Intelligent planning method and system for forearm angular deformity

Abstract

The present invention discloses an intelligent planning method and system for forearm angular deformity, which realizes osteotomy and repositioning and correction of the deformed bone through a three-dimensional model of the patient's deformed bone and the contralateral healthy bone. The beneficial effects of the present invention are: using a registration method based on joint anatomical regions to calculate the difference between the deformed forearm and the healthy contralateral side, constructing a reconstruction template for the deformed bone, and calculating the deformed area of ​​the deformed bone; using the deformed area as the osteotomy range constraint, calculating the osteotomy position and reduction angle that optimizes the reduction target, automatically generating the solution required for the clinical goal, improving the effect and accuracy of preoperative planning for osteotomy correction of forearm angular deformity, and greatly reducing the time required for preoperative planning, and realizing intelligent preoperative planning.

Description

Technical Field

[0001] The present invention relates to a forearm deformity planning method, in particular to a forearm angular deformity intelligent planning method and system, belonging to the technical field of intelligent diagnosis and treatment systems. Background Art

[0002] The forearm is the part of the human body that realizes the pronation and supination functions of the upper limbs. It is composed of the radius and ulna. Post-traumatic healing (malunion) of the forearm bones in non-anatomical positions or congenital bone deformation can lead to functional disorders such as limited range of motion of the patient's arm, reduced grip strength, and instability of the distal radioulnar joint, as well as pain. If not treated properly, it can lead to severe degenerative pathologies such as osteoarthritis.

[0003] Angulated deformity is a common type of forearm bone deformity. The current gold standard for surgical treatment of angular deformity of forearm bones is to restore the normal anatomical structure through osteotomy correction. Clinicians cut the pathological bone into two or more bone fragments, rearrange them to a reasonable physiological position, and stabilize them with bone plates or implants. This corrective surgery is complex to implement and requires clinicians to develop precise preoperative planning plans.

[0004] In the prior art: 1) Preoperative planning is that clinicians manually calculate the osteotomy position and correction angle based on their clinical experience, X-rays, CT scans and other imaging data, and follow the basic principles of osteotomy and correction to achieve the purpose of limb correction. However, this osteotomy and correction method requires clinicians to draw and calculate manually, so there are problems such as the inability to scientifically predict the surgical effect before the operation, insufficient surgical accuracy, and poor homogeneity of operations performed by different doctors, which in turn affects the formulation of the optimal surgical plan;

[0005] 2) With the development of computer-aided design in medicine, many computer-aided preoperative osteotomy and correction software systems have been developed. Among them, computer-aided osteotomy based on two-dimensional technology has solved the preoperative planning of osteotomy and correction for some simple deformities. It can automatically calculate the osteotomy angle and reposition the bone segment after osteotomy. However, these systems still have some shortcomings: for multi-plane deformities and complex mixed deformities, two-dimensional medical images cannot reflect the three-dimensional inherent characteristics of bone deformities, and still require clinicians to operate based on experience, which consumes a lot of time and energy of clinicians, and the data obtained is not accurate enough;

[0006] 3) Although preoperative manual planning based on 3D computer-assisted osteotomy can intuitively analyze complex mixed deformities in multiple planes and perform osteotomy correction, it requires clinicians to conduct repeated experiments and manual calculations and verifications, involving multiple manual adjustments in 3D space, resulting in unnecessary clinical costs. Summary of the invention

[0007] The purpose of the present invention is to provide an intelligent planning method and system for forearm angular deformity in order to solve at least one of the above-mentioned technical problems, so as to achieve osteotomy and repositioning and correction of the deformed bone through a three-dimensional model of the patient's deformed bone and the contralateral healthy bone.

[0008] The present invention achieves the above-mentioned purpose through the following technical scheme: A forearm angular deformity intelligent planning method comprises the following steps:

[0009] Step 1: Establish a mirror image model of the healthy bone on the opposite side of the deformed forearm bone to make it a reconstruction template for correction and reduction;

[0010] Step 2: Set the joint anatomical region for the radius and ulna of the forearm bones, perform weighted registration based on the anatomical region on the distal and proximal ends of the radius and ulna, and obtain the matrices for the distal and proximal registration respectively;

[0011] Step 3: Calculate the displacement along the long axis of the bone in the transformation matrix based on the distal and proximal comprehensive registration matrices;

[0012] Step 4: taking the displacement as the difference between the two sides of the deformed forearm bone, compensating for the difference in the reconstruction template, and constructing a new reconstruction template;

[0013] Step 5: align the distal and proximal anatomical regions of the forearm deformed bone with the new reconstruction template, set the deformity threshold, and calculate the deformity region of the forearm deformed bone;

[0014] Step 6: Calculate the direction of the reset rotation axis according to the registration transformation matrix between the forearm deformed bone and the new reconstruction template;

[0015] Step 7: Using the origin as the starting point, construct a direction vector in the same direction as the rotation axis, and calculate the transformation matrix M that transforms this direction vector to coincide with the Y axis of the world coordinate system y ;

[0016] Step 8: Perform spatial transformation M on the deformed bone and reconstruction template y , project the deformed area contour of the deformed bone onto the XOZ plane, interpolate with the cubic spline function, and obtain the deformed contour curve function expression;

[0017] Step 9: Take any point on the contour curve as the rotation axis position. The direction of the rotation axis has been determined, so the orientation of the osteotomy correction reduction rotation axis is determined;

[0018] Step 10: Align the anatomical region of the joint of the deformed forearm bone as the reduction target, take any point on the contour curve as the rotation axis orientation parameter, take the angle of rotation of the anatomical region around the rotation axis as the reduction angle parameter, and use the quasi-Newton method in the traditional nonlinear optimization method to obtain the rotation axis orientation parameter and reduction angle parameter that minimize the reduction target error.

[0019] As a further solution of the present invention: in the step one, the mirror image model of the healthy bone on the opposite side of the deformed forearm bone is generated by taking any two-dimensional coordinate plane in space as a reference plane, obtaining the symmetric point of each coordinate point about the reference plane, and generating the mirror image model according to the point cloud data.

[0020] As a further solution of the present invention: the anatomical region of the distal and proximal joints of the ulna in the forearm bone is defined as the distal and proximal 20% of the ulna along its long axis, and the weight is set to 1; the anatomical region of the proximal joint of the radius is defined as the proximal 20% of the long axis of the ulna, and the weight is set to 1; the anatomical region of the distal joint of the radius is connected to the wrist, and its anatomical region is defined by searching 50 points in its neighborhood using 7 anatomical points, which are the dorsal distal edge of the sigmoid notch, the distal edge of the palmar sigmoid notch, the center of the distal styloid process of the radius, the center of the lunate facet joint, the watershed line of Lister's tubercle and the center of the radius, and the alignment weights of the anatomical regions defined by the 7 anatomical points are: 0.18, 0.18, 0.18, 0.18, 0.05 and 0.05 respectively. The registration method uses an iterative closest point algorithm based on anatomical region weights.

[0021] As a further solution of the present invention: in the step 4, the relationship between the difference amounts on both sides of the forearm bones is: the length difference of the radius on both sides is equal to 0.98 times the length difference of the ulna on both sides, and the length that the reconstruction template of the deformed bone should have is calculated by the displacement difference of the healthy bone (radius or ulna), and the contralateral bone mirror model of the deformed bone is scaled along its long axis.

[0022] As a further solution of the present invention: in the step five, the deformed bone in the deformed forearm and its reconstruction template are aligned at the proximal end based on the weight of the joint anatomical area, and then the bone model is discretized along the long axis of the bone with a step size of 0.1 mm, and the root mean square error between the deformed bone and the reconstruction template within each step size is calculated, and the same operation is performed on the deformed bone and its reconstruction template at the distal end; after the alignment, the deformity threshold is set so that its root mean square error exceeds 15% of the minimum root mean square error, and this threshold is used to determine the range of bone deformity.

[0023] As a further solution of the present invention: in step 6, the proximal registration matrix of the deformed bone is M p , the registration matrix of the distal end of the deformed bone is M d , with M p -1*M d To synthesize the registration matrix, the repositioned rotation axis orientation was calculated using the Rogrigius formula.

[0024] As a further solution of the present invention: in the step nine, since the forearm angular deformity is treated by single-blade wedge osteotomy, the osteotomy segment is rotated and reset around the rotation axis passing through the deformity contour, and the direction of the rotation axis has been calculated, therefore, the orientation of the rotation axis is determined by the position of any point on the deformity contour line.

[0025] As a further solution of the present invention: in the step ten, the manual selection of the osteotomy position and the reduction angle is cumbersome and it is relatively complicated to find the optimal solution. Therefore, the optimal solution is calculated by the quasi-Newton method, and the position on the deformity contour curve is used as the osteotomy position and rotation axis position parameters, the angle of rotation of the osteotomy segment around the rotation axis is used as the reduction angle parameter, and the root mean square error of the alignment of the joint anatomical area is used as the target. A nonlinear continuous function model with the osteotomy position and the reduction angle as parameters is established, and the optimal solution of the model is calculated.

[0026] An intelligent planning system for forearm angular deformity, the system comprising the following modules:

[0027] Model space transformation module: used to adjust the spatial position of the skeleton model before intelligent planning;

[0028] Model registration module: by manually selecting the anatomical position or automatically calculating the anatomical position, the bone model is registered using an iterative closest point algorithm based on different weights of anatomical regions;

[0029] Automatic bone deformity diagnosis module: Based on anatomical region registration and the set deformity threshold, the bone deformity area of ​​the deformed bone in the deformed forearm is automatically calculated

[0030] Bone model cutting module: You can customize the osteotomy surface to cut the bone model;

[0031] Angular deformity single-blade osteotomy correction optimization module: The osteotomy range constraint is set according to the bone deformity range, and the quasi-Newton method is used to solve the osteotomy position and reduction angle that minimizes the alignment error of the joint anatomical area, forming an automatic pre-operative planning plan for single-blade osteotomy to correct forearm angular deformity.

[0032] The beneficial effects of the present invention are: using a registration method based on joint anatomical regions to calculate the difference between the deformed forearm and the healthy contralateral side, constructing a reconstruction template for the deformed bone, and calculating the deformed area of ​​the deformed bone; using the deformed area as the osteotomy range constraint, calculating the osteotomy position and reduction angle that optimize the reduction target, automatically generating the solution required for the clinical goal, improving the effect and accuracy of preoperative planning for osteotomy correction of forearm angular deformity, and significantly reducing the time required for preoperative planning, thereby realizing intelligent preoperative planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall structure of the preoperative intelligent planning software system according to an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of an automatic diagnosis module for bone deformity according to an embodiment of the present invention;

[0035] Figure 3 is a schematic diagram of registration of a deformed forearm bone and a contralateral forearm bone according to an embodiment of the present invention;

[0036] Figure 4 is a schematic diagram of the outline of the bone deformity region according to an embodiment of the present invention;

[0037] Figure 5 Schematic diagram of a single-blade wedge-shaped automatic osteotomy module for forearm angular deformity according to an embodiment of the present invention;

[0038] Figure 6 Schematic diagram of single-blade wedge osteotomy correction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] like Figure 1 As shown, an intelligent planning method for forearm angular deformity comprises the following steps:

[0042] Step 1: Establish a mirror image model of the healthy bone on the opposite side of the deformed forearm bone to make it a reconstruction template for correction and reduction;

[0043] Step 2: Set the joint anatomical region for the radius and ulna of the forearm bones, and perform registration, and calculate the displacement along the long axis of the bone in the transformation matrix according to the distal and proximal comprehensive registration matrices;

[0044] In step 2, the anatomical region of the distal radius joint in the forearm bone is composed of 7 anatomical regions defined by 7 clinical anatomical feature points.

[0045] Step 3: taking the displacement as the difference between the two sides of the deformed bone in the deformed forearm, compensating for the difference in the reconstruction template, and constructing a new reconstruction template;

[0046] In step 3, the method for compensating for the difference in the reconstruction template includes:

[0047] (1) Calculate the distal radius joint registration matrix, proximal radius joint registration matrix, distal ulna registration matrix, and proximal ulna registration matrix of the deformed forearm;

[0048] (2) calculating the displacement of the radius along the long axis of the reconstructed template bone during the registration process, and calculating the displacement of the ulna along the long axis of the reconstructed template bone during the registration process;

[0049] (3) Based on the relationship between the difference between the radius and ulna of the bilateral forearms, the deformed radius or ulna is scaled along the long axis of the bone to compensate for the difference in the reconstruction template.

[0050] Step 4: Calculate the deformed area and reduction rotation axis of the deformed forearm bone;

[0051] Step 5: Obtain the function expression of the deformity contour curve within the range of bone deformity;

[0052] In step 5, the deformity contour curve equation of the bone deformity area is obtained in a two-dimensional plane, and the correction rotation axis of the single-blade wedge osteotomy is perpendicular to the deformity contour curve in the two-dimensional plane.

[0053] Step 6: Use any point on the contour curve as the rotation axis position of the single-blade wedge osteotomy;

[0054] Step 7: Use the quasi-Newton method to calculate the osteotomy position and reduction angle of the forearm angular deformity to obtain the optimal osteotomy correction reduction target;

[0055] In step 7, the method for automatic osteotomy correction of forearm angular deformity includes:

[0056] (1) The osteotomy position and reduction angle were used as control variables, the minimum root mean square error of the alignment of the distal anatomical region of the deformed bone joint was used as the optimization goal, and the bone deformity region was used as the range constraint of the osteotomy position;

[0057] (2) Establish a nonlinear continuous function model between the optimization objective and the control variable, use the BFGS quasi-Newton method to iteratively optimize the function model, and find the minimum value of the optimization objective under the constraints;

[0058] (3) The optimal osteotomy position and reduction angle are used as the preoperative planning scheme for osteotomy correction. The deformed bone is osteotomized to simulate single-blade wedge osteotomy correction. The distal bone segment of the deformed bone is rotated around the rotation axis to calculate the reduction angle for correction.

[0059] Embodiment 2

[0060] like Figures 2 to 6 As shown, an intelligent planning method for forearm angular deformity comprises the following steps:

[0061] A1. Establish the long axis of the bone according to the skeletal model, use the model registration module to calculate the difference between the two sides of the forearm, compensate the reconstruction template, discretize the joint anatomical area with an appropriate step size, calculate the root mean square error of the model point set, determine the bone deformity area, fit the deformity contour curve equation, and calculate the reset rotation axis by registering the deformed bone with the reconstruction template;

[0062] A2. The purpose of automatic diagnosis of bone deformity is to use the determined range of bone deformity as a constraint for the selection of osteotomy position. Different osteotomy positions will lead to different reduction errors. Therefore, the automatic diagnosis of bone deformity determines the approximate range of the osteotomy plane to provide a parameter range basis for the nonlinear optimization quasi-Newton method.

[0063] Deformed forearm deformity Osteotomy correction of radial deformity

[0064] The following details:

[0065] The healthy forearm on the opposite side of the deformed forearm is imported into the system and presented in the form of an STL model. The XOZ plane in the three-dimensional world coordinate system is used as the symmetry plane to generate a mirror model of the healthy forearm on the opposite side, thus completing the establishment of the deformed forearm reconstruction template.

[0066] Import the deformed forearm and the reconstruction template into the system at the same time, perform registration based on the anatomical region weights on the radius and ulna in the deformed forearm and the radius and ulna in the reconstruction template, calculate the difference between the two sides through the registration matrix, and compensate the reconstruction template. The steps are as follows:

[0067] Step 1: Calculate the moment of inertia tensor of the healthy radius and ulna of the reconstruction template. The principal axis of inertia determined by the moment of inertia tensor is the long axis direction of the bone and passes through the center of mass of the radius and ulna;

[0068] Step 2: The anatomical regions of the proximal radius, proximal ulna, and distal ulna in the forearm are determined by the 20% region along the long axis of the bone. Seven clinical anatomical points are selected at the distal end of the radius. With each clinical anatomical point as the center, the nearest neighbor search algorithm is used to search for 50 points around it as the anatomical region of the distal radius.

[0069] Step 3: In the alignment of the anatomical regions of the forearm bone joints, the proximal radius, proximal ulna and distal ulna can be aligned using the joint region as a whole. Since the distal radius is connected to the wrist joint and the distal ulna joint, the alignment of its articular surface is more important than the alignment of the long bone region. Different weights are set for the anatomical regions determined by the seven anatomical points of the distal radius. The alignment weights of the 5 anatomical regions on the articular surface are set to 0.18, and the alignment weights of the 2 anatomical regions in the long bone region are set to 0.05. The alignment effect is shown below. Figure 3 As shown, the proximal radius registration matrix M is calculated rp , distal radius registration matrix M rd , the proximal ulna registration matrix M up , distal ulna registration matrix M ud ;

[0070] Step 4: Calculate the radius reduction matrix Mr = M rp -1 *M rd , ulnar reduction matrix M up-1 *M ud , according to the displacement component in the ulnar reduction matrix, the difference ΔZ between the two radii along the long axis of the healthy ulna is calculated Ulna =2.1mm, according to the difference formula of ulna and radius of forearm on both sides ΔZ Radius =0.98*ΔZ Ulna , calculate the amount ΔZ that the deformed radial bone reconstruction template needs to scale along its long axis Radius =2.06 mm, and the radial reconstruction template was scaled;

[0071] After constructing the deformed radius reconstruction template that compensates for the difference, 20% of the point sets at the distal and proximal ends of the deformed radius were selected along the long axis of the deformed radius reconstruction template bone, and 20% of the point sets at the distal and proximal ends of the reconstruction template were reconstructed. The distal and proximal ends were registered respectively, and the registered bone model was discretely divided with a step size of 0.1 mm. The root mean square error between the deformed bone and the reconstruction template in each step size was calculated. The deformity threshold was set at 15% when the root mean square error exceeded the minimum value, and the deformity range of the deformed radius was determined.

[0072] After the deformity range of the deformed radius is calculated, the comprehensive registration matrix M is calculated for the deformed radius and the reconstruction template using the same method as in Step 3. RP , extract the transformation matrix M RP The rotation axis direction vector RV = (0.2, 0.4, 0.18).

[0073] Determining the contour curve equation of the deformity contour projected on the XOZ plane is the last step in bone deformity diagnosis. For the radial deformity area in this example, the steps are as follows:

[0074] Step 1: Calculate the transformation matrix MY from the vector RV to coincide with the Y axis, perform the same transformation on the deformed radius and the reconstruction template in the proximal registration, and project the deformed area onto the XOZ plane, as shown in Figure 4 As shown, curve 1 and curve 2 are two contour lines;

[0075] Step 2: Use cubic spline function interpolation to calculate the equation of curve 1 Curve 2 equation

[0076] After the deformity area is determined, the range of a cutting plane can be roughly determined. However, the specific location of the wedge osteotomy to minimize the radius reduction error requires optimization calculation of the osteotomy surface position and reduction angle. The optimization process in the present invention is based on the quasi-Newton method in the traditional nonlinear optimization method, such as Figure 5 As shown, the following steps are included:

[0077] B1. Convert the minimum clinical target resetting error into mathematical expression, establish a mathematical model, and determine the optimization target and variable parameters;

[0078] B2. Use the quasi-Newton method to process the optimization objective and calculate the variable parameters that minimize the optimization objective.

[0079] The detailed instructions are as follows:

[0080] The optimization model was established based on the determined deformity range of 78.6-85.7 mm along the Z axis of the world coordinate system, the reduction angle range of 0-360°, and the reduction target of the minimum root mean square error of the anatomical area of ​​the distal radial joint.

[0081]

[0082] The BFGS algorithm in the quasi-Newton method is used in the intelligent planning system, and the allowable error is set to 1mm. It is used for iterative optimization calculation. The final calculated reduction target error in this example is 0.54mm, the Z coordinate of the osteotomy position is 80.4, and the reduction angle is 14.2°. Figure 6 shown.

[0083] Embodiment 3

[0084] An intelligent planning system for forearm angular deformity, comprising:

[0085] Model space transformation module: used to adjust the spatial position of the skeleton model before intelligent planning;

[0086] Model registration module: by manually selecting the anatomical position or automatically calculating the anatomical position, the bone model is registered using an iterative closest point algorithm based on different weights of anatomical regions;

[0087] Automatic bone deformity diagnosis module: Based on anatomical region registration and the set deformity threshold, the bone deformity area of ​​the deformed bone in the deformed forearm is automatically calculated

[0088] Bone model cutting module: You can customize the osteotomy surface to cut the bone model;

[0089] Angular deformity single-blade osteotomy correction optimization module: The osteotomy range constraint is set according to the bone deformity range, and the quasi-Newton method is used to solve the osteotomy position and reduction angle that minimizes the alignment error of the joint anatomical area, forming an automatic pre-operative planning plan for single-blade osteotomy to correct forearm angular deformity.

[0090] Working principle: Use the registration method based on the joint anatomical area to calculate the difference between the deformed forearm and the healthy contralateral side, build a reconstruction template for the deformed bone, and calculate the deformed area of ​​the deformed bone; use the deformed area as the osteotomy range constraint to calculate the optimal osteotomy position and reduction angle for the reduction target, and automatically generate the solution required for the clinical goal.

[0091] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0092] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. An intelligent planning method for forearm angular deformity, Features: The following steps are involved: Step 1: Establish a mirror image model of the healthy bone on the opposite side of the deformed forearm bone to make it a reconstruction template for correction and reduction; Step 2: Set the joint anatomical region for the radius and ulna of the forearm bones, and perform registration, and calculate the displacement along the long axis of the bone in the transformation matrix according to the distal and proximal comprehensive registration matrices; Step 3: taking the displacement as the difference between the two sides of the deformed bone in the deformed forearm, compensating for the difference in the reconstruction template, and constructing a new reconstruction template; Step 4: Calculate the deformed area and reduction rotation axis of the deformed forearm bone; Step 5: Obtain the function expression of the deformity contour curve within the range of bone deformity; Step 6: Use any point on the contour curve as the rotation axis position of the single-blade wedge osteotomy; Step 7: Use the quasi-Newton method to calculate the osteotomy position and reduction angle of the forearm angular deformity to obtain the optimal osteotomy correction reduction target.

2. According to claim 1, a method for intelligent planning of forearm angular deformity, Features: In step 2, the joint anatomical region of the radius and ulna of the forearm bone is composed of 7 anatomical regions defined by 7 clinical anatomical feature points.

3. The intelligent planning method for forearm angular deformity according to claim 1, Features: In step 3, the method for compensating for the difference in the reconstruction template includes: (1) Calculate the distal radius joint registration matrix, proximal radius joint registration matrix, distal ulna registration matrix, and proximal ulna registration matrix of the deformed forearm; (2) calculating the displacement of the radius along the long axis of the reconstructed template bone during the registration process, and calculating the displacement of the ulna along the long axis of the reconstructed template bone during the registration process; (3) Based on the relationship between the difference between the radius and ulna of the bilateral forearms, the deformed radius or ulna is scaled along the long axis of the bone to compensate for the difference in the reconstruction template.

4. The intelligent planning method for forearm angular deformity according to claim 1, Features: In the step 5, the deformity contour curve function in the bone deformity range is obtained in a two-dimensional plane, and the correction rotation axis of the single-blade wedge osteotomy is perpendicular to the deformity contour curve in the two-dimensional plane.

5. The intelligent planning method for forearm angular deformity according to claim 1, Features: In step 7, the method for calculating the osteotomy position and reduction angle of the forearm angular deformity includes: (1) The osteotomy position and reduction angle were used as control variables, the minimum root mean square error of the alignment of the distal anatomical region of the deformed bone joint was used as the optimization goal, and the bone deformity region was used as the range constraint of the osteotomy position; (2) Establish a nonlinear continuous function model between the optimization objective and the control variable, use the BFGS quasi-Newton method to iteratively optimize the function model, and find the minimum value of the optimization objective under the constraints; (3) The optimal osteotomy position and reduction angle are used as the preoperative planning scheme for osteotomy correction. The deformed bone is osteotomized to simulate single-blade wedge osteotomy correction. The distal bone segment of the deformed bone is rotated around the rotation axis to calculate the reduction angle for correction.

6. A system based on the intelligent planning method for forearm angular deformity according to claim 1, Features: The system comprises Model space transformation module: used to adjust the spatial position of the skeleton model before intelligent planning; Model registration module: by manually selecting the anatomical position or automatically calculating the anatomical position, the bone model is registered using an iterative closest point algorithm based on different weights of anatomical regions; Automatic bone deformity diagnosis module: Based on anatomical region registration and according to the set deformity threshold, the bone deformity area of ​​the deformed bone in the deformed forearm is automatically calculated; Bone model cutting module: You can customize the osteotomy surface to cut the bone model; Angular deformity single-blade osteotomy correction optimization module: The osteotomy range constraint is set according to the bone deformity range, and the quasi-Newton method is used to solve the osteotomy position and reduction angle that minimizes the alignment error of the joint anatomical area, forming an automatic pre-operative planning plan for single-blade osteotomy to correct forearm angular deformity.

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