Ankle foot orthosis parameterization design optimization method and system for 3D printing

Abstract

The invention discloses an ankle-foot orthosis parameterization design optimization method and system for 3D printing, and relates to the technical field of ankle-foot orthosis. Medical image data are preprocessed, abnormal foot pathological tissue segmentation is carried out, and an initial three-dimensional model of the abnormal foot pathological tissue is obtained through conversion; dividing the initial three-dimensional model into finite element mesh models, calculating material attributes of pathological tissues of the malformed feet, performing multi-scale analysis to obtain stress tensor and strain tensor of the malformed feet, and evaluating pressure sore risk indexes; according to the material attribute, the stress tensor, the strain tensor and the pressure sore risk index, an orthosis parameter prediction model is utilized, and orthosis design parameters are output; the thickness of a pressure sore prevention buffer layer of the orthosis and the indexing density and size distribution of vent holes are optimized, and a pressure sensor and a temperature and humidity sensor are arranged in the orthosis and used for adjusting the local environment in real time; and generating a three-dimensional solid model based on the design parameters of the orthosis, converting the three-dimensional solid model into a 3D printing file, and manufacturing a solid orthosis by using a 3D printer.

Description

Technical Field

[0001] The present application relates to the technical field of ankle-foot correction, and in particular to a parametric design optimization method and system for ankle-foot orthosis oriented to 3D printing. Background Art

[0002] Ankle-foot orthosis is a commonly used orthopedic device for correcting foot and ankle deformities or dysfunctions. The traditional design and production process of ankle-foot orthosis is usually based on the patient's foot and ankle conditions. The orthotic technicians hand-make a plaster model, and then process and produce the orthosis based on the plaster model. In recent years, 3D printing technology has been widely used in the medical field, providing new ideas for solving the design and production problems of traditional ankle-foot orthosis. Combining computer-aided design methods such as parametric design and topology optimization with 3D printing manufacturing processes is expected to achieve personalized, precise design and rapid manufacturing of ankle-foot orthosis.

[0003] The Chinese patent application with publication number CN110897772A discloses a method for making an ankle-foot orthosis based on 3D printing, which includes the following steps: step 1, using plantar pressure data; step 2, scanning the affected limb with a 3D scanner; step 3, obtaining an STL file from the point cloud data of the affected limb obtained in step 2; step 4, using ankle-foot biomechanics, a three-point force system and the abnormal plantar pressure obtained in step 1 to digitally design the structure of the ankle-foot orthosis in a 3D model; step 5, topology optimization; step 6, 3D printing pre-processing to obtain an ankle-foot orthosis with support; step 7, post-processing, adding pads, Velcro and other accessories, and finally letting the affected limb try it on. The requirements for operator experience and technology are reduced, and at the same time, lightweight design can be achieved, the weight of the ankle-foot orthosis can be reduced, and the patient's acceptance of the ankle-foot orthosis can be improved.

[0004] The existing methods for designing ankle-foot orthoses are mainly based on abnormal plantar pressure. The dimension of personalized design is relatively single, and the consideration of the individual needs of patients is not comprehensive enough. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one purpose of the present application is to propose a parametric design optimization method and system for ankle-foot orthosis for 3D printing, which improves the comprehensiveness of considering the individual needs of patients.

[0006] One aspect of the present application provides a parametric design optimization method for an ankle-foot orthosis for 3D printing, comprising:

[0007] Step S100: collecting medical image data of the patient's ankle and foot, preprocessing the medical image data, performing segmentation of the pathological tissue of the clubfoot based on the preprocessed medical image data, obtaining a segmentation result, and converting the segmentation result into an initial three-dimensional model of the pathological tissue of the clubfoot;

[0008] Step S200: meshing the initial three-dimensional model to generate a finite element mesh model of the pathological tissue of the clubfoot, calculating the material properties of the pathological tissue of the clubfoot in the finite element mesh model according to the gray value, performing multi-scale analysis on the finite element mesh model to obtain the stress tensor and strain tensor of the clubfoot, and evaluating the pressure ulcer risk index of the clubfoot;

[0009] Step S300: outputting design parameters of the orthosis using a pre-trained orthosis parameter prediction model according to the material properties, stress tensor, strain tensor and pressure sore risk index of the clubfoot;

[0010] Step S400: Optimizing the thickness of the anti-pressure sore buffer layer and the graduation density and size distribution of the ventilation holes of the orthosis based on the pressure sore risk index, and building a pressure sensor and a temperature and humidity sensor in the orthosis to adjust the local environment in real time;

[0011] Step S500: Generate a three-dimensional solid model based on the design parameters of the orthosis, convert it into a 3D printing file, and use a 3D printer to manufacture a physical orthosis.

[0012] The specific method of collecting medical image data of the patient's ankle and foot, preprocessing the medical image data, segmenting the pathological tissue of the clubfoot based on the preprocessed medical image data, obtaining the segmentation result, and converting the segmentation result into the initial three-dimensional model of the pathological tissue of the clubfoot is:

[0013] Step S110: using medical imaging equipment to collect medical imaging data of the patient's ankle and foot, and preprocessing the medical imaging data to obtain preprocessed medical imaging data;

[0014] Step S120: using a deep learning-based image segmentation algorithm to perform clubfoot pathological tissue segmentation on the preprocessed medical image data to obtain a binary segmentation mask image;

[0015] The specific method of using the deep learning-based image segmentation algorithm to segment the preprocessed medical image data into pathological tissues of clubfoot to obtain a binary segmentation mask image is as follows:

[0016] Step S121: obtaining medical image samples of patients with clubfoot, annotating the pathological tissue of each medical image sample, and obtaining a corresponding binary segmentation mask image;

[0017] Step S122: The deep learning-based image segmentation algorithm selects a U-Net model based on an attention mechanism, and constructs an input layer, an encoder, a decoder, an output layer, and a loss function of the U-Net model;

[0018] The input layer is used to receive preprocessed medical image data;

[0019] The encoder includes four downsampling blocks, each of which consists of two convolutional layers, an attention module, and a maximum pooling layer;

[0020] The decoder includes four upsampling blocks, each of which consists of a transposed convolutional layer, an attention model, a multi-scale feature fusion module and two convolutional layers;

[0021] The output layer outputs a binary segmentation mask image of the medical image data;

[0022] The loss function adopts weighted cross entropy loss;

[0023] Step S123: taking the medical image sample as input and the corresponding binary segmentation mask image as output, constructing a training sample for training the U-Net model based on the attention mechanism, taking the value of the loss function between the real binary segmentation mask image and the predicted binary segmentation mask image as the training target, and obtaining the trained U-Net model based on the attention mechanism when the loss function converges;

[0024] Step S124: segmenting the preprocessed medical image data using a U-Net model based on an attention mechanism to obtain a binary segmentation mask image of the medical image data;

[0025] Step S130: taking the binary segmentation mask image corresponding to each medical image data as the segmentation result of the medical image data, wherein the voxel with a value of 1 in the binary segmentation mask image represents the deformed pathological tissue area, and the voxel with a value of 0 represents the normal tissue area;

[0026] Step S140: Scanning the binary segmentation mask image based on a three-dimensional reconstruction algorithm, calculating the binary state of the vertices of each voxel cube unit of the binary segmentation mask image, and converting the binary state into an index value to generate a triangular face mesh model;

[0027] Step S150: post-processing the triangular patch mesh model to obtain an initial three-dimensional model of the clubfoot pathological tissue;

[0028] The specific method of meshing the initial three-dimensional model, generating a finite element mesh model of the clubfoot pathological tissue, calculating the material properties of the clubfoot pathological tissue in the finite element mesh model according to the gray value, performing multi-scale analysis on the finite element mesh model, and obtaining the stress tensor and strain tensor of the clubfoot is:

[0029] Step S210: Initial 3D model Meshing to generate finite element mesh model of clubfoot pathological tissue ;

[0030] Step S220: aligning the medical image data of the patient with the finite element mesh model of the clubfoot pathological tissue, extracting the gray value of the corresponding area in the medical image data for each unit of the finite element mesh model, and calculating the average gray value of the unit;

[0031] Step S230: Calculate the bone density of each unit according to the average gray value of the unit;

[0032] Step S240: calculating the material properties of the clubfoot pathological tissue of the unit according to the bone density, wherein the material properties include elastic modulus, Poisson's ratio and shear modulus;

[0033] Step S250: performing multi-scale mechanical analysis on the finite element mesh model of the clubfoot pathological tissue to obtain stress tensors and strain tensors of the clubfoot pathological tissue at macroscale, mesoscale and microscale;

[0034] The specific method of performing multi-scale mechanical analysis on the finite element mesh model of the clubfoot pathological tissue to obtain the stress tensor and strain tensor of the clubfoot pathological tissue at the macroscale, mesoscale and microscale is:

[0035] Step S251: the multi-scale mechanical analysis includes macro-scale analysis, meso-scale analysis and micro-scale analysis;

[0036] Step S252: The macro-scale analysis takes the finite element mesh model as a whole and solves the stress tensor at the macro-scale based on the static equilibrium equation. and the strain tensor ;

[0037] Step S253: Select a representative area of ​​the clubfoot pathological tissue, establish a mesomechanical model, use the stress tensor obtained from the macroscopic analysis as the boundary condition of the mesomechanical model, and solve the stress tensor at the mesoscale and the strain tensor ;

[0038] Step S254: For the stress and strain concentration area in the microscopic scale, extract its microstructural characteristics, establish a micromechanical model, use the stress tensor and strain tensor at the microscopic scale as the boundary conditions of the micromechanical model, and solve the stress tensor at the microscopic scale. and the strain tensor ;

[0039] The specific method for evaluating the pressure ulcer risk index of clubfoot is:

[0040] Step S260: Calculate the fused stress tensor based on the stress tensors and representative volumes of the clubfoot pathological tissue at the macroscale, mesoscale, and microscale ;

[0041] Step S270: Calculate the pressure of different units of the clubfoot pathological tissue according to the fused stress tensor and shear force , and obtain its pressure distribution and shear force distribution;

[0042] Step S280: Preset critical thresholds of pressure and shear force, and calculate the pressure sore risk index of each unit according to its pressure and shear force ;

[0043] The specific method of outputting the design parameters of the orthosis according to the material properties of the clubfoot, the stress tensor, the strain tensor and the pressure sore risk index by using the pre-trained orthosis parameter prediction model is as follows:

[0044] Step S310: using the stress tensor, strain tensor, and material properties of the clubfoot as mechanical characteristics of the clubfoot, using the mechanical characteristics of the clubfoot and the pressure sore risk index as input data, using the design parameters of the orthosis as output data, wherein the design parameters include geometric parameters, material parameters, and mechanical parameters, and obtaining training samples;

[0045] Step S320: using the neural network model as an initial model, training an orthosis parameter prediction model between the mechanical characteristics of the clubfoot, the pressure sore risk index and the design parameters of the orthosis, wherein the orthosis parameter prediction model includes an input layer, a hidden layer, an output layer and a loss function;

[0046] The input layer inputs the mechanical characteristics of the clubfoot and the pressure sore risk index into the input layer nodes of the neural network model, and the nonlinear representation of the mechanical characteristics of the clubfoot and the pressure sore risk index is extracted in the hidden layer through an n-layer fully connected neural network. The output layer maps the nonlinear representation to the design parameters of the orthosis. The mean square loss is used as the loss function to calculate the difference between the predicted design parameters and the actual design parameters. The minimization of the loss function is used as the training goal to optimize the model parameters. When the loss function converges to the minimum value, the training is completed, and a trained orthosis parameter prediction model is obtained.

[0047] Step S330: using the current patient's clubfoot mechanical characteristics and pressure sore risk index as input data to predict the design parameters of the patient's clubfoot orthosis;

[0048] The specific method of optimizing the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes based on the pressure sore risk index, and building a pressure sensor and a temperature and humidity sensor in the orthosis to adjust the local environment in real time is as follows:

[0049] Step S410: Optimizing the thickness t(x, y) of the anti-pressure sore buffer layer according to the pressure sore risk index of different units of the patient's ankle and foot;

[0050] Step S420: Designing ventilation holes on the outer wall of the orthosis and optimizing the distribution density of the ventilation holes and size distribution ;

[0051] Step S430: A pressure sensor and a temperature and humidity sensor are built into the orthosis to monitor the pressure distribution on the inner wall of the orthosis in real time. When the pressure is greater than the critical threshold value of the pressure When the thickness of the anti-pressure sore cushioning layer of the orthosis is optimized, the thickness of the anti-pressure sore cushioning layer of the orthosis is triggered;

[0052] Step S440: The temperature and humidity sensor monitors the temperature and humidity inside the orthosis in real time. When the temperature is greater than the critical threshold of the skin surface temperature or the humidity is greater than the critical threshold of the skin surface humidity, the optimization mechanism of the orthosis vent is triggered.

[0053] One aspect of the present application provides a parametric design optimization system for ankle-foot orthosis for 3D printing, comprising:

[0054] A three-dimensional model generation module is used to collect medical image data of the patient's ankle and foot, pre-process the medical image data, segment the pathological tissue of the deformed foot based on the pre-processed medical image data, obtain the segmentation result, and convert the segmentation result into an initial three-dimensional model of the pathological tissue of the deformed foot;

[0055] The pressure sore risk assessment module is used to mesh the initial three-dimensional model, generate a finite element mesh model of the pathological tissue of the clubfoot, calculate the material properties of the pathological tissue of the clubfoot in the finite element mesh model according to the gray value, perform multi-scale analysis on the finite element mesh model, obtain the stress tensor and strain tensor of the clubfoot, and assess the pressure sore risk index of the clubfoot;

[0056] A design parameter output module, which is used to output the design parameters of the orthosis according to the material properties of the clubfoot, the stress tensor, the strain tensor and the pressure sore risk index using a pre-trained orthosis parameter prediction model;

[0057] The local environment adjustment module is used to optimize the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes based on the pressure sore risk index. The pressure sensor and temperature and humidity sensor are built into the orthosis to adjust the local environment in real time.

[0058] The 3D printing conversion module is used to generate a three-dimensional solid model based on the design parameters of the orthosis, and convert it into a 3D printing file, and use a 3D printer to manufacture a solid orthosis.

[0059] One aspect of the present application provides a readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor to execute steps in a parametric design optimization method for an ankle-foot orthosis for 3D printing.

[0060] The parametric design optimization method and system for ankle-foot orthosis for 3D printing proposed in this application have the following advantages over the prior art:

[0061] This application uses a combination of medical imaging, deep learning, and finite element analysis to achieve accurate three-dimensional reconstruction and mechanical property analysis of the pathological tissue of the clubfoot. By collecting medical imaging data of the patient's ankle and foot, the U-Net model based on the attention mechanism is used to segment the pathological tissue of the clubfoot, and an accurate three-dimensional model of the pathological tissue is obtained. The model is further meshed by finite elements and material properties are calculated, and multi-scale mechanical analysis is carried out to comprehensively evaluate the stress-strain distribution and pressure ulcer risk of the clubfoot.

[0062] This application constructs training samples with the mechanical characteristics of clubfoot and pressure sore risk index as input and the orthosis design parameters as output, and uses a neural network model to train the orthosis parameter prediction model. It achieves end-to-end mapping from individual patient pathological mechanical information to orthosis design parameters, greatly improving the pertinence and timeliness of orthosis design and reducing the design process's reliance on professional knowledge and experience.

[0063] In view of the high incidence of pressure sores in patients with clubfoot, this application integrates functional structures such as anti-pressure sore buffer layers and vents into the orthosis design, and achieves optimal matching of structural parameters based on the pressure sore risk index of different areas of the patient's ankle and foot.

[0064] This application has built-in pressure sensors and temperature and humidity sensors in the orthosis, which realizes real-time monitoring of the local stress state and skin condition of the deformed foot, and establishes a closed-loop feedback adjustment mechanism between the orthosis and the affected foot. When the pressure exceeds the critical threshold, the stress concentration is dynamically relieved by optimizing the local stiffness of the inner wall of the orthosis; when the temperature and humidity exceed the critical threshold, the heat and humidity dissipation is accelerated by adjusting the opening of the vents. This active adaptive design concept further improves the correction effect and comfort of the orthosis and reduces the incidence of secondary pressure sores. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A method flow chart of the parametric design optimization method of ankle-foot orthosis for 3D printing provided in this application;

[0066] Figure 2 A flow chart of the method for constructing the initial three-dimensional model provided in this application;

[0067] Figure 3 A flow chart of the method for optimizing the thickness of the anti-pressure sore buffer layer and the vent holes in the orthosis provided in the present application;

[0068] Figure 4 It is a left side view of the three-dimensional solid model of the orthosis;

[0069] Figure 5 It is a right side view of the three-dimensional solid model of the orthosis;

[0070] Figure 6 A schematic diagram of the structure of a three-dimensional solid model of the orthosis provided in this application;

[0071] Figure 7 Functional module diagram of the parametric design optimization system for ankle-foot orthosis for 3D printing provided in this application.

[0072] Figure numerals: 1. leg fixing part; 2. ankle joint fixing part; 3. fixing ring at the upper end of the calf; 4. fixing device on the fixing ring at the upper end of the calf; 5. fixing device on the fixing ring at the lower end of the calf; 6. adjusting device on the fixing ring at the lower end of the calf; 7. inner wall of the orthosis. DETAILED DESCRIPTION

[0073] In order to better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present application and do not limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0074] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in this application, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0075] It should also be understood that expressions such as "including", "comprising", "having", "containing" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application". And, the term "exemplary" is intended to refer to an example or illustration.

[0076] Unless otherwise defined, all words (including engineering terms and scientific and technological terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that unless there is a clear explanation in this application, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0077] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0078] Example 1

[0079] like Figure 1 As shown, the parametric design optimization method of the ankle-foot orthosis for 3D printing provided in this application includes:

[0080] Step S100: Collect medical image data of the patient's ankle and foot, pre-process the medical image data, segment the pathological tissue of the clubfoot based on the pre-processed medical image data, obtain the segmentation result, and convert the segmentation result into an initial three-dimensional model of the pathological tissue of the clubfoot.

[0081] The specific method of collecting medical image data of the patient's ankle and foot, preprocessing the medical image data, segmenting the pathological tissue of the clubfoot based on the preprocessed medical image data, obtaining the segmentation result, and converting the segmentation result into the initial three-dimensional model of the pathological tissue of the clubfoot is:

[0082] Step S110: using medical imaging equipment to collect medical imaging data of the patient's ankle and foot, and preprocessing the medical imaging data to obtain preprocessed medical imaging data;

[0083] Step S120: using a deep learning-based image segmentation algorithm to perform clubfoot pathological tissue segmentation on the preprocessed medical image data to obtain a binary segmentation mask image;

[0084] The specific method of using the deep learning-based image segmentation algorithm to segment the preprocessed medical image data into pathological tissues of clubfoot to obtain a binary segmentation mask image is as follows:

[0085] Step S121: obtaining medical image samples of patients with clubfoot, annotating the pathological tissue of each medical image sample, and obtaining a corresponding binary segmentation mask image;

[0086] The binary segmentation mask image refers to a C-channel image of the same size as the input medical image data, which represents the probability that each voxel belongs to C pathological tissue categories; preferably, the number of channels C of the binary segmentation mask image is equal to 1, and the binary segmentation mask image is a single-channel image of the same size as the input medical image data, and the value of each voxel is 0 or 1. When the value of the voxel is 0, it indicates that the voxel does not belong to pathological tissue, and when the value of the voxel is 1, it indicates that it belongs to pathological tissue.

[0087] Step S122: The deep learning-based image segmentation algorithm selects a U-Net model based on an attention mechanism, and constructs an input layer, an encoder, a decoder, an output layer, and a loss function of the U-Net model;

[0088] The input layer is used to receive preprocessed medical image data;

[0089] The encoder includes four downsampling blocks, each of which consists of two convolutional layers, an attention module, and a maximum pooling layer;

[0090] The decoder includes four upsampling blocks, each of which consists of a transposed convolutional layer, an attention model, a multi-scale feature fusion module and two convolutional layers;

[0091] The output layer outputs a binary segmentation mask image of the medical image data;

[0092] The loss function adopts weighted cross entropy loss;

[0093] The calculation formula of the weighted cross entropy loss is: ,in, is the weighted cross entropy loss of the lth layer, is the weight coefficient of the lth layer, Nd is the number of layers of the decoder, and Nd is equal to 4;

[0094] The calculation formula of the weighted cross entropy loss of the lth layer is: , where Nk is the number of medical image samples, Nc is the number of voxel categories in the binary segmentation mask image, is the category weight, is the real binary segmentation mask image of the kth medical image sample, is the binary segmentation mask image predicted by the decoder layer l, log(.) is the logarithmic function;

[0095] The U-Net model based on the attention mechanism is based on preprocessed medical image data. As input, the size is ,in , , are the height, width and depth of the medical image data respectively; the convolution layer of the encoder performs a convolution operation on the input medical image data to obtain a convolution feature map, an attention module is added after each convolution layer, the attention weight of each channel is calculated, the attention weight is applied to the convolution feature map, and the convolution feature map is pooled using the maximum pooling layer to obtain a feature map;

[0096] Assume the convolution operation of the lth layer is , the attention module is , the maximum pooling operation is , then the feature map output by the encoder is It can be expressed as: ;in, ;

[0097] The transposed convolution layer of the decoder upsamples the feature map, and the feature maps of different levels of the encoder are fused with the feature maps in the decoder through the multi-scale feature fusion module. After upsampling, the convolution operation is performed through the convolution layer of the decoder for further feature extraction, and the decoder outputs the final feature map;

[0098] Suppose the transposed convolution operation of the mth layer is , the multi-scale feature fusion module is , then the feature map output by the decoder is It can be expressed as: = ; Among them, when m is 4, , m is equal to 4, 3, 2, 1;

[0099] The multi-scale feature fusion module combines the feature map of the encoder layer l and the feature map of the decoder layer m Fusion is performed to obtain the fused feature map ,in, Indicates a connection operation. is the fusion weight;

[0100] The fusion weight is calculated by 1×1×1 convolution and softmax function: ;

[0101] The output layer uses a 1×1×1 convolutional layer to map the feature map output by the decoder into a binary segmentation mask image;

[0102] Step S123: taking the medical image sample as input and the corresponding binary segmentation mask image as output, constructing a training sample for training the U-Net model based on the attention mechanism, taking the value of the loss function between the real binary segmentation mask image and the predicted binary segmentation mask image as the training target, and obtaining the trained U-Net model based on the attention mechanism when the loss function converges;

[0103] Step S124: Use the U-Net model based on the attention mechanism to segment the preprocessed medical image data to obtain a binary segmentation mask image of the medical image data.

[0104] Step S130: taking the binary segmentation mask image corresponding to each medical image data as the segmentation result of the medical image data, wherein the voxel with a value of 1 in the binary segmentation mask image represents the deformed pathological tissue area, and the voxel with a value of 0 represents the normal tissue area;

[0105] Step S140: Scan the binary segmentation mask image based on a three-dimensional reconstruction algorithm, calculate the binary state of the vertices of each voxel cube unit of the binary segmentation mask image, and convert it into an index value to generate a triangular patch mesh model.

[0106] The specific method of scanning the binary segmentation mask image based on the three-dimensional reconstruction algorithm, calculating the binary state of the vertex of each voxel of the cube unit of the binary segmentation mask image, and converting it into an index value to generate a triangular patch mesh model is as follows:

[0107] Step S141: using the Marching Cubes algorithm as a 3D reconstruction algorithm to scan the binary segmentation mask image, traverse each voxel of the binary segmentation mask image, and for each voxel, extract a cube unit consisting of eight adjacent voxels;

[0108] Step S142: Calculate the binary states of the eight vertices of the cube unit, and calculate the binary states of the eight vertices of the cube unit according to the isosurface threshold. , convert it into an index value;

[0109] The binary states of the eight vertices of the computing cube unit are calculated according to the isosurface threshold The specific method of converting it into an index value is as follows: assign index numbers to the 8 vertices of the cube unit; , compare the gray value of each vertex with the threshold to obtain the binary state of the vertex. If the gray value of the vertex is greater than or equal to the isosurface threshold, its binary state is 1, otherwise its binary state is 0; arrange the binary states of the eight vertices in the order of index numbers to form an eight-bit binary number, convert the binary number into a decimal number, and obtain the index value of the cube unit;

[0110] Step S143: searching the triangle patch lookup table of the Marching Cubes algorithm according to the index value to obtain the topological structure and vertex positions of the triangle patches inside the cube unit;

[0111] Step S144: For each vertex of the triangle, calculate its spatial coordinates by linear interpolation , save the spatial coordinates and topological structure of the generated triangular face vertices into the triangular face mesh model;

[0112] The calculation formula of the spatial coordinates is: , , ,in, , , is the coordinate of the current voxel in the binary segmentation mask image, , , Represents the actual length of the voxel in the x, y, and z directions, , , Indicates the relative position of the vertices of the triangle in the cube unit;

[0113] Step S145: repeat steps S141 to S144 to traverse all voxels until the entire binary segmentation mask image is processed.

[0114] Step S150: post-processing the triangular patch mesh model to obtain an initial three-dimensional model of the clubfoot pathological tissue;

[0115] Figure 2 A flow chart of the method for constructing the initial three-dimensional model provided in this application;

[0116] The post-processing includes smoothing, simplification and patching, which are respectively used to eliminate aliasing and spikes to make the surface smoother, reduce the number of triangular facets to improve calculation efficiency and fill possible holes to ensure the integrity of the model.

[0117] Step S200: meshing the initial three-dimensional model to generate a finite element mesh model of the pathological tissue of the clubfoot, calculating the material properties of the pathological tissue of the clubfoot in the finite element mesh model according to the gray value, performing multi-scale analysis on the finite element mesh model to obtain the stress tensor and strain tensor of the clubfoot, and evaluating the pressure ulcer risk index of the clubfoot;

[0118] The specific method of meshing the initial three-dimensional model, generating a finite element mesh model of the clubfoot pathological tissue, calculating the material properties of the clubfoot pathological tissue in the finite element mesh model according to the gray value, performing multi-scale analysis on the finite element mesh model, and obtaining the stress tensor and strain tensor of the clubfoot is:

[0119] Step S210: Initial 3D model Meshing to generate finite element mesh model of clubfoot pathological tissue ;

[0120] The conditions that need to be met for the grid division are: the value range of the unit size is ,in, , are the minimum and maximum values ​​of the unit size, respectively;

[0121] The minimum value of the unit size is generally 1 to 2 times the voxel size, and the maximum value is generally the minimum characteristic size of the pathological tissue. ;

[0122] The finite element mesh model of the clubfoot pathological tissue It is represented by a set of nodes, a set of units, a connection relationship matrix and a set of boundary conditions, where nodes are discrete points in the finite element mesh model, representing the position of the pathological tissue of the clubfoot in space, and the degree of freedom is defined as a displacement vector at each node; units represent the basic building blocks in the finite element mesh model, which are generated by nodes according to the topological structure, and their material properties are defined on each unit; connection relationships represent the topological relationship between nodes and units; and boundary conditions represent the interaction between the pathological tissue of the clubfoot and the external environment.

[0123] Step S220: aligning the medical image data of the patient with the finite element mesh model of the clubfoot pathological tissue, extracting the gray value of the corresponding area in the medical image data for each unit of the finite element mesh model, and calculating the average gray value of the unit;

[0124] The calculation formula for the average gray value of the unit is: ,in, is the number of voxels in the area corresponding to the ath unit, is the gray value of the nath voxel in the area corresponding to the ath unit;

[0125] Step S230: Calculate the bone density of each unit according to the average gray value of the unit;

[0126] The calculation formula of the bone density is: ,in, , is the linear conversion coefficient between gray value and bone density;

[0127] The linear conversion coefficient is set by those skilled in the art based on experience.

[0128] Step S240: Calculate the material properties of the clubfoot pathological tissue of the unit according to the bone density, wherein the material properties include elastic modulus , Poisson's ratio and shear modulus ;

[0129] The calculation formula of the elastic modulus is: ,in, is the maximum elastic modulus of bone tissue, k1 is the first material constant, It is the minimum bone density of bone tissue;

[0130] The maximum elastic modulus of the bone tissue ranges from 15 to 20 GPa, and the specific value is set by those skilled in the art based on experience.

[0131] The first material constant has a value range of 2~4cm 3 / g.

[0132] The minimum bone density of the bone tissue is 0.1 g / cm 3 .

[0133] The calculation formula of the Poisson's ratio is: ;in, , are the maximum and minimum Poisson's ratios of bone tissue, is the second material parameter;

[0134] The maximum and minimum Poisson's ratios of the bone tissue are in the range of 0.2-0.4 and 0.1-0.2 respectively;

[0135] The value range of the second material parameter is 1~2cm 3 / g.

[0136] The calculation formula of the shear modulus is: ;

[0137] In the pathological tissue of clubfoot, the elastic modulus indicates the hardness of bone or soft tissue, the Poisson's ratio can reflect the degree of deformation of the pathological tissue, and the shear modulus reflects the strength and deformation characteristics of cartilage, muscle or lesion area.

[0138] Step S250: performing multi-scale mechanical analysis on the finite element mesh model of the clubfoot pathological tissue to obtain stress tensors and strain tensors of the clubfoot pathological tissue at macroscale, mesoscale and microscale;

[0139] The specific method of performing multi-scale mechanical analysis on the finite element mesh model of the clubfoot pathological tissue to obtain the stress tensor and strain tensor of the clubfoot pathological tissue at the macroscale, mesoscale and microscale is:

[0140] Step S251: the multi-scale mechanical analysis includes macro-scale analysis, meso-scale analysis and micro-scale analysis;

[0141] Step S252: The macro-scale analysis takes the finite element mesh model as a whole and solves the stress tensor at the macro-scale based on the static equilibrium equation. and the strain tensor ;

[0142] , Represent the stress tensor and strain tensor in the i-th coordinate direction and the j-th coordinate direction respectively;

[0143] The equation of the static equilibrium equation is: ,in, is the component of the external force acting on the pathological tissue of clubfoot in the i-th coordinate direction, Represents the stress tensor The rate of change in the jth coordinate direction;

[0144] The calculation formula of the strain tensor is: ,in, Represents the displacement vector The rate of change in the jth coordinate direction, Represents the displacement vector The rate of change in the i-th coordinate direction;

[0145] Step S253: Select a representative area of ​​the clubfoot pathological tissue, establish a mesomechanical model, use the stress tensor obtained from the macroscopic analysis as the boundary condition of the mesomechanical model, and solve the stress tensor at the mesoscale and the strain tensor ;

[0146] The formula of the micromechanical model is: ,in, represents the elastic tensor at the microscopic scale, represents the shear strain tensor between the kth coordinate direction and the lth coordinate direction at the mesoscopic scale;

[0147] The calculation formula of the elastic tensor is: ,in, represents the elastic tensor of the matrix material, Represents the correction items of each coordinate direction of bone density, The coordinate direction correction items representing the orientation of trabeculae, is the angular coordinate of the direction of trabeculae;

[0148] Preferably, the representative region is selected from the calcaneus and talus regions;

[0149] Step S254: For the stress and strain concentration area in the microscopic scale, extract its microstructural characteristics, establish a micromechanical model, use the stress tensor and strain tensor at the microscopic scale as the boundary conditions of the micromechanical model, and solve the stress tensor at the microscopic scale. and the strain tensor ;

[0150] The formula of the micromechanical model is: ,in, is the elastic tensor at the microscopic scale, is the shear strain tensor between the kth coordinate direction and the lth coordinate direction at the microscopic scale;

[0151] The calculation formula of the elastic tensor at the microscopic scale is: ,in, Represents bone density at a microscopic scale and microstructural parameters Correction terms in each coordinate direction;

[0152] The specific method for evaluating the pressure ulcer risk index of clubfoot is:

[0153] Step S260: Calculate the fused stress tensor based on the stress tensors and representative volumes of the clubfoot pathological tissue at the macroscale, mesoscale, and microscale ;

[0154] The calculation formula of the fused stress tensor is: , where V is the total volume of the pathological tissue of the clubfoot, , , They are the representative volumes of the pathological tissue of clubfoot at the macroscopic, mesoscopic and microscopic scales, respectively. The total volume of the pathological tissue of clubfoot meets the representative volume. ;

[0155] The representative volume of the clubfoot pathological tissue at the macroscale, mesoscale and microscale refers to the smallest volume unit used to analyze and calculate the mechanical properties of the tissue at each scale. The initial three-dimensional model of the clubfoot pathological tissue is analyzed at different scales to extract the corresponding representative volume; at the macroscale, a uniform volume unit is selected from the entire three-dimensional model by a volume sampling method to simulate the overall mechanical properties; at the mesoscale, a part of the local area features, such as trabeculae, is extracted; at the microscale, smaller units are scanned at high resolution to select representative areas.

[0156] Step S270: Calculate the pressure and shear force of different units of the clubfoot pathological tissue according to the fused stress tensor to obtain its pressure distribution and shear force distribution;

[0157] The calculation formula for the pressure of different units of the clubfoot pathological tissue is: ,in, , , are the fused stress tensors in the first, second and third coordinate directions respectively;

[0158] Said , , is the fused stress tensor in three coordinate directions, that is, the fused stress tensor in the orthogonal coordinate system;

[0159] The calculation formula for the shear force in different areas of the clubfoot pathological tissue is: ;

[0160] Step S280: preset critical thresholds of pressure and shear force, and calculate the pressure sore risk index of each unit according to the pressure and shear force of each unit;

[0161] The calculation formula of the pressure sore risk index is: ,in, , , are the weight coefficients of pressure, shear force, and interaction term of pressure and shear force, , are the critical thresholds of pressure and shear force, , are the sensitivity coefficients of pressure and shear force respectively;

[0162] The weight coefficients of the pressure, shear force, the interaction terms of pressure and shear force, the critical thresholds of pressure and shear force, and the sensitivity coefficients of pressure and shear force are set by those skilled in the art based on experience.

[0163] Step S300: outputting design parameters of the orthosis using a pre-trained orthosis parameter prediction model according to the material properties, stress tensor, strain tensor and pressure sore risk index of the clubfoot;

[0164] The specific method of outputting the design parameters of the orthosis according to the material properties of the clubfoot, the stress tensor, the strain tensor and the pressure sore risk index by using the pre-trained orthosis parameter prediction model is as follows:

[0165] Step S310: using the stress tensor, strain tensor, and material properties of the clubfoot as mechanical characteristics of the clubfoot, using the mechanical characteristics of the clubfoot and the pressure sore risk index as input data, using the design parameters of the orthosis as output data, wherein the design parameters include geometric parameters, material parameters, and mechanical parameters, and obtaining training samples;

[0166] Step S320: using the neural network model as an initial model, training an orthosis parameter prediction model between the mechanical characteristics of the clubfoot, the pressure sore risk index and the design parameters of the orthosis, wherein the orthosis parameter prediction model includes an input layer, a hidden layer, an output layer and a loss function;

[0167] The input layer inputs the mechanical characteristics of the clubfoot and the pressure sore risk index into the input layer nodes of the neural network model, and the nonlinear representation of the mechanical characteristics of the clubfoot and the pressure sore risk index is extracted in the hidden layer through an n-layer fully connected neural network. The output layer maps the nonlinear representation to the design parameters of the orthosis. The mean square loss is used as the loss function to calculate the difference between the predicted design parameters and the actual design parameters. The minimization of the loss function is used as the training goal to optimize the model parameters. When the loss function converges to the minimum value, the training is completed, and a trained orthosis parameter prediction model is obtained.

[0168] The calculation formula of the nonlinear characterization is: ,in, is the output of the qth hidden layer node, f(.) is the activation function, is the weight from the rth node in the input layer to the qth node in the hidden layer, is the bias term of the qth node in the hidden layer, For input data;

[0169] The calculation formula for mapping the nonlinear representation to the design parameters of the orthosis is: ,in, is the output of the sth node of the output layer, i.e., the predicted value of the orthosis design parameter, g(.) is the activation function of the output layer, is the weight from the qth node in the hidden layer to the sth node in the output layer, is the bias term of the sth node in the output layer;

[0170] The calculation formula of the loss function is: ,in, is the predicted value of the orthosis design parameters in the nyth training sample, is the true value of the orthosis design parameter in the nyth training sample, Ny is the number of training samples, ;

[0171] Step S330: using the current patient's clubfoot mechanical characteristics and pressure sore risk index as input data to predict the design parameters of the patient's clubfoot orthosis;

[0172] Step S400: Optimizing the thickness of the anti-pressure sore buffer layer and the graduation density and size distribution of the ventilation holes of the orthosis based on the pressure sore risk index, and building a pressure sensor and a temperature and humidity sensor in the orthosis to adjust the local environment in real time;

[0173] The specific method of optimizing the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes based on the pressure sore risk index, and building a pressure sensor and a temperature and humidity sensor in the orthosis to adjust the local environment in real time is as follows:

[0174] Step S410: Optimizing the thickness t(x, y) of the anti-pressure sore buffer layer according to the pressure sore risk index of different units of the patient's ankle and foot;

[0175] The calculation formula for the thickness of the anti-pressure sore buffer layer is: ,in, is the base thickness, is the proportionality coefficient, is the pressure sore risk index at the coordinate (x, y) on the pathological tissue of the clubfoot at the microscopic scale;

[0176] Preferably, the base thickness is 3 mm and the proportional coefficient is 0.5.

[0177] Step S420: Designing ventilation holes on the outer wall of the orthosis and optimizing the distribution density of the ventilation holes and size distribution ;

[0178] The calculation formula of the distribution density is: ,in, is the basic density, is the adjustment coefficient of the pressure ulcer risk index, Skin surface temperature The adjustment coefficient, is the critical threshold of skin surface temperature;

[0179] The calculation formula for the size distribution is: ,in, As the basic size, is the adjustment coefficient of the pressure ulcer risk index, Skin surface humidity The adjustment coefficient, is the critical threshold of skin surface moisture;

[0180] The adjustment coefficient of the basic density, basic size and pressure sore risk index and , the adjustment coefficients of skin surface temperature and skin surface humidity, and the critical thresholds of skin surface temperature and skin surface humidity are set by technicians in this field based on experience.

[0181] Preferably, the basic density is 0.8 pieces / cm² and the basic size is The value is 1mm, and the adjustment coefficient of the pressure sore risk index The value is 0.6. The value is 0.4, the adjustment coefficient of skin surface temperature The value is 0.02, the adjustment coefficient of skin surface humidity The value is 0.03; the critical threshold value of skin surface temperature is 32°C, and the critical threshold value of skin surface humidity is 60%;

[0182] Step S430: A pressure sensor and a temperature and humidity sensor are built into the orthosis to monitor the pressure distribution on the inner wall of the orthosis in real time. When the pressure is greater than the critical threshold value of the pressure When the thickness of the anti-pressure sore cushioning layer of the orthosis is optimized, the thickness of the anti-pressure sore cushioning layer of the orthosis is triggered;

[0183] The mechanism for triggering the optimization of the thickness of the anti-pressure sore buffer layer of the orthosis refers to jumping to step S410 to recalculate the optimal thickness of the anti-pressure sore buffer layer.

[0184] Step S440: The temperature and humidity sensor monitors the temperature and humidity inside the orthosis in real time. When the temperature is greater than the critical threshold of the skin surface temperature or the humidity is greater than the critical threshold of the skin surface humidity, the optimization mechanism of the orthosis vent is triggered;

[0185] Figure 3 A flow chart of the method for optimizing the thickness of the anti-pressure sore buffer layer and the vent holes in the orthosis provided in the present application;

[0186] The triggering mechanism for optimizing the ventilation holes of the orthosis refers to jumping to step S420 to recalculate the optimal distribution density and size distribution of the ventilation holes.

[0187] Step S500: Generate a three-dimensional solid model based on the design parameters of the orthosis, convert it into a 3D printing file, and use a 3D printer to manufacture a physical orthosis;

[0188] The printing material of the solid orthosis supports real-time adjustment function, such as selecting electroactive polymers or shape memory polymers. These materials can achieve dynamic adjustment of shape or mechanical properties when receiving specific external stimuli such as temperature, humidity, and pressure.

[0189] According to the orthosis parameter prediction model pre-trained in this application, the mechanical characteristics of the current patient's clubfoot and the pressure sore risk index are used as input data to predict the design parameters of the orthosis for the patient's clubfoot. The design parameters include geometric parameters, material parameters and mechanical parameters, which determine the overall shape, size and material properties of the orthosis. Based on the design parameters, a three-dimensional solid model of the orthosis is constructed in the three-dimensional modeling software.

[0190] Figure 4 This is the left side view of the three-dimensional solid model of the orthosis. Figure 5 The right side view of the three-dimensional solid model of the orthosis. The three-dimensional solid model of the orthosis consists of a leg fixing part 1 and an ankle joint fixing part 2. Among them, the upper calf fixing ring 3 can fix the orthosis on the patient's calf according to the fixing device 4 on the upper calf fixing ring and the fixing device 5 on the lower calf fixing ring.

[0191] In order to embed pressure sensors and temperature and humidity sensors in the orthosis, it is necessary to reserve installation space for the sensors at the predetermined position of the three-dimensional solid model of the orthosis, design the circuit system for sensor signal acquisition, processing and wireless transmission in the three-dimensional solid model, and integrate the digital design of the sensors and feedback system with the main model of the orthosis to form a complete digital model of the intelligent orthosis.

[0192] exist Figure 5 In the embodiment, a temperature and humidity sensor is arranged at the adjusting device 6 on the fixing ring at the lower end of the calf. When the temperature is greater than the critical threshold of the skin surface temperature or the humidity is greater than the critical threshold of the skin surface humidity, the adjusting mechanism of the adjusting device 6 on the fixing ring at the lower end of the calf is triggered to achieve the ventilation effect of the orthosis.

[0193] Figure 6 A schematic diagram of the structure of the three-dimensional solid model of the orthosis provided in this application. Figure 6 In the embodiment, an anti-pressure sore buffer layer and a pressure sensor are integrated in the inner wall 7 of the orthosis, and an optimization mechanism for the thickness of the anti-pressure sore buffer layer of the orthosis is triggered according to the pressure distribution monitored by the pressure sensor.

[0194] Convert the complete digital model of the smart orthosis into an STL file and import it into the slicing software for slicing. Select appropriate slicing parameters in the slicing software to generate digital instructions for 3D printing. Select appropriate 3D printing materials, input the generated digital instructions into the 3D printer, and start printing the orthosis layer by layer. Integrate devices such as pressure sensors, temperature and humidity sensors into the orthosis, and encapsulate and fix them.

[0195] Example 2

[0196] like Figure 7 As shown, the parametric design optimization system for ankle-foot orthosis for 3D printing provided by this application includes:

[0197] A three-dimensional model generation module is used to collect medical image data of the patient's ankle and foot, pre-process the medical image data, segment the pathological tissue of the deformed foot based on the pre-processed medical image data, obtain the segmentation result, and convert the segmentation result into an initial three-dimensional model of the pathological tissue of the deformed foot;

[0198] The pressure sore risk assessment module is used to mesh the initial three-dimensional model, generate a finite element mesh model of the pathological tissue of the clubfoot, calculate the material properties of the pathological tissue of the clubfoot in the finite element mesh model according to the gray value, perform multi-scale analysis on the finite element mesh model, obtain the stress tensor and strain tensor of the clubfoot, and assess the pressure sore risk index of the clubfoot;

[0199] A design parameter output module, which is used to output the design parameters of the orthosis according to the material properties of the clubfoot, the stress tensor, the strain tensor and the pressure sore risk index using a pre-trained orthosis parameter prediction model;

[0200] The local environment adjustment module is used to optimize the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes based on the pressure sore risk index. The pressure sensor and temperature and humidity sensor are built into the orthosis to adjust the local environment in real time.

[0201] The 3D printing conversion module is used to generate a three-dimensional solid model based on the design parameters of the orthosis, and convert it into a 3D printing file, and use a 3D printer to manufacture a solid orthosis.

[0202] Example 3

[0203] According to one embodiment of the present application, a readable storage medium is also provided. Computer-readable instructions are stored on the readable storage medium. When the computer-readable instructions are executed by the processor, the parametric design optimization method for ankle-foot orthosis for 3D printing according to the embodiment of the present application can be executed. The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0204] In addition, according to the implementation mode of the present application, the process described above can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium stores machine-readable instructions, the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, for example: collecting medical image data of the patient's ankle and foot, preprocessing the medical image data, segmenting the pathological tissue of the deformed foot based on the preprocessed medical image data, obtaining the segmentation result, converting the segmentation result into an initial three-dimensional model of the pathological tissue of the deformed foot; meshing the initial three-dimensional model to generate a finite element mesh model of the pathological tissue of the deformed foot, and calculating the gray value of the deformed foot pathology in the finite element mesh model. The material properties of the physical tissue are analyzed at multiple scales on the finite element mesh model to obtain the stress tensor and strain tensor of the deformed foot, and the pressure sore risk index of the deformed foot is evaluated; the design parameters of the orthosis are outputted using the pre-trained orthosis parameter prediction model according to the material properties, stress tensor, strain tensor and pressure sore risk index of the deformed foot; the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the vents are optimized based on the pressure sore risk index, and a pressure sensor and a temperature and humidity sensor are built into the orthosis for real-time adjustment of the local environment; a three-dimensional solid model is generated based on the design parameters of the orthosis, and converted into a 3D printing file, and a physical orthosis is manufactured using a 3D printer. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0205] The methods, systems, and storage media of the present application can be implemented in a variety of ways. For example, the methods, systems, and storage media of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order for the method steps is for illustration only, and the method steps of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application can also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing methods according to the present application.

[0206] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0207] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A parametric design optimization method for ankle-foot orthosis for 3D printing, characterized in that: include: Collect medical image data of the patient's ankle and foot, pre-process the medical image data, segment the pathological tissue of the clubfoot based on the pre-processed medical image data, obtain the segmentation result, and convert the segmentation result into an initial three-dimensional model of the pathological tissue of the clubfoot; The initial three-dimensional model was meshed to generate a finite element mesh model of the pathological tissue of the clubfoot. The material properties of the pathological tissue of the clubfoot in the finite element mesh model were calculated according to the grayscale value. The finite element mesh model was subjected to multi-scale analysis to obtain the stress tensor and strain tensor of the clubfoot, and the pressure ulcer risk index of the clubfoot was evaluated. According to the material properties, stress tensor, strain tensor and pressure sore risk index of the clubfoot, the pre-trained orthosis parameter prediction model is used to output the design parameters of the orthosis; Based on the pressure sore risk index, the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes are optimized. Pressure sensors and temperature and humidity sensors are built into the orthosis to adjust the local environment in real time. A three-dimensional solid model is generated based on the design parameters of the orthosis, converted into a 3D printing file, and the physical orthosis is manufactured using a 3D printer.

2. The parametric design optimization method for ankle-foot orthosis for 3D printing according to claim 1, characterized in that: The specific method of collecting medical image data of the patient's ankle and foot, preprocessing the medical image data, segmenting the pathological tissue of the clubfoot based on the preprocessed medical image data, obtaining the segmentation result, and converting the segmentation result into the initial three-dimensional model of the pathological tissue of the clubfoot is: Using medical imaging equipment to collect medical imaging data of the patient's ankle and foot, preprocessing the medical imaging data to obtain preprocessed medical imaging data; The image segmentation algorithm based on deep learning is used to segment the pathological tissue of the clubfoot in the preprocessed medical image data to obtain a binary segmentation mask image. The binary segmentation mask image corresponding to each medical image data is used as the segmentation result of the medical image data, wherein the voxel with a value of 1 in the binary segmentation mask image represents the deformed pathological tissue area, and the voxel with a value of 0 represents the normal tissue area; Scan the binary segmentation mask image based on a 3D reconstruction algorithm, calculate the binary state of the vertex of each voxel of the cube unit of the binary segmentation mask image, and convert it into an index value to generate a triangular patch mesh model; The triangular patch mesh model was post-processed to obtain the initial three-dimensional model of the pathological tissue of the clubfoot.

3. The parametric design optimization method for ankle-foot orthosis for 3D printing according to claim 2, characterized in that: The specific method of using the deep learning-based image segmentation algorithm to segment the preprocessed medical image data into pathological tissues of clubfoot to obtain a binary segmentation mask image is as follows: Obtain medical image samples of patients with clubfoot, annotate the pathological tissue of each medical image sample, and obtain a corresponding binary segmentation mask image; The image segmentation algorithm based on deep learning selects a U-Net model based on an attention mechanism, and constructs an input layer, an encoder, a decoder, an output layer, and a loss function of the U-Net model; The input layer is used to receive preprocessed medical image data; The encoder includes four downsampling blocks, each of which consists of two convolutional layers, an attention module, and a maximum pooling layer; The decoder includes four upsampling blocks, each of which consists of a transposed convolutional layer, an attention model, a multi-scale feature fusion module and two convolutional layers; The output layer outputs a binary segmentation mask image of the medical image data; The loss function adopts weighted cross entropy loss; Taking the medical image samples as input and the corresponding binary segmentation mask images as output, construct training samples for training the U-Net model based on the attention mechanism, and taking the value of the loss function between the real binary segmentation mask image and the predicted binary segmentation mask image as the training target. When the loss function converges, the trained U-Net model based on the attention mechanism is obtained. The preprocessed medical image data is segmented using the U-Net model based on the attention mechanism to obtain a binary segmentation mask image of the medical image data.

4. The parametric design optimization method for ankle-foot orthosis for 3D printing according to claim 3, characterized in that: The specific method of meshing the initial three-dimensional model, generating a finite element mesh model of the clubfoot pathological tissue, calculating the material properties of the clubfoot pathological tissue in the finite element mesh model according to the gray value, performing multi-scale analysis on the finite element mesh model, and obtaining the stress tensor and strain tensor of the clubfoot is: Initial 3D model Meshing to generate finite element mesh model of clubfoot pathological tissue ; The medical imaging data of the patient is registered with the finite element mesh model of the pathological tissue of the clubfoot. For each unit of the finite element mesh model, the gray value of the corresponding area in the medical imaging data is extracted, and the average gray value of the unit is calculated; The bone density of each unit is calculated based on the average gray value of the unit; Calculating material properties of the clubfoot pathological tissue of the unit according to the bone density, wherein the material properties include elastic modulus, Poisson's ratio and shear modulus; Multi-scale mechanical analysis was performed on the finite element mesh model of clubfoot pathological tissue to obtain the stress tensor and strain tensor of clubfoot pathological tissue at the macroscale, mesoscale and microscale.

5. The parametric design optimization method for ankle-foot orthosis for 3D printing according to claim 4, characterized in that: The specific method for evaluating the pressure ulcer risk index of clubfoot is: The stress tensor after fusion is calculated based on the stress tensor and representative volume of the clubfoot pathological tissue at the macro, meso and micro scales. ; Calculate the stress of different units of the clubfoot pathological tissue based on the stress tensor after fusion and shear force , and obtain its pressure distribution and shear force distribution; Preset critical thresholds for pressure and shear force, and calculate the pressure ulcer risk index based on the pressure and shear force of each unit .

6. The parametric design optimization method for ankle-foot orthosis for 3D printing according to claim 5, characterized in that: The specific method of outputting the design parameters of the orthosis according to the material properties of the clubfoot, the stress tensor, the strain tensor and the pressure sore risk index by using the pre-trained orthosis parameter prediction model is as follows: Using the stress tensor, strain tensor, and material properties of the clubfoot as mechanical characteristics of the clubfoot, using the mechanical characteristics of the clubfoot and a pressure sore risk index as input data, and using the design parameters of the orthosis as output data, wherein the design parameters include geometric parameters, material parameters, and mechanical parameters, to obtain training samples; Using the neural network model as the initial model, training an orthosis parameter prediction model between the mechanical characteristics of the clubfoot, the pressure sore risk index and the design parameters of the orthosis, the orthosis parameter prediction model comprising an input layer, a hidden layer, an output layer and a loss function; The input layer inputs the mechanical characteristics of the clubfoot and the pressure sore risk index into the input layer nodes of the neural network model, and the nonlinear representation of the mechanical characteristics of the clubfoot and the pressure sore risk index is extracted through an n-layer fully connected neural network in the hidden layer. The output layer maps the nonlinear representation to the design parameters of the orthosis. The mean square loss is used as the loss function to calculate the difference between the predicted design parameters and the actual design parameters. The minimization of the loss function is used as the training goal to optimize the model parameters. When the loss function converges to the minimum value, the training is completed and the trained orthosis parameter prediction model is obtained. The mechanical characteristics of the patient's clubfoot and the pressure sore risk index are used as input data to predict the design parameters of the orthosis for the patient's clubfoot.

7. The parametric design optimization method for ankle-foot orthosis for 3D printing according to claim 6, characterized in that: The specific method of optimizing the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes based on the pressure sore risk index, and building a pressure sensor and a temperature and humidity sensor in the orthosis to adjust the local environment in real time is as follows: According to the pressure sore risk index of different units of the patient's ankle and foot, optimize the thickness of the anti-pressure sore buffer layer t(x,y); Design ventilation holes on the outer wall of the orthosis and optimize the distribution density of ventilation holes and size distribution ; The pressure sensor and temperature and humidity sensor are built into the orthosis to monitor the pressure distribution on the inner wall of the orthosis in real time. When the thickness of the anti-pressure sore cushioning layer of the orthosis is optimized, the thickness of the anti-pressure sore cushioning layer of the orthosis is triggered; The temperature and humidity sensor monitors the temperature and humidity inside the orthosis in real time. When the temperature is greater than the critical threshold of the skin surface temperature or the humidity is greater than the critical threshold of the skin surface humidity, the optimization mechanism of the orthosis vent is triggered.

8. A parametric design optimization system for ankle-foot orthosis oriented to 3D printing, which is used to implement the parametric design optimization method for ankle-foot orthosis oriented to 3D printing according to any one of claims 1 to 7, characterized in that: include: A three-dimensional model generation module is used to collect medical image data of the patient's ankle and foot, pre-process the medical image data, segment the pathological tissue of the deformed foot based on the pre-processed medical image data, obtain the segmentation result, and convert the segmentation result into an initial three-dimensional model of the pathological tissue of the deformed foot; The pressure sore risk assessment module is used to mesh the initial three-dimensional model, generate a finite element mesh model of the pathological tissue of the clubfoot, calculate the material properties of the pathological tissue of the clubfoot in the finite element mesh model according to the gray value, perform multi-scale analysis on the finite element mesh model, obtain the stress tensor and strain tensor of the clubfoot, and assess the pressure sore risk index of the clubfoot; A design parameter output module, which is used to output the design parameters of the orthosis according to the material properties of the clubfoot, the stress tensor, the strain tensor and the pressure sore risk index using a pre-trained orthosis parameter prediction model; The local environment adjustment module is used to optimize the thickness of the anti-pressure sore buffer layer of the orthosis and the graduation density and size distribution of the ventilation holes based on the pressure sore risk index. The pressure sensor and temperature and humidity sensor are built into the orthosis to adjust the local environment in real time. The 3D printing conversion module is used to generate a three-dimensional solid model based on the design parameters of the orthosis, and convert it into a 3D printing file, and use a 3D printer to manufacture a solid orthosis.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps in the parametric design optimization method for ankle-foot orthosis for 3D printing as described in any one of claims 1-7.

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