Intelligent nail placement path planning and quantitative evaluation method and device for wrist scaphoid fracture fixation

Abstract

The application discloses a kind of scaphoid fracture fixing intelligent nail placement path planning and quantitative evaluation method and device, belong to medical engineering technical field.The method includes: obtaining three-dimensional point cloud data;According to three-dimensional point cloud data, utilize RANSAC algorithm to identify fracture surface, obtain fracture surface center;According to fracture surface and fracture center, calculate all feasible nail placement paths;Second, all feasible nail placement paths are path planning hard constraint, obtain feasible nail placement path after hard constraint;Utilize NSGA-Ⅱ algorithm to the feasible nail placement path after hard constraint is soft constraint, obtain optimal nail placement path;Obtain clinical operation nail placement path;Finally, using quantitative evaluation method, evaluate the gap between clinical operation nail placement path and optimal nail placement path, obtain evaluation result.By using the application, the reliability and accuracy of nail placement path planning can be improved.

Description

Technical Field

[0001] This invention relates to the field of medical engineering technology, and in particular to a method and device for intelligent screw placement path planning and quantitative evaluation of scaphoid fracture fixation. Background Technology

[0002] Scaphoid fractures are a type of wrist joint fracture, accounting for approximately 60% of all wrist fractures, second only to radius fractures. As a highly utilized organ, a scaphoid fracture can severely impact a patient's work and daily life. If left untreated in its early stages, a scaphoid fracture can easily lead to traumatic arthritis, significantly affecting wrist joint function. Therefore, it is crucial to detect the injury promptly and resume treatment as early as possible to avoid irreversible loss of wrist function due to subsequent traumatic arthritis. Some studies have shown that surgical treatment of scaphoid fractures with pinning can provide a higher healing rate, helping to fix the fracture ends and improve blood supply around the bone, ultimately achieving bony union.

[0003] Traditional methods for planning screw placement pathways in scaphoid fractures primarily involve determining the path based on screw length, placement direction, and strength. This requires placing screws both perpendicular to the fracture line and along the longitudinal axis of the scaphoid bone to define the placement path. Furthermore, the screw length needs continuous adjustment based on the placement path length, ensuring that the screw length is less than the path length. The lack of constraints on the placement path length leads to a complex planning process and lower reliability and accuracy of the results. Summary of the Invention

[0004] To address the technical problem of low reliability and accuracy in existing technologies for screw placement path planning in scaphoid fractures, this invention provides a method and device for intelligent screw placement path planning and quantitative evaluation in scaphoid fracture fixation. The technical solution is as follows:

[0005] On the one hand, a method for intelligent screw placement path planning and quantitative evaluation of scaphoid fracture fixation is provided. This method is implemented by an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation, and includes:

[0006] S1. Acquire 3D point cloud data; the 3D point cloud data is the point cloud data of the skin, scaphoid bone and surrounding bones for reconstructing a 3D model;

[0007] S2. Based on the 3D point cloud data, use the RANSAC algorithm to identify the fracture surface and obtain the center of the fracture surface;

[0008] S3. Calculate all feasible screw placement paths based on the fracture surface and fracture center;

[0009] S4. Apply hard constraints to path planning for all feasible pinning paths to obtain feasible pinning paths after hard constraints.

[0010] S5. Use the NSGA-II algorithm to apply soft constraints to the feasible pinning paths after hard constraints to obtain the optimal pinning path.

[0011] S6. Obtain the clinical surgical nail placement path;

[0012] S7. Use quantitative evaluation methods to assess the difference between the clinical surgical screw placement path and the optimal screw placement path, and obtain the evaluation results.

[0013] Optionally, S3 calculates all feasible screw placement paths based on the fracture surface and fracture center, including:

[0014] S31. Set the guide needle length and obtain the direction of the fracture surface normal vector based on the guide needle length and the three-dimensional point cloud data;

[0015] S32. Obtain the cone-shaped space based on the direction of the fracture surface normal vector, the center of the fracture surface, and the normal vector of the guide pin length;

[0016] S33. Calculate all feasible nail placement paths based on the direction of the cone-shaped space and the normal vector of the fracture surface.

[0017] Optionally, the hard constraints of S4 include: path length constraints, path angle constraints, and path obstacle avoidance constraints.

[0018] Optionally, S5 uses the NSGA-II algorithm to apply soft constraints to the feasible pinning paths after hard constraints, obtaining the optimal pinning path, including:

[0019] S51. Set soft constraints;

[0020] S52. Use the set soft constraints as the objective function of the NSGA-II algorithm, and use the NSGA-II algorithm to filter the feasible pinning paths after hard constraints to obtain the optimal pinning path.

[0021] Optionally, soft constraints include: path distance conditions, path length conditions, and path angle conditions.

[0022] On the other hand, a smart screw placement path planning and quantitative evaluation device for scaphoid fracture fixation is provided. This device is applied to the smart screw placement path planning and quantitative evaluation method for scaphoid fracture fixation. The device includes:

[0023] The first acquisition unit is used to acquire three-dimensional point cloud data; the three-dimensional point cloud data is the point cloud data of the three-dimensional model reconstructed from the images of the skin, scaphoid bone and surrounding bones;

[0024] The second acquisition unit is used to identify the fracture surface and obtain the center of the fracture surface based on the RANSAC algorithm according to the three-dimensional point cloud data.

[0025] The calculation unit is used to calculate all feasible screw placement paths based on the fracture surface and the fracture center;

[0026] The first constraint unit is used to perform hard constraints on path planning for all feasible pinning paths, and obtain the feasible pinning paths after hard constraints.

[0027] The second constraint unit is used to apply soft constraints to the feasible pinning paths after hard constraints using the NSGA-II algorithm to obtain the optimal pinning path.

[0028] The third acquisition unit is used to acquire the clinical surgical nail placement path;

[0029] The evaluation unit is used to assess the difference between the clinical surgical screw placement path and the optimal screw placement path using quantitative evaluation methods, and to obtain evaluation results.

[0030] Optionally, the computing unit is used for:

[0031] Set the guide needle length, and obtain the fracture surface normal vector direction based on the guide needle length and the 3D point cloud data;

[0032] The cone-shaped space is obtained based on the normal vector direction of the fracture surface, the center of the fracture surface, and the normal vector of the guide pin length;

[0033] Based on the direction of the cone-shaped space and the normal vector of the fracture surface, all feasible nail placement paths are calculated.

[0034] Optionally, the first constraint element is used for:

[0035] Apply hard constraints to all feasible pinning paths for path planning, including path length constraints, path angle constraints, and path obstacle avoidance constraints.

[0036] Optionally, the second constraint element is used for:

[0037] Set soft constraints;

[0038] The set soft constraints are used as the objective function of the NSGA-II algorithm. The NSGA-II algorithm is then used to filter the feasible pinning paths after hard constraints to obtain the optimal pinning path.

[0039] Optionally, soft constraints include: path distance conditions, path length conditions, and path angle conditions.

[0040] On the other hand, a device for intelligent screw placement path planning and quantitative evaluation of scaphoid fracture fixation is provided. The device includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements any one of the methods described above for intelligent screw placement path planning and quantitative evaluation of scaphoid fracture fixation.

[0041] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the methods in the above-described intelligent screw placement path planning and quantitative evaluation method for scaphoid fracture fixation.

[0042] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0043] This invention first acquires three-dimensional point cloud data. Based on the 3D point cloud data, the RANSAC algorithm is used to identify the fracture surface and obtain the center of the fracture surface. All feasible screw placement paths are calculated based on the fracture surface and the fracture center. Next, hard constraints are applied to all feasible screw placement paths to obtain hard-constrained feasible screw placement paths. The NSGA-II algorithm is then used to apply soft constraints to the hard-constrained feasible screw placement paths to obtain the optimal screw placement path. The clinical surgical screw placement path is then obtained. Finally, a quantitative evaluation method is used to assess the difference between the clinical surgical screw placement path and the optimal screw placement path to obtain the evaluation result. Compared with existing technologies, this invention sets hard and soft constraints on feasible screw placement paths, eliminating path problems such as penetrating bone, excessively small needle insertion angles, and excessively short path lengths, thus improving the reliability and accuracy of screw placement path planning. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of a method for intelligent screw placement path planning and quantitative evaluation for scaphoid fracture fixation provided by an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the specific constraint content of applying soft and hard constraints to feasible pin placement paths according to an embodiment of the present invention.

[0047] Figure 3 This is a block diagram of an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the structure of an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation provided in an embodiment of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0050] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0051] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0052] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0053] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0054] This invention provides a method for intelligent screw placement path planning and quantitative evaluation of scaphoid fracture fixation. This method can be implemented using an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation, which can be a terminal or a server. Figure 1 The flowchart shown is for a method of intelligent screw placement path planning and quantitative evaluation for scaphoid fracture fixation. The processing flow of this method may include the following steps:

[0055] S1. Obtain 3D point cloud data.

[0056] Among them, the three-dimensional point cloud data is the point cloud data of the three-dimensional model of the skin, scaphoid bone and surrounding bones reconstructed from images.

[0057] In one feasible implementation, images of the segmented skin, scaphoid bone, and surrounding bones are selected for three-dimensional reconstruction. The reconstructed three-dimensional model of the skin, scaphoid bone, and surrounding bones is used as input and converted into a point cloud file in .ply format to obtain three-dimensional point cloud data.

[0058] S2. Based on the 3D point cloud data, use the RANSAC algorithm to identify the fracture surface and obtain the center of the fracture surface.

[0059] In one feasible implementation method, the specific steps of the RANSAC algorithm are as follows:

[0060] (1) Randomly select 3 points from the 3D point cloud data as the initial points for plane fitting, calculate the plane normal vector based on the 3 randomly selected points, and use the plane normal vector to determine whether other points are on the plane;

[0061] (2) If some points satisfy the equation of the plane, they are called interior points; otherwise, they are called exterior points.

[0062] Among them, the inner points are the points that need to be retained and can be used to determine the fracture surface; the outer points are the points that need to be deleted.

[0063] (3) By iterating continuously, better interior point and plane parameters are obtained until the stopping condition is met, and the fracture surface is determined.

[0064] The point located at the center of the fracture surface is the center of the fracture surface.

[0065] S3. Calculate all feasible screw placement paths based on the fracture surface and fracture center.

[0066] Optionally, the specific operation of S3 may include the following steps S31-S33:

[0067] S31. Set the guide needle length and obtain the direction of the fracture surface normal vector based on the guide needle length and the three-dimensional point cloud data;

[0068] The length of the guide needle can be set to 24cm.

[0069] The direction of the normal vector of the fracture surface is calculated to ensure the correct needle insertion direction.

[0070] In one feasible implementation, the direction of the normal vector of the fracture surface can be obtained by the following formula (1):

[0071]

[0072] in, Let P represent the normal vector of the fracture surface, and let P represent the center point of the fracture surface. m1 P m2 , where p represents the endpoint of each feasible pinning path, and v represents the grayscale value at point p.

[0073] Among them, P m1 P m2 The value can be obtained through the following formulas (2) and (3):

[0074]

[0075]

[0076] Among them, P m1 P m2 These represent the endpoints of each feasible pinning path. Let P represent the normal vector of the fracture surface, and let l represent the center point of the fracture surface. m Indicates the length of the guide pin.

[0077] S32. Obtain the cone-shaped space based on the direction of the fracture surface normal vector, the center of the fracture surface, and the normal vector of the guide pin length;

[0078] One approach is to insert the needle using the distal surface of the palmar scaphoid tubercle, with the distal fracture surface chosen as the initial insertion surface.

[0079] The minimum screw insertion angle can be set to 40°, and the screw length can be set to 2.4cm.

[0080] In one feasible implementation, a coordinate system is established with the vertex of the conical space as the origin of the coordinate system, the normal vector of the fracture surface as the z-axis, and the fracture surface as the xoy plane. The conical space can be represented by the following formula (4):

[0081]

[0082] in, Represents a conical space, l m Indicates the length of the guide pin. This represents the projection of the needle insertion path onto the xoy plane.

[0083] in, It can be expressed by the following formula (5):

[0084]

[0085] in, This represents the projection of the needle insertion path onto the xoy plane, where β represents a variable. It will change with the value of β, ranging from 0 to 360.

[0086] S33. Calculate all feasible nail placement paths based on the direction of the cone-shaped space and the normal vector of the fracture surface.

[0087] In one feasible implementation, the direction vector of the straight line containing the feasible pin placement path can be represented by the following formula (6):

[0088]

[0089] in, This represents the direction vector of the line containing the feasible pinning path. This indicates that the path is projected onto the xoy plane. Let α represent the normal vector of the fracture surface, and let α represent the tilt angle along the z-axis.

[0090] S4. Apply hard constraints to path planning for all feasible pinning paths to obtain feasible pinning paths after hard constraints.

[0091] Optionally, hard constraints include: path length constraints, path angle constraints, and path obstacle avoidance constraints.

[0092] (1) Path length constraint

[0093] In one feasible implementation, the needle is inserted at any point P on the skin surface. S (x s ,y s ,z s ) and the intersection point P of the path and the proximal scaphoid bone i (x i ,y i ,z i The distance D between them a It can be expressed by the following formula (7):

[0094]

[0095] In one feasible implementation, the path intersects at the proximal scaphoid point P. i (x i ,y i ,z i ) and the intersection point P of the distal scaphoid bone of the path j (x j ,y j ,z j The distance D between them b It can be expressed by the following formula (8):

[0096]

[0097] Where, when D a Less than or equal to the guide needle length of 21cm and D b If the screw length is greater than or equal to 2.4cm, the path corresponding to the insertion point is considered to meet the constraints of the guide needle length and the inserted screw length; otherwise, it is not met.

[0098] (2) Path angle constraints

[0099] In one feasible implementation, if the angle between the skin surface and the guide needle is too small when inserting the guide needle, the guide needle is prone to sliding on the skin surface, causing path deviation or difficulty in needle insertion. The insertion angle of the guide needle should be at least greater than 20°.

[0100] Among them, at the needle entry point P S Take k nearest points and perform plane fitting using the least squares method on these k points. Since the needle insertion points are relatively dense, the fitted local plane can be used as the surface S of the hand skin. The local plane can be represented by the following formula (9):

[0101]

[0102] in, Let d represent the normal vector on the plane. i Indicates the needle entry point P S Distance to the origin This represents the direction vectors of the selected k points.

[0103] Wherein, the locally fitted plane S passes through k m The centroid of the nearest k-neighbor It can be expressed by the following formula (10):

[0104]

[0105] Where l takes values ​​from 1 to k, and k represents the needle insertion point P. S Take the number of neighboring points.

[0106] The plane S, which is locally fitted as described above, passes through k m The centroid of the nearest k-neighbor Solving for the covariance matrix M, we can obtain the covariance matrix M, which can be expressed by the following formula (11):

[0107]

[0108] To obtain the local plane, the covariance M needs to be decomposed into eigenvalues, which are then used respectively. Indicates. If but The corresponding eigenvector is the normal vector. From the direction vector of the guide needle insertion path, the angle θ between the guide needle path passing through the skin insertion point and the normal vector of the skin surface can be expressed by the following formula (12):

[0109]

[0110] in, This represents the direction vector of the line containing the feasible pinning path. This represents the normal vector on the plane.

[0111] The above formula is used to remove paths where the angle between the guide needle and the skin surface is less than 20°.

[0112] (3) Path obstacle avoidance constraints

[0113] When the guide needle is inserted, it will pass through various bones in the wrist, including: trapezium, trapezium, capitate, lunate, and radius.

[0114] In one feasible implementation, the vector from the center point P of the fracture surface along the feasible nail placement path direction is... The search continues for the next point on the opposite path. If the next point is not a labeled bone, the search continues; otherwise, the search for that path is terminated. If no labeled bone appears after traversing all points on the feasible pinning path, the path satisfies the obstacle avoidance constraint.

[0115] S5. Use the NSGA-II algorithm to apply soft constraints to the feasible pinning paths after hard constraints to obtain the optimal pinning path.

[0116] Optionally, the specific operation of S5 may include the following steps S51-S52:

[0117] S51. Set soft constraints;

[0118] Optionally, soft constraints include: path distance conditions, path length conditions, and path angle conditions.

[0119] (1) Path distance condition

[0120] In one feasible implementation, the guide needle insertion path should be far from the surrounding bones of the scaphoid bone, treating the surrounding bones as a whole, and performing an expansion operation centered on the path, stopping upon encountering the marked bone; the intersection of the expansion path and the marked bone is denoted as P. e (i), P e (i) Distance L from the straight line containing the path i This is the distance from the i-th path to the critical structure. The path risk sub-objective function under the condition of path-skeleton distance can be expressed by the following formula (13):

[0121]

[0122] Among them, L min and L max These are the minimum and maximum distances to the critical structure, respectively. L r (i) takes values ​​from 0 to 1. The greater the distance between the path and the skeleton, the smaller the value of the sub-objective function and the lower the risk of the corresponding path.

[0123] (2) Path angle conditions

[0124] In one feasible implementation, the screw insertion angle is constrained, and the path risk sub-objective function under the angle constraint can be expressed by the following formula (14):

[0125]

[0126] Among them, the screw insertion angle γ i =90-α i γ min γ represents the minimum value of the effective path and the angle of entry into the fracture surface. max This represents the maximum value of the effective path and the angle of entry into the fracture surface. The larger the angle of entry, the smaller the value of the sub-objective function, and the lower the risk of the corresponding path.

[0127] (3) Path length condition

[0128] In one feasible implementation, the path risk sub-objective function under path length constraints can be expressed by the following formula (15):

[0129]

[0130] in, D s The longer the screw in the scaphoid bone, the lower the risk of the corresponding path and the better the screw placement effect.

[0131] S52. Use the set soft constraints as the objective function of the NSGA-II algorithm, and use the NSGA-II algorithm to filter the feasible pinning paths after hard constraints to obtain the optimal pinning path.

[0132] In one feasible implementation method, the specific implementation steps of the NSGA-II algorithm are as follows:

[0133] (1) First, the initial population is randomly generated. After non-dominated sorting, the first generation of offspring population is obtained through the three basic operations of selection, crossover and mutation of the genetic algorithm.

[0134] (2) Starting from the second generation, the parent population and the offspring population are merged and a fast non-dominated sort is performed. At the same time, the crowding degree of individuals in each non-dominated layer is calculated. Based on the non-dominated relationship and the crowding degree of individuals, suitable individuals are selected to form a new parent population.

[0135] (3) Generate a new offspring population through the basic operations of the genetic algorithm; and so on, until the conditions for program termination are met.

[0136] Figure 2 This describes the specific constraints for applying soft and hard constraints to feasible pinning paths.

[0137] In one feasible implementation, the feasible pins are subject to soft constraints, which may include length constraints, angle constraints, and obstacle constraints.

[0138] The length constraint can be applied to the guide pin length and the screw length, requiring that the path length be less than the guide pin length and the screw length be less than the intrascaphoid trajectory length.

[0139] Among them, the angle constraint can be used to constrain the angle between the guide needle and the skin surface, which must be no less than 20°.

[0140] Among them, obstacle constraints can be applied to the surrounding skeleton, requiring that the needle path not pass through the surrounding bone structure.

[0141] In one feasible implementation, after applying soft constraints to the feasible pins, hard constraints can be set on the feasible pin paths, which may include distance conditions, length conditions, and angle conditions.

[0142] Among these, the distance condition is to maximize the distance between the path and important bones; the length condition is to make the screw portion within the scaphoid as long as possible; and the angle condition is to make the path as perpendicular as possible to the fracture surface.

[0143] S6. Obtain the clinical surgical nail placement path.

[0144] S7. Use quantitative evaluation methods to assess the difference between the clinical surgical screw placement path and the optimal screw placement path, and obtain the evaluation results.

[0145] In one feasible implementation, the evaluation indicators in the quantitative evaluation method may include: angle, intrascapular length, and vertical compression distance.

[0146] Among them, the angle is the included angle in the vertical direction of the fracture surface; the length is the length of the screw in the scaphoid bone; the vertical compression distance is the angular projection of the screw in the vertical direction of the fracture surface. The larger the vertical compression distance, the better the screw placement path planning effect.

[0147] This invention first acquires three-dimensional point cloud data. Based on the 3D point cloud data, the RANSAC algorithm is used to identify the fracture surface and obtain the center of the fracture surface. All feasible screw placement paths are calculated based on the fracture surface and the fracture center. Next, hard constraints are applied to all feasible screw placement paths to obtain hard-constrained feasible screw placement paths. The NSGA-II algorithm is then used to apply soft constraints to the hard-constrained feasible screw placement paths to obtain the optimal screw placement path. The clinical surgical screw placement path is then obtained. Finally, a quantitative evaluation method is used to assess the difference between the clinical surgical screw placement path and the optimal screw placement path to obtain the evaluation result. Compared with existing technologies, this invention sets hard and soft constraints on feasible screw placement paths, eliminating path problems such as penetrating bone, excessively small needle insertion angles, and excessively short path lengths, thus improving the reliability and accuracy of screw placement path planning.

[0148] Figure 3 This is a block diagram illustrating an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation according to an exemplary embodiment. The device is used in an intelligent screw placement path planning and quantitative evaluation method for scaphoid fracture fixation. (Refer to...) Figure 3 The device includes a first acquisition unit 310, a second acquisition unit 320, a calculation unit 330, a first constraint unit 340, a second constraint unit 350, a third acquisition unit 360, and an evaluation unit 370. For ease of explanation, Figure 3 Only the main components of the full-process visualization device 600 are shown:

[0149] The first acquisition unit 310 is used to acquire three-dimensional point cloud data; the three-dimensional point cloud data is the point cloud data of the three-dimensional model of the skin, scaphoid bone and surrounding bones reconstructed from images;

[0150] The second acquisition unit 320 is used to identify the fracture surface and obtain the center of the fracture surface based on the three-dimensional point cloud data using the RANSAC algorithm.

[0151] The calculation unit 330 is used to calculate all feasible screw placement paths based on the fracture surface and the fracture center.

[0152] The first constraint unit 340 is used to perform hard constraints on path planning for all feasible pinning paths to obtain feasible pinning paths after hard constraints.

[0153] The second constraint unit 350 is used to apply soft constraints to the feasible pinning path after hard constraints using the NSGA-II algorithm to obtain the optimal pinning path.

[0154] The third acquisition unit 360 is used to acquire the clinical surgical screw placement path;

[0155] Evaluation unit 370 is used to evaluate the difference between the clinical surgical screw placement path and the optimal screw placement path using a quantitative evaluation method, and obtain evaluation results.

[0156] Optionally, the computing unit 330 is used for:

[0157] Set the guide needle length, and obtain the fracture surface normal vector direction based on the guide needle length and the 3D point cloud data;

[0158] The cone-shaped space is obtained based on the normal vector direction of the fracture surface, the center of the fracture surface, and the normal vector of the guide pin length;

[0159] Based on the direction of the cone-shaped space and the normal vector of the fracture surface, all feasible nail placement paths are calculated.

[0160] Optionally, the first constraint unit 340 is used for:

[0161] Hard constraints include: path length constraints, path angle constraints, and path obstacle avoidance constraints.

[0162] Optionally, the second constraint unit 350 is used for:

[0163] Set soft constraints;

[0164] The set soft constraints are used as the objective function of the NSGA-II algorithm. The NSGA-II algorithm is then used to filter the feasible pinning paths after hard constraints to obtain the optimal pinning path.

[0165] Optionally, soft constraints include: path distance conditions, path length conditions, and path angle conditions.

[0166] This invention first acquires three-dimensional point cloud data. Based on the 3D point cloud data, the RANSAC algorithm is used to identify the fracture surface and obtain the center of the fracture surface. All feasible screw placement paths are calculated based on the fracture surface and the fracture center. Next, hard constraints are applied to all feasible screw placement paths to obtain hard-constrained feasible screw placement paths. The NSGA-II algorithm is then used to apply soft constraints to the hard-constrained feasible screw placement paths to obtain the optimal screw placement path. The clinical surgical screw placement path is then obtained. Finally, a quantitative evaluation method is used to assess the difference between the clinical surgical screw placement path and the optimal screw placement path to obtain the evaluation result. Compared with existing technologies, this invention sets hard and soft constraints on feasible screw placement paths, eliminating path problems such as penetrating bone, excessively small needle insertion angles, and excessively short path lengths, thus improving the reliability and accuracy of screw placement path planning.

[0167] Figure 4 This is a structural schematic diagram of an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation can include the above-mentioned... Figure 3 The illustrated device is an intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation. Optionally, the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation may include a processor 2001.

[0168] Optionally, the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation may also include a memory 2002 and a transceiver 2003.

[0169] The processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0170] The following is combined Figure 4 A detailed introduction to each component of the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation:

[0171] The processor 2001 is the control center of the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation. It can be a single processor or a collective term for multiple processing elements. For example, the processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0172] Optionally, the processor 2001 can perform various functions of the intelligent screw placement path planning and quantitative evaluation device 410 for wrist scaphoid fracture fixation by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0173] In a specific implementation, as one example, the processor 2001 may include one or more CPUs, for example... Figure 4 CPU0 and CPU1 are shown in the diagram.

[0174] In a specific implementation, as one example, the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation may also include multiple processors, such as... Figure 4 The processors 2001 and 2004 are shown. Each of these processors can be a single-core processor or a multi-core processor. Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0175] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0176] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the processor 2001 or exist independently, and may be connected via the interface circuit of the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation. Figure 4 (Not shown in the figure) is coupled to processor 2001, and the embodiments of the present invention do not specifically limit this.

[0177] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0178] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 4 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0179] Optionally, the transceiver 2003 can be integrated with the processor 2001 or exist independently, and can be connected to the interface circuit of the intelligent screw placement path planning and quantitative evaluation device 410 for wrist scaphoid fracture fixation. Figure 4 (Not shown in the figure) is coupled to processor 2001, and the embodiments of the present invention do not specifically limit this.

[0180] It should be noted that, Figure 4 The structure of the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0181] Furthermore, the technical effect of the intelligent screw placement path planning and quantitative evaluation device 410 for scaphoid fracture fixation can be referred to the technical effect of the intelligent screw placement path planning and quantitative evaluation method for scaphoid fracture fixation described in the above method embodiments, and will not be repeated here.

[0182] It should be understood that the processor 2001 in this embodiment of the invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0183] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0184] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0185] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0186] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0187] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0188] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0189] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0190] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0191] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0192] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0193] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0194] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent screw placement path planning and quantitative evaluation for scaphoid fracture fixation, characterized in that, The method includes: S1. Acquire three-dimensional point cloud data; the three-dimensional point cloud data is the point cloud data of the three-dimensional model of the skin, scaphoid bone and surrounding bones reconstructed from images; S2. Based on the three-dimensional point cloud data, the RANSAC algorithm is used to identify the fracture surface and obtain the center of the fracture surface; S3. Calculate all feasible screw placement paths based on the fracture surface and fracture center; Specifically, S3 calculates all feasible screw placement paths based on the fracture surface and fracture center, including: S31. Set the guide needle length and obtain the direction of the fracture surface normal vector based on the guide needle length and the three-dimensional point cloud data; S32. Obtain the cone-shaped space based on the direction of the fracture surface normal vector, the center of the fracture surface, and the normal vector of the guide pin length; S33. Calculate all feasible nail placement paths based on the direction of the cone-shaped space and the normal vector of the fracture surface; S4. Apply hard constraints to path planning for all feasible pinning paths to obtain feasible pinning paths after hard constraints. The hard constraints of S4 include: path length constraint, path angle constraint, and path obstacle avoidance constraint. S5. Use the NSGA-II algorithm to apply soft constraints to the feasible pinning paths after the hard constraints, and obtain the optimal pinning path. Specifically, S5 utilizes the NSGA-II algorithm to apply soft constraints to the feasible pinning paths after the hard constraints, thereby obtaining the optimal pinning path, including: S51. Set soft constraints; The soft constraints include: path distance conditions, path length conditions, and path angle conditions. S52. Use the set soft constraints as the objective function of the NSGA-II algorithm, and use the NSGA-II algorithm to filter the feasible pinning paths after hard constraints to obtain the optimal pinning path. S6. Obtain the clinical surgical nail placement path; S7. Using a quantitative evaluation method, assess the difference between the clinical surgical screw placement path and the optimal screw placement path to obtain the evaluation results.

2. A device for intelligent screw placement path planning and quantitative evaluation for scaphoid fracture fixation, characterized in that, The device includes: The first acquisition unit is used to acquire three-dimensional point cloud data; the three-dimensional point cloud data is the point cloud data of the three-dimensional model reconstructed from the images of the skin, scaphoid bone and surrounding bones; The second acquisition unit is used to identify the fracture surface and obtain the center of the fracture surface based on the RANSAC algorithm according to the three-dimensional point cloud data. The calculation unit is used to calculate all feasible screw placement paths based on the fracture surface and the fracture center; The computing unit is used for: Set the guide needle length, and obtain the fracture surface normal vector direction based on the guide needle length and the 3D point cloud data; The cone-shaped space is obtained based on the normal vector direction of the fracture surface, the center of the fracture surface, and the normal vector of the guide pin length; Based on the direction of the cone-shaped space and the normal vector of the fracture surface, all feasible nail placement paths are calculated; The first constraint unit is used to perform hard constraints on path planning for all feasible pinning paths, and obtain the feasible pinning paths after hard constraints. The hard constraints include: path length constraints, path angle constraints, and path obstacle avoidance constraints. The second constraint unit is used to apply soft constraints to the feasible pinning paths after hard constraints using the NSGA-II algorithm to obtain the optimal pinning path. Wherein, the second constraint unit is used for: Set soft constraints; The soft constraints include: path distance conditions, path length conditions, and path angle conditions. The set soft constraints are used as the objective function of the NSGA-II algorithm. The NSGA-II algorithm is then used to filter the feasible pinning paths after hard constraints to obtain the optimal pinning path. The third acquisition unit is used to acquire the clinical surgical nail placement path; The evaluation unit is used to assess the difference between the clinical surgical screw placement path and the optimal screw placement path using quantitative evaluation methods, and to obtain evaluation results.

3. A device for intelligent screw placement path planning and quantitative evaluation for scaphoid fracture fixation, characterized in that, The intelligent screw placement path planning and quantitative evaluation device for scaphoid fracture fixation includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in claim 1.

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