avoid penetration and non-physical stress peaks, thereby ensuring the numerical stability of the contact behavior.

[0089] Sub-step S22 involves numerical solution and convergence analysis based on loads and boundary conditions, constitutive model, and contact conditions to obtain model prediction results.

[0090] After defining the geometry, materials, and contact, numerical solutions and convergence analyses can be performed to verify the accuracy and stability of the model.

[0091] Optionally, the matching of solution algorithms during the numerical solution process can be interpreted as follows: for quasi-static problems such as slow loading or steady-state compression, implicit integration algorithms can be used. These algorithms, based on Newton-Raphson iteration, can efficiently handle strongly nonlinear and contact problems and provide high-precision equilibrium solutions after convergence. However, in situations involving rapid loading, impact, drop, or gait-based periodic excitation with significant inertial effects, explicit integration algorithms can be used. It should be noted that explicit methods do not rely on matrix inversion and can capture high-frequency dynamic responses within short time steps, making them suitable for soft tissue dynamics analysis involving large deformations, frequent contact, and significant nonlinear propagation.

[0092] Optionally, convergence analysis may include spatial grid convergence and time step convergence.

[0093] Specifically, for spatial grid convergence, the model's grid independence can be assessed by comparing the changes in key output quantities (such as maximum principal strain, peak contact pressure, and strain energy density) under different grid densities. For time step convergence, the rationality of the time integration step should be further verified for calculations containing time effects.

[0094] Generally, when the difference in key mechanical properties between medium and fine meshes is less than 5%–10%, the model can be considered to have achieved spatial convergence. Simultaneously, energy balance, contact penetration, and residual force levels need to be monitored to ensure the stability of the numerical results. Through systematic convergence testing, it can be ensured that the model output results are independent of the discrete scale and time resolution. It should be noted that this step mainly tests the convergence of the established finite element model, especially the mesh and the set time step. For finite element calculations, the time step and mesh size affect the calculation results, but as the mesh is refined and the time step is reduced, a convergent solution can be obtained; that is, further reducing the mesh and time step will no longer significantly affect the calculation results. Therefore, convergence analysis of the mesh and time step can be performed before conducting finite element analysis.

[0095] Sub-step S23 verifies the model prediction results to obtain the verified calculation results.

[0096] Model validation is a crucial step in ensuring the credibility of numerical simulations.

[0097] Optionally, the validation process involves comparing the model's predicted results with experimental or clinical observation data, including comparisons of indicators such as contact pressure distribution, displacement field, deformation curve, and stress concentration area. In vitro tests or surface measurements (such as force sensors, pressure pads, and image displacement tracking) can provide reference data to assess the model's accuracy in macroscopic and local responses.

[0098] Furthermore, parameter sensitivity analysis and uncertainty analysis can be conducted to assess the influence range of material parameters, friction coefficients, and boundary loads on the calculation results, thereby defining the confidence interval of the model's predictions. After verification, the confidence level and applicable scope of the model can be determined based on its application objectives. This application does not impose any limitations on these aspects.

[0099] Sub-step S24 involves post-processing and quantitatively analyzing the stress-strain field of the calculation results to obtain the stress-strain distribution results.

[0100] After numerical solution and verification are completed, the calculation results can be post-processed and quantitatively analyzed for stress-strain fields, that is, stress-strain field extraction and statistical analysis can be performed to obtain stress-strain distribution results.

[0101] Optionally, stress-strain field extraction and statistical analysis can be performed by extracting statistical indicators such as maximum value, 95th percentile, average value, and volume fraction exceeding a threshold for a specific region. These indicators can quantify the degree of tissue deformation and energy concentration characteristics under external loads, and can be further used to establish the correlation between stress-strain distribution and pain or injury thresholds. In other words, the stress-strain distribution results can primarily be used to indicate the degree of tissue deformation and energy concentration characteristics under external loads.

[0102] Among them, the stress evaluation index can mainly use Von Mises stress; the strain evaluation index can mainly use equivalent strain.

[0103] For example, after the finite element method is completed, the solver outputs the strain and stress components at each integration point. For the Von Mises stress σ... vM It can be calculated using the following formula:

[0104]

[0105] In the formula, σ1, σ2 and σ3 are principal stresses.

[0106] Equivalent strain ε eq It can be calculated using the following formula:

[0107]

[0108] In the formula, ε xx ε yy ε zz For the normal strain component, ε xy ε yz ε xz For shear strain components.

[0109] In some embodiments of this application, the pain stimulus index calculation process in step S103 can be achieved through threshold region identification, pain stimulus index calculation, and pain heatmap visualization.

[0110] Specifically, by comparing the simulated stress field with the mechanical pain threshold, the overthreshold region can be identified, and the pain stimulus index can be calculated using methods such as overthreshold volume fraction, peak extraction, or spatial integration to form a quantitative pain risk index. Step S103 may specifically include the following sub-steps:

[0111] Sub-step S31 identifies pain risk areas within tissues based on the mechanical pain threshold and stress-strain distribution results of various tissues.

[0112] First, the pain threshold for various tissues can be determined, which is the minimum stimulus intensity required to elicit a pain response. Pain thresholds can be determined based on existing literature, experimental data, or individualized assessments. Thresholds typically differ between tissue types; for example, superficial tissues such as skin and mucous membranes have lower thresholds, while deep fascia, muscles, or connective tissues have higher thresholds. Local thresholds may also decrease during inflammation or postoperative healing. Optionally, to ensure the physiological rationality of the assessment, a stratified threshold setting can be used, allowing different biomechanical thresholds to correspond to different structures such as skin, fascia, and muscles.

[0113] Next, pain risk areas within the tissue can be identified. Specifically, the mechanical pain threshold of various tissues can be mapped to stress and strain field distributions. By comparing the stress or strain magnitude of each unit with the corresponding mechanical pain threshold, it can be determined whether it exceeds the pain threshold. If the local stress or strain of a unit exceeds the threshold, the area is considered a potential pain trigger point or high-risk area. In other words, areas where the stress or strain magnitude exceeds or reaches the corresponding mechanical pain threshold can be identified as pain risk areas within the tissue. This step enables the spatial identification of over-threshold areas within the tissue, providing a foundation for subsequent pain index calculations.

[0114] Sub-step S32 involves statistically analyzing the proportion of pain risk areas in the individual's three-dimensional tissue finite element model, and combining this proportion with the time dimension to calculate the change of the mechanical pain stimulation index over time, thereby obtaining information on the peak value and duration of the pain stimulation.

[0115] Sub-step S33: Based on the peak and duration information of the pain stimulus, the mechanical pain stimulus index is obtained.

[0116] The pain stimulus index is used to quantify the overall level of pain risk. It can be used to reflect the spatial range and intensity of pain areas by statistically analyzing the proportion of units that exceed a threshold in the overall model.

[0117] When dealing with transient loading (such as coughing or changes in body position), the change of the pain stimulus index over time can be calculated by combining the time dimension to obtain information on the peak value and duration of the pain stimulus.

[0118] Specifically, under transient loading conditions, the stress and strain fields within human tissues evolve continuously over time. Therefore, the calculation of the pain stimulus index needs to incorporate a time dimension to reflect the dynamic characteristics of pain risk. In practice, during the transient finite element method (FEM) calculation, the solver outputs complete stress and strain field distributions at each time step. Based on pre-defined pain thresholds for different tissues, the set of elements whose stress or strain exceeds the threshold is identified at each time step, and their proportion of the total number of elements in the model is calculated, thus obtaining the instantaneous pain stimulus index at that moment. By repeating the above process for all time steps, a function of the pain stimulus index changing over time can be obtained. From this, key quantities reflecting the dynamic characteristics of pain can be extracted, including the peak value of the pain stimulus index (characterizing the strongest instantaneous pain risk), the duration (i.e., the time interval during which the index exceeds a given level), and the rise and fall processes with loading.

[0119] Furthermore, the time history can be integrated to obtain a cumulative index reflecting the overall pain load, which can characterize the different biomechanical effects induced by short-term strong stimulation or long-term moderate stimulation. This application does not limit this aspect.

[0120] Finally, the spatial distribution of the pain stimulus index can be visualized to generate a pain thermogram. Optionally, the pain intensity of different locations can be displayed using color gradients or grayscale changes, with high-risk areas highlighted in red and low-stimulation areas marked in green or light colors. It should be noted that the pain thermogram can be overlaid on the patient's anatomical model or image slices to visually demonstrate the location, extent, and trend of pain areas. This result can not only be used for postoperative pain risk assessment but also provide guidance for clinical interventions, such as optimizing positioning, adjusting brace compression direction, or developing local analgesia plans.

[0121] In some embodiments of this application, the threshold mapping relationship obtained in step S104 is already established. The process of establishing the threshold mapping relationship is a step in subjective pain evaluation and threshold calibration. For example... Figure 2 As shown, the process of subjective pain assessment and threshold calibration may include steps such as subjective pain score collection, standardized stimulus design, simulation data correlation analysis, and establishment of threshold mapping relationships.

[0122] Specifically, subjective pain evaluations of the target object can be collected by performing several standardized actions or posture changes on the target object as pain induction conditions of different intensities. Then, a threshold mapping relationship between the mechanical pain stimulation index and the target object's subjective pain evaluation can be established. Specifically, the subjective pain evaluation under each action or posture is analyzed in correspondence with the mechanical pain stimulation index. The threshold mapping relationship between subjective pain evaluation and mechanical pain stimulation index is established through regression or fitting methods, thereby determining the individual pain threshold and response curve of the target object, and realizing subjective pain evaluation and threshold calibration.

[0123] The pain stimulus index does not simply reflect the spatial extent of pain, but rather it is a weighted cumulative sum of the stress amplitude exceeding the tissue pain threshold and the volume of the stimulated tissue, used to quantitatively characterize the comprehensive damage activation level experienced by human tissue. This index can simultaneously reflect both the "intensity" and "range of stimulation," thus forming a physical quantitative expression consistent with the degree of nerve ending activation.

[0124] Subjective pain scores (such as NRS and VAS) reflect a patient's perceived output of overall pain intensity. This perceived output is closely related to the number of activated nociceptors and their discharge characteristics at the neurophysiological level, and is influenced by factors such as central nervous system modulation.

[0125] This application's embodiments construct a pain stimulus index to establish a monotonic mapping relationship between the patient's subjective pain intensity and the amount of physical stimulus. Specifically, it uses initial subjective scores to fit and correct thresholds or stress-pain relationships, thereby correcting individual pain sensitivity and improving prediction accuracy and clinical applicability.

[0126] Optionally, during the calibration process, a period when the patient is awake and the analgesic effect is stable can be selected to collect subjective or objective pain scores. For example, an appropriate evaluation method can be selected based on the patient's communication ability: numerical or visual scales can be used for those who can express themselves, while behavioral or observational rating tools can be used for those who cannot. This application does not impose any limitations on these methods.

[0127] To ensure comparability of results, several standardized actions or postural changes can be designed as pain-inducing conditions of varying intensities, such as lying still, postural adjustment, or short-term exertion. While performing these actions, an established individual patient finite element model can be used, with corresponding boundary conditions and external force parameters input, to calculate the tissue stress and strain distribution and the corresponding pain stimulus index. In practical applications, this can be understood as the mechanical pain stimulus index calculated in step S103, which is the simulation result obtained based on the aforementioned input conditions for subsequent correspondence analysis. Subsequently, the subjective pain evaluation under each action can be correlated with the mechanical pain stimulus index, and a mapping relationship between pain scores and pain stimulus indices can be established through regression or fitting methods, thereby determining the individual pain threshold and response curve of the target subject. This process can be considered a "calibration" between the model and the patient's pain perception, and usually only requires a few actions to complete.

[0128] Optionally, the determination of an individual's pain threshold can be achieved through a standardized calibration process.

[0129] First, based on commonly used clinical pain management standards, several reference pain levels with clear clinical significance can be pre-defined. For example, an NRS score of 4 can be defined as the "moderate pain threshold" and suggest routine intervention, while an NRS score of 7 can be defined as the "severe or breakthrough pain threshold" and suggest warning or enhanced intervention. Then, under conditions of relatively stable analgesia, subjects can be asked to perform various standardized movements or postural changes, and the subjective pain score for each movement can be recorded. Simultaneously, based on the patient's individual finite element model, the pain stimulus index I under these movement conditions can be calculated. pain , forming (I pain A paired dataset of pain stimulus index (NRS) is used. With the pain stimulus index as the independent variable and the NRS score as the dependent variable, a personalized pain stimulus-perception mapping function is established using monotonic regression or nonlinear fitting (such as logistic or sigmoid functions): NRS = f(I...). pain After the mapping function is constructed, a preset reference pain level can be substituted into the function, and its corresponding pain stimulus index value can be calculated, for example, by solving... The patient's moderate pain threshold was obtained. and severe pain threshold .

[0130] Once calibrated, the model can automatically predict pain intensity and distribution under different body positions or operational conditions without relying on frequent subjective ratings. If the patient's condition, analgesia status, or tissue conditions change significantly, the threshold and mapping relationship can be quickly updated through a simplified recalibration, ensuring the model remains consistent with clinical reality. For example, in any subsequent operational situation, only the current pain stimulus index I needs to be calculated using finite element analysis.pain and with , By comparing these parameters, it is possible to determine whether a moderate or severe level of pain has been reached without subjective scoring input, thereby enabling pain risk prediction and early warning. In this way, pain assessment achieves an integrated mapping from subjective perception to objective simulation, providing a basis for pain early warning and intervention decisions in nursing, rehabilitation, or postoperative management.

[0131] In some embodiments of this application, the pain objectification assessment output achieved in steps S105 and S106 can be specifically implemented through an objectification assessment system, such as... Figure 2 As shown, the system can output results at three levels: pain stimulus index, pain distribution heatmap, and pain risk classification, enabling dynamic prediction from local mechanical response to overall pain risk.

[0132] Specifically, this involves the simulation calculation stage, which can be represented by the aforementioned steps S101 to S103.

[0133] Optionally, during the simulation calculation phase, the system can automatically analyze the stress and strain distribution of tissues under specific body positions, movements, or external loads based on the patient's individual threshold model. It can extract information such as the proportion of out-of-threshold regions, high-quantile stress values, strain energy density, and peak pressure, and calculate the individual pain stimulus index accordingly. This individual pain stimulus index comprehensively reflects the spatial range and intensity of the pain stimulus, serving as the basis for subsequent risk grading and visualization output.

[0134] Subsequently, the system can spatially map the pain stimulus index onto the individual's three-dimensional finite element model of tissue established in S101, generating a pain heatmap. Different colors represent different risk levels, with red areas indicating high-risk areas and green areas indicating low-risk areas. It should be noted that the pain heatmap can be overlaid on the patient's individual anatomical structure, clearly showing the spatial distribution and concentration areas of pain, facilitating medical staff to intuitively determine the source of pain and the location of risk.

[0135] Then, by combining the pain stimulus index with the patient's previous subjective pain score mapping, the system can automatically output a pain risk classification, such as mild, moderate, and severe pain, and can dynamically update it with changes in body position, external force application, or time progression, achieving real-time pain risk monitoring. Nursing staff can use this information to adjust body position, reduce pressure, or administer local analgesia in advance; doctors can also optimize analgesic drugs or interventions based on the risk level. It should be noted that the individualized pain threshold obtained through the mapping of "pain stimulus index – subjective pain score" in this embodiment can correspond to different intervention levels such as moderate pain (NRS≥4) and severe pain (NRS≥7), achieving individualized pain level prediction, classification, and early warning.

[0136] In clinical applications, individual three-dimensional finite element models can be established using patient medical images to simulate the stress-strain distribution of the incision area under different postoperative positions and external force conditions. Then, combined with literature and a small number of subjective scores, the mechanical pain threshold can be individually calibrated. The mechanical simulation results are combined with the physiological mechanism of pain to calculate the individualized pain stimulus index, and the high-risk pain area is output intuitively in the form of a heat map, so as to realize the mechanistic quantification and individualized risk assessment of postoperative incision pain.

[0137] This system can predict high-incidence areas of pain, guide nursing and operational pathway planning, avoid stimulating high-risk areas, and is also used for postoperative pain tracking and efficacy evaluation. Furthermore, as patients recover, the system can continuously output pain change trends, providing objective quantitative evidence for individualized analgesia and rehabilitation. Objective pain results can be output through interactive 3D heatmaps, pain risk reports, or system integration interfaces, supporting real-time access in hospital information systems or nursing terminals, enabling digital and intelligent management of pain monitoring, early warning, and intervention.

[0138] In this embodiment, a three-dimensional finite element model of the target object is established, and finite element simulation is performed using this model to obtain stress-strain distribution results. Then, the mechanical pain thresholds of various tissues are acquired, and a mechanical pain stimulation index is calculated based on these thresholds and the stress-strain distribution results. By obtaining the established threshold mapping relationship between the mechanical pain stimulation index and the target object's subjective pain evaluation, this mapping relationship can be used to calibrate and obtain the target object's individual pain threshold. At this point, the individual pain stimulation index can be calculated based on the individual mechanical pain threshold and stress-strain distribution results, and the spatial distribution of the individual pain stimulation index is visualized to generate a pain heatmap of the target object. The generated pain can be used to reflect the spatial distribution and intensity level of pain risk, thereby achieving an objective evaluation of the target object's human pain. By establishing a three-dimensional finite element model of the individual tissue, performing stress-strain simulation, and combining a small amount of subjective pain scores for threshold calibration and verification to calculate the pain stimulation index, objective pain evaluation can be achieved without relying on continuous subjective input, making it applicable to various clinical scenarios.

[0139] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0140] Reference Figure 3 The diagram illustrates a structural block diagram of a human pain assessment device based on finite element simulation provided in an embodiment of this application, which may specifically include the following modules:

[0141] Finite element model creation module 301 is used to create an individual three-dimensional finite element model of the target object;

[0142] Finite element simulation module 302 is used to perform finite element simulation using an individual three-dimensional finite element model of the target object to obtain stress-strain distribution results.

[0143] The mechanical pain stimulation index calculation module 303 is used to obtain the mechanical pain threshold of various tissues and calculate the mechanical pain stimulation index based on the mechanical pain threshold and stress-strain distribution results of various tissues.

[0144] The threshold mapping relationship acquisition module 304 is used to acquire the established threshold mapping relationship between the mechanical pain stimulation index and the subjective pain evaluation of the target object; the threshold mapping relationship is used to map and calibrate to obtain the individual pain threshold of the target object;

[0145] The individual pain stimulus index calculation module 305 is used to calculate the individual pain stimulus index of the target object based on the individual mechanical pain threshold and stress-strain distribution results.

[0146] The pain heatmap generation module 306 is used to visualize the spatial distribution of an individual's pain stimulus index and generate a pain heatmap of the target object; the pain heatmap is used to reflect the spatial distribution and intensity level of pain risk.

[0147] In some embodiments of this application, the finite element model establishment module 301 may include the following sub-modules:

[0148] The finite element model building submodule is used to acquire medical images. By segmenting the medical images into tissues, the contours of different types of tissues are obtained. The contours of different types of tissues are used to form a multi-level tissue segmentation model. The multi-level tissue segmentation model is converted into a three-dimensional surface model, and the three-dimensional surface model is geometrically optimized to obtain the target three-dimensional surface model. The target three-dimensional surface model is meshed to obtain a finite element mesh. Based on the finite element mesh, the boundary relationships of different tissues in the tissue segmentation model are processed to obtain an individual three-dimensional tissue finite element model.

[0149] In some embodiments of this application, the finite element simulation module 302 may include the following sub-modules:

[0150] The stress-strain distribution result generation module is used to set loads and boundary conditions in an individual three-dimensional finite element model of tissue according to the physiological or pathological state of the tissue, establish corresponding constitutive models according to the tissue characteristics of different types of tissue, and set contact conditions; based on the loads and boundary conditions, constitutive models, and contact conditions, numerical solutions and convergence analysis are performed to obtain model prediction results; the model prediction results are verified to obtain verified calculation results; and the calculation results are post-processed and quantitatively analyzed to obtain stress-strain distribution results.

[0151] Among them, the load is used to simulate the various external forces and corresponding stress characteristics that tissues actually bear under physiological or pathological conditions; the boundary condition is used to simulate the anatomical support and constraint state that tissues are actually in; the constitutive model is used to indicate the inherent mechanical properties of different types of tissues; and the contact condition is used to indicate the interaction between different tissue layers, between tissue and bone surface, and between tissue and external support surface.

[0152] In some embodiments of this application, the mechanical pain stimulation index calculation module 303 may include the following sub-modules:

[0153] The mechanical pain stimulation index calculation submodule is used to identify pain risk areas within tissues based on the mechanical pain threshold and stress-strain distribution results of various tissues; to statistically analyze the proportion of pain risk areas in the individual three-dimensional finite element model of the tissue, and to calculate the change of the mechanical pain stimulation index over time by combining the proportion with the time dimension, thereby obtaining the peak value and duration information of the pain stimulation; and to obtain the mechanical pain stimulation index based on the peak value and duration information of the pain stimulation.

[0154] In some embodiments of this application, the stress-strain distribution results include stress field distribution and strain field distribution, wherein the stress field distribution is used to indicate the stress magnitude of each element, and the strain field distribution is used to indicate the strain magnitude of each element; the mechanical pain stimulation index calculation submodule may include the following units:

[0155] The pain risk area identification unit is used to map the mechanical pain threshold of various tissues to the stress field distribution and strain field distribution. By comparing the stress or strain magnitude of each unit with the corresponding mechanical pain threshold, the area where the stress or strain magnitude exceeds the corresponding mechanical pain threshold is identified as the pain risk area within the tissue.

[0156] In some embodiments of this application, the subjective pain assessment of the target object is based on performing several standardized actions or postural changes on the target object; the apparatus provided in the embodiments of this application may further include the following modules:

[0157] The threshold mapping relationship establishment module is used to collect the subjective pain evaluation of the target object based on performing several standardized actions or posture changes on the target object as pain induction conditions of different intensities; the subjective pain evaluation under each action or posture is correlated with the mechanical pain stimulation index, and a threshold mapping relationship between the subjective pain evaluation and the mechanical pain stimulation index is established through regression or fitting methods.

[0158] In some embodiments of this application, the apparatus provided in this application may further include the following modules:

[0159] The pain risk level output module is used to compare an individual's pain stimulus index with their personal pain threshold and output the pain risk level.

[0160] In this embodiment, a three-dimensional finite element model of the target object is established, and finite element simulation is performed using this model to obtain stress-strain distribution results. Then, the mechanical pain thresholds of various tissues are acquired, and a mechanical pain stimulation index is calculated based on these thresholds and the stress-strain distribution results. By obtaining the established threshold mapping relationship between the mechanical pain stimulation index and the target object's subjective pain evaluation, this mapping relationship can be used to calibrate and obtain the target object's individual pain threshold. At this point, the individual pain stimulation index can be calculated based on the individual mechanical pain threshold and stress-strain distribution results, and the spatial distribution of the individual pain stimulation index is visualized to generate a pain heatmap of the target object. The generated pain can be used to reflect the spatial distribution and intensity level of pain risk, thereby achieving an objective evaluation of the target object's human pain. By establishing a three-dimensional finite element model of the individual tissue, performing stress-strain simulation, and combining a small amount of subjective pain scores for threshold calibration and verification to calculate the pain stimulation index, objective pain evaluation can be achieved without relying on continuous subjective input, making it applicable to various clinical scenarios.

[0161] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0162] This application also provides an electronic device, see embodiments thereof. Figure 4 The provided electronic device 400 includes a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and capable of running on the processor 420. When the computer program 411 is executed by the processor, it implements the various processes of the above-described embodiment of the human pain evaluation method based on finite element simulation and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0163] This application also provides a computer-readable storage medium, see embodiments thereof. Figure 5 The computer-readable storage medium 500 provides a computer program 411. When the computer program 411 is executed by the processor, it implements the various processes of the above-described embodiment of the human pain evaluation method based on finite element simulation and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0164] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0165] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division; in actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. Additionally, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0166] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0167] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0168] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0169] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0171] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0172] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0173] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes; these computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0176] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0177] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A method for evaluating pain in a human body based on finite element simulation, characterized by, The method includes: Establish an individual three-dimensional finite element model of the target object; Finite element simulation was performed using an individual three-dimensional finite element model of the target object to obtain stress-strain distribution results. The mechanical pain threshold of various tissues is obtained, and the mechanical pain stimulation index is calculated based on the mechanical pain threshold of various tissues and the stress-strain distribution results. Obtain the established threshold mapping relationship between the mechanical pain stimulation index and the subjective pain evaluation of the target object; the threshold mapping relationship is used to map and calibrate to obtain the individual pain threshold of the target object; Based on the individual pain threshold and the stress-strain distribution results, the individual pain stimulus index of the target object is calculated; The spatial distribution of the individual pain stimulus index is visualized to generate a pain heatmap of the target object; the pain heatmap is used to reflect the spatial distribution and intensity level of pain risk.

2. The method of claim 1, wherein, The establishment of the individual three-dimensional tissue finite element model includes: Medical images are acquired, and tissue segmentation is performed on the medical images to obtain the contours of different types of tissues; the contours of different types of tissues are used to form a multi-level tissue segmentation model; The multi-level tissue segmentation model is converted into a three-dimensional surface model, and the three-dimensional surface model is geometrically optimized to obtain the target three-dimensional surface model. The target three-dimensional surface model is meshed to obtain a finite element mesh; Based on the finite element mesh, the boundary relationships of different tissues in the tissue segmentation model are processed to obtain an individual three-dimensional tissue finite element model.

3. The method of claim 1, wherein, The stress-strain distribution results obtained by performing finite element simulation using the individual three-dimensional tissue finite element model include: In the individual three-dimensional finite element model, loads and boundary conditions are set according to the physiological or pathological state of the tissue, and corresponding constitutive models are established according to the tissue characteristics of different types of tissues, as well as contact conditions are set. Numerical solutions and convergence analyses are performed based on the loads and boundary conditions, the constitutive model, and the contact conditions to obtain model prediction results. The model prediction results are verified to obtain the verified calculation results; The calculation results are then subjected to post-processing and quantitative analysis of the stress-strain field to obtain the stress-strain distribution results. The load is used to simulate the various external forces and corresponding stress characteristics that tissues actually experience under physiological or pathological conditions; the boundary conditions are used to simulate the anatomical support and constraint state of the tissues; the constitutive model is used to indicate the inherent mechanical properties of different types of tissues; and the contact conditions are used to indicate the interactions between different tissue layers, between tissues and bone surfaces, and between tissues and external support surfaces.

4. The method of claim 1, wherein, The mechanical pain stimulation index is calculated based on the mechanical pain threshold of the various tissues and the stress-strain distribution results, including: Based on the mechanical pain threshold of the various tissues and the stress-strain distribution results, pain risk areas within the tissues are identified; The proportion of the pain risk area in the individual's three-dimensional tissue finite element model is statistically analyzed, and the change of the mechanical pain stimulation index over time is calculated by combining the proportion with the time dimension to obtain information on the peak value and duration of the pain stimulation. The mechanical pain stimulation index is obtained based on the peak value and duration information of the pain stimulus.

5. The method of claim 4, wherein, The stress-strain distribution results include stress field distribution and strain field distribution. The stress field distribution is used to indicate the stress magnitude of each element, and the strain field distribution is used to indicate the strain magnitude of each element. The identification of pain risk areas within the tissue includes: The mechanical pain threshold of the various tissues is mapped to the stress field distribution and the strain field distribution, and the stress or strain magnitude of each unit is compared with the corresponding mechanical pain threshold. The region where the stress or strain exceeds the corresponding mechanical pain threshold is identified as a pain risk area within the tissue.

6. The method of claim 1, wherein, The subjective pain assessment of the target subject is based on performing several standardized actions or postural changes on the target subject; the method further includes: Subjective pain evaluations of the target object were collected by performing several standardized actions or posture changes on the target object as pain induction conditions of different intensities. The subjective pain evaluation under each action or posture is correlated with the mechanical pain stimulation index. A threshold mapping relationship between the subjective pain evaluation and the mechanical pain stimulation index is established by regression or fitting methods.

7. The method of claim 1, wherein, The method further includes: The individual pain stimulus index is compared with the individual pain threshold to output the pain risk level.

8. A human pain evaluation device based on finite element simulation, characterized by, The device includes: The finite element model creation module is used to create individual three-dimensional finite element models of the target object. The finite element simulation module is used to perform finite element simulation using the individual three-dimensional finite element model of the target object to obtain stress-strain distribution results. The mechanical pain stimulation index calculation module is used to obtain the mechanical pain threshold of various tissues and calculate the mechanical pain stimulation index based on the mechanical pain threshold of various tissues and the stress-strain distribution results. The threshold mapping relationship acquisition module is used to acquire the established threshold mapping relationship between the mechanical pain stimulation index and the subjective pain evaluation of the target object; the threshold mapping relationship is used to map and calibrate to obtain the individual pain threshold of the target object; The individual pain stimulus index calculation module is used to calculate the individual pain stimulus index of the target object based on the individual mechanical pain threshold and the stress-strain distribution results; The pain heatmap generation module is used to visualize the spatial distribution of the individual's pain stimulus index and generate a pain heatmap of the target object; the pain heatmap is used to reflect the spatial distribution and intensity level of pain risk.

9. An electronic device, comprising: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the human pain assessment method based on finite element simulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the human pain assessment method based on finite element simulation as described in any one of claims 1 to 7.

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