An intelligent learning diagnosis and treatment system for intelligent analysis of orthopedic images

Abstract

The present invention belongs to the technical field of medical intelligent imaging. The present invention provides an intelligent learning diagnosis and treatment system for intelligent analysis of orthopedic images. The system performs image detection on a bone fracture area of ​​a rehabilitation patient to obtain an image of the bone fracture area of ​​the rehabilitation patient. The image of the bone fracture area of ​​the rehabilitation patient is overlapped and compared with an image of a normal rehabilitation bone area to obtain image comparison data. The image comparison data is analyzed to obtain a bone overlap value. Based on the bone overlap value, the bone overlap value is compared with a bone overlap threshold to generate a bone rehabilitation effect signal. If the bone overlap value is greater than or equal to the bone overlap threshold, it indicates that the patient's bone position is greatly offset, and a bone rehabilitation effect poor signal is generated, thereby reducing the influence of subjective judgment and improving the accuracy and reliability of the evaluation.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and in particular to an intelligent learning and diagnosis system for intelligent analysis of orthopedic images. Background Art

[0002] Medical imaging is an important branch of medicine. It uses a variety of imaging technologies such as X-rays, CT, MRI, and ultrasound to observe the internal structure of the human body non-invasively, providing an important basis for the diagnosis of diseases. In the field of orthopedics, imaging examinations are an important means of evaluating fractures, joint lesions, bone tumors and other diseases.

[0003] The Chinese invention patent with publication number CN111292821A discloses a medical diagnosis system, including a diagnosis and treatment data integration module, which is used to obtain diagnosis and treatment data related to patients from one or more medical information sources and integrate the diagnosis and treatment data; the diagnosis and treatment data at least includes medical image data and medical text data; an artificial intelligence image analysis module, which is used to analyze the medical image data based on artificial intelligence image analysis technology and generate an image analysis report; an artificial intelligence text analysis module, which is used to extract information from the medical text data and / or the image analysis report based on artificial intelligence information extraction technology to obtain structured diagnosis and treatment data related to the disease.

[0004] However, there is no evaluation of the rehabilitation status of fracture rehabilitation patients, the rehabilitation effect of fracture rehabilitation patients is not obtained, and the bone displacement area data is obtained based on the difference signal of the rehabilitation effect of fracture rehabilitation patients, and the fracture end stability value is analyzed to determine the rehabilitation stability of fracture rehabilitation patients. If an unstable rehabilitation signal is obtained, it is also necessary to obtain the bone displacement area value to determine the key rehabilitation area of ​​the patient's fracture, so as to accurately locate the key rehabilitation area of ​​the patient's fracture. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve the technical problem is: the present invention provides an intelligent learning diagnosis and treatment system for intelligent analysis of orthopedic images, the system comprising the following modules:

[0007] Bone image analysis module: perform image detection on the bone fracture area of ​​the rehabilitation patient to obtain the bone fracture area image of the rehabilitation patient, overlap and compare the bone fracture area image of the rehabilitation patient with the normal rehabilitation bone area image to obtain image comparison data, and analyze the image comparison data to obtain the bone overlap value;

[0008] Rehabilitation effect evaluation module: based on the bone coincidence value, the bone coincidence value is compared with the bone coincidence threshold to generate a bone rehabilitation effect signal;

[0009] If the bone coincidence value is greater than or equal to the bone coincidence threshold, a bone rehabilitation effect poor signal is generated;

[0010] Fracture end stability analysis module: Based on the bone rehabilitation effect difference signal, the bone displacement area data is obtained, the fracture end stability value is analyzed, the fracture end stability value is compared with the fracture end stability threshold, and a rehabilitation stability signal is generated;

[0011] Key rehabilitation area determination module: Based on the rehabilitation instability signal, the bone offset area value is obtained, all the bone offset area values ​​are compared, the maximum value of the bone offset area is extracted, and the bone offset area corresponding to the maximum value of the bone offset area is marked as the patient's fracture key review area, thereby completing the determination of the patient's fracture key review area.

[0012] As a further solution of the present invention: the process of obtaining the bone coincidence value is as follows:

[0013] Substitute the numerical value of the bone offset and the area occupied by the bone offset into the formula: C = (Pg + Pv) × α, and calculate the bone coincidence value C, where Pg represents the numerical value of the bone offset, Pv represents the area occupied by the bone offset, and α represents the preset proportional coefficient.

[0014] As a further solution of the present invention: the method for obtaining the number of bone offset values ​​is:

[0015] Obtain the number of bone offset regions, calculate the ratio of the number of bone offset regions to the number of normal bone image regions, and obtain a bone offset value;

[0016] The area occupied by the bone offset is obtained as follows:

[0017] The area occupied by the bone offset region is obtained, the areas occupied by the bone offset regions are added and summed to obtain the area value of the bone offset region, and the ratio of the area value of the bone offset region to the area of ​​the normal bone image region is calculated to obtain the area value occupied by the bone offset.

[0018] As a further solution of the present invention: the bone offset region is obtained in the following manner:

[0019] Based on the bone fracture area image of the rehabilitation patient, the bone fracture area image of the rehabilitation patient is divided into several areas, marked as fracture rehabilitation areas, and the center position of the patient's bone in each fracture rehabilitation area is obtained and marked as the center point of the fracture rehabilitation area. Based on the normal rehabilitation bone area image, the normal rehabilitation bone area image is divided into several areas, marked as normal bone areas, and the center position of the normal bone in each normal bone area is obtained and marked as the center point of the normal bone area. The normal rehabilitation bone area image and the bone fracture area image of the rehabilitation patient are overlapped and paired, and the center point of the fracture rehabilitation area and the center point of the normal bone area in the same position area are connected to obtain the center distance value, and the center distance value is compared with the center distance threshold. The comparison process is as follows:

[0020] If the center distance value is greater than or equal to the center distance threshold, a bone offset signal is generated, and the fracture rehabilitation area corresponding to the bone offset signal is marked as a bone offset area.

[0021] As a further solution of the present invention: the method for obtaining the stability value of the fracture end is:

[0022] Obtain the displacement degree value of the fracture end, substitute the displacement degree value of the fracture end into the formula L=Yw×β, and calculate the fracture end stability value L, where Yw represents the displacement degree value of the fracture end, and β represents the preset proportional coefficient.

[0023] As a further solution of the present invention: the process of obtaining the fracture end displacement value is as follows:

[0024] The fracture end movement angle value and the fracture end movement distance value are obtained, and the fracture end movement angle value and the fracture end movement distance value are added together to obtain the fracture end displacement degree value.

[0025] As a further solution of the present invention: the method for obtaining the fracture end movement angle value is:

[0026] All the tangent values ​​of the offset angles are added together and averaged to obtain the fracture end movement angle value.

[0027] As a further solution of the present invention: the method for obtaining the offset angle tangent value is:

[0028] Based on the bone offset area and the normal bone area, the center point of the fracture end in the bone offset area and the center point of the longitudinal axis of the backbone in the normal bone area image are obtained, the center point of the fracture end in the bone offset area and the center point of the longitudinal axis of the backbone in the normal bone area image are connected by a straight line to obtain a center point line, and the center point line is extended to form an offset angle with the longitudinal axis of the backbone in the normal bone area image to obtain the tangent value of the offset angle.

[0029] As a further solution of the present invention: the method for obtaining the fracture end movement distance value is:

[0030] The length of the center point connection line is obtained, and the center point connection line length values ​​are added and averaged to obtain the fracture end movement distance value.

[0031] As a further solution of the present invention: the bone offset region value is obtained as follows:

[0032] Based on the fracture end stability value, the fracture end stability value is calculated by ratio with the fracture end stability threshold to obtain the fracture end stability coefficient;

[0033] Based on the center spacing value, the center spacing value is calculated by ratio with the center spacing threshold to obtain the center spacing coefficient;

[0034] The fracture end stability coefficient and the center spacing coefficient are obtained, and the fracture end stability coefficient and the center spacing coefficient corresponding to the bone offset area are added to obtain the bone offset area value.

[0035] The beneficial effects of the present invention are as follows:

[0036] 1. The present invention performs image detection on the bone fracture area of ​​the rehabilitation patient to obtain the bone fracture area image of the rehabilitation patient, overlaps and compares the bone fracture area image of the rehabilitation patient with the normal rehabilitation bone area image to obtain image comparison data, and analyzes the image comparison data to obtain a bone overlap value, based on the bone overlap value, compares the bone overlap value with a bone overlap threshold to generate a bone rehabilitation effect signal; if the bone overlap value is greater than or equal to the bone overlap threshold, it means that the patient's bone position is greatly offset, and a bone rehabilitation effect poor signal is generated, thereby reducing the influence of subjective judgment and improving the accuracy and reliability of the evaluation.

[0037] 2. The present invention obtains bone displacement area data based on the bone rehabilitation effect difference signal, analyzes and obtains the fracture end stability value, compares the fracture end stability value with the fracture end stability threshold, and generates a rehabilitation stability signal. If the fracture end stability value is greater than or equal to the fracture end stability threshold, it means that the fracture end is displaced to a large extent, and the area occupied by the fracture end fracture fragments is large and the distribution is relatively concentrated, and a rehabilitation instability signal is generated. Based on the rehabilitation instability signal, the bone displacement area value is obtained to determine the patient's fracture key rehabilitation area. The patient's fracture key rehabilitation area can be accurately located, which helps doctors to formulate more targeted rehabilitation plans and improve rehabilitation effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below in conjunction with the accompanying drawings.

[0039] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0040] Figure 2 is a flowchart of Embodiment 2 of the present invention;

[0041] Figure 3 It is a flowchart of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0042] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0043] Example 1

[0044] like Figure 1 As shown, the data analysis method based on the tree structure dimension according to the embodiment of the present invention includes:

[0045] See also Figure 1 , Figure 2 As shown, an intelligent learning diagnosis and treatment system for intelligent analysis of orthopedic images according to an embodiment of the present invention includes the following modules:

[0046] Bone image analysis module: perform image detection on the bone fracture area of ​​the rehabilitation patient to obtain the bone fracture area image of the rehabilitation patient, overlap and compare the bone fracture image of the rehabilitation patient with the normal rehabilitation bone image to obtain image comparison data, and analyze the image comparison data to obtain the bone overlap value;

[0047] In some embodiments, an image of a bone fracture area of ​​a rehabilitation patient is detected by an X-ray machine to obtain an image of the bone fracture area of ​​the rehabilitation patient, and the image of the bone fracture area of ​​the rehabilitation patient is overlapped and compared with an image of a normal rehabilitation bone area to obtain image comparison data, and the image comparison data is analyzed, wherein the image comparison data includes a numerical value of the offset bone and an area value occupied by the offset bone, and a bone overlap value is obtained, wherein the process of obtaining the bone overlap value is as follows:

[0048] Based on the bone fracture area image of the rehabilitation patient, the bone fracture area image of the rehabilitation patient is divided into several areas, marked as fracture rehabilitation areas, and the center position of the patient's bone in each fracture rehabilitation area is obtained and marked as the center point of the fracture rehabilitation area. Based on the normal rehabilitation bone area image, the normal rehabilitation bone area image is divided into several areas, marked as normal bone areas, and the center position of the normal bone in each normal bone area is obtained and marked as the center point of the normal bone area. The normal rehabilitation bone area image and the bone fracture area image of the rehabilitation patient are overlapped and paired, and the center point of the fracture rehabilitation area and the center point of the normal bone area in the same position area are connected to obtain the center distance value, and the center distance value is compared with the center distance threshold. The comparison process is as follows:

[0049] If the center distance value is greater than or equal to the center distance threshold, it means that the patient's bone fracture position has a low degree of overlap with the normal bone position, a bone offset signal is generated, and the patient's bone area corresponding to the bone offset signal is marked as a bone offset area;

[0050] If the center distance value is less than the center distance threshold, it means that the patient's bone fracture position has a high degree of overlap with the normal bone position, a bone non-deviated signal is generated, and the patient's bone area corresponding to the bone deviation signal is marked as a bone non-deviated area;

[0051] Obtain the number of bone offset regions, calculate the ratio of the number of bone offset regions to the number of normal bone image regions, and obtain a bone offset value;

[0052] Obtain the area occupied by the bone offset region, add the areas occupied by the bone offset region to obtain the area value of the bone offset region, and calculate the ratio of the area value of the bone offset region to the area of ​​the normal bone image region to obtain the area value of the bone offset;

[0053] Substitute the number of bone offsets and the area occupied by the bone offsets into the formula: C = (Pg + Pv) × α, and calculate the bone coincidence value C, where Pg represents the number of bone offsets, Pv represents the area occupied by the bone offsets, and α represents the preset proportional coefficient;

[0054] Rehabilitation effect evaluation module: based on the bone coincidence value, the bone coincidence value is compared with the bone coincidence threshold to generate a bone rehabilitation effect signal;

[0055] Among them, the bone rehabilitation effect signal includes a good bone rehabilitation effect signal and a poor bone rehabilitation effect signal;

[0056] In some embodiments, based on the acquired bone coincidence value, the bone coincidence value is compared with the bone coincidence threshold value, and the comparison process is as follows:

[0057] If the bone coincidence value is greater than or equal to the bone coincidence threshold, it means that the patient's bone position is greatly offset, and a signal of poor bone rehabilitation effect is generated;

[0058] If the bone coincidence value is less than the bone coincidence threshold, it means that the patient's bone position deviation is small, generating a signal of good bone rehabilitation effect;

[0059] The technical solution of the embodiment of the present invention is mainly as follows: performing image detection on the bone fracture area of ​​the rehabilitation patient to obtain the image of the bone fracture area of ​​the rehabilitation patient, overlapping and comparing the bone fracture image of the rehabilitation patient with the normal rehabilitation bone image to obtain image comparison data, and analyzing the image comparison data to obtain a bone coincidence value, based on the bone coincidence value, comparing the bone coincidence value with the bone coincidence threshold to generate a bone rehabilitation effect signal; if the bone coincidence value is greater than or equal to the bone coincidence threshold, it means that the patient's bone position is greatly offset, and a bone rehabilitation effect poor signal is generated, which reduces the influence of subjective judgment and improves the accuracy and reliability of the evaluation.

[0060] Embodiment 2:

[0061] Based on Example 1, please refer to Figure 3 As shown, an integrated power supply system with good voltage and current stabilization effects according to an embodiment of the present invention includes the following modules:

[0062] Fracture end stability analysis module: Based on the low bone coincidence signal, the bone offset area data is obtained, and the fracture end stability value is analyzed and compared with the fracture end stability threshold to generate a signal of whether the rehabilitation is stable;

[0063] The signal of whether the rehabilitation is stable includes a signal of stable rehabilitation and a signal of unstable rehabilitation;

[0064] In some embodiments, when the bone coincidence signal is low, the bone offset region data is obtained, wherein the bone offset region data includes the fracture end displacement degree value and the fracture end morphology value, the fracture end displacement degree value and the fracture end morphology value are added and summed to obtain the fracture end stability value, and the fracture end stability value is compared with the fracture end stability threshold, and the comparison process is as follows:

[0065] If the fracture end stability value is greater than or equal to the fracture end stability threshold, it means that the fracture end is displaced to a greater extent, and the fracture end fracture fragments occupy a larger area and are distributed more concentratedly, generating a rehabilitation instability signal;

[0066] If the fracture end stability value is greater than or equal to the fracture end stability threshold, it means that the displacement of the fracture end is small, and the area occupied by the fracture end fracture fragments is small and the distribution is more dispersed, generating a rehabilitation instability signal;

[0067] The method for obtaining the stability value of the fracture end is as follows:

[0068] Obtain the displacement degree value and the morphology value of the fracture end, substitute the displacement degree value and the morphology value of the fracture end into the formula L=Yw×β+Xt×θ, and calculate the stability value L of the fracture end, where Yw represents the displacement degree value of the fracture end, Xt represents the morphology value of the fracture end, and β and θ represent the preset proportional coefficients;

[0069] Furthermore, the fracture end stability value means whether the relative position relationship of the fracture ends is stable after fracture, that is, whether it is easy to be displaced or whether it is easy to be displaced again after reduction;

[0070] Exemplarily, the process of obtaining the fracture end displacement value is as follows:

[0071] Obtain the fracture end movement angle value and the fracture end movement distance value, add the fracture end movement angle value and the fracture end movement distance value to obtain the fracture end displacement degree value;

[0072] It should be further explained that the displacement degree of the fracture end means the angular displacement and distance displacement between the fracture end and the longitudinal axis of the bone after the fracture.

[0073] First, the method for obtaining the fracture end movement angle value is as follows:

[0074] Based on the bone offset region and the normal bone region, the center point of the fracture end in the bone offset region and the center point of the longitudinal axis of the backbone in the normal bone region image are obtained, the center point of the fracture end in the bone offset region and the center point of the longitudinal axis of the backbone in the normal bone region image are connected by a straight line to obtain a center point connection line, and the center point connection line is extended to form an offset angle with the longitudinal axis of the backbone in the normal bone region image to obtain the tangent value of the offset angle;

[0075] All the tangent values ​​of the offset angles are added and averaged to obtain the displacement angle value of the fracture end;

[0076] It should be further explained that the meaning of the tangent value of the offset angle is to evaluate the rehabilitation status of the fracture end direction in each fracture rehabilitation area;

[0077] The second specific method for obtaining the fracture end movement distance value is:

[0078] Based on the center point connection line obtained above, the length value of the center point connection line is obtained, and the center point connection line length values ​​are added and averaged to obtain the fracture end movement distance value;

[0079] Key rehabilitation area determination module: Based on the rehabilitation instability signal, the bone displacement area value is obtained to determine the patient's fracture key rehabilitation area;

[0080] In some embodiments, when a rehabilitation instability signal is generated, a bone offset region value is obtained, all bone offset region values ​​are compared, a maximum value of the bone offset region is extracted, and a bone offset region corresponding to the maximum value of the bone offset region is marked as a key review region for the patient's fracture, thereby completing the work of determining the key review region for the patient's fracture;

[0081] The bone offset area value is obtained as follows:

[0082] Based on the fracture end stability value, the fracture end stability value is calculated by ratio with the fracture end stability threshold to obtain the fracture end stability coefficient;

[0083] Based on the center spacing value, the center spacing value is calculated by ratio with the center spacing threshold to obtain the center spacing coefficient;

[0084] Obtain the fracture end stability coefficient and the center spacing coefficient, add the fracture end stability coefficient and the center spacing coefficient corresponding to the bone offset area to obtain the bone offset area value;

[0085] The technical solution of the embodiment of the present invention is mainly as follows: based on the low signal of bone coincidence, the bone displacement area data is obtained, the fracture end stability value is analyzed, the fracture end stability value is compared with the fracture end stability threshold, and a rehabilitation stability signal is generated. If the fracture end stability value is greater than or equal to the fracture end stability threshold, it means that the degree of displacement of the fracture end is large, and the area occupied by the fracture end fracture fragments is large and the distribution is relatively concentrated, and a rehabilitation instability signal is generated. Based on the rehabilitation instability signal, the bone displacement area value is obtained, and the patient's fracture focus rehabilitation area is determined, which can accurately locate the patient's fracture focus rehabilitation area. This helps doctors formulate more targeted rehabilitation plans and improve rehabilitation effects.

[0086] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent learning diagnosis and treatment system for intelligent analysis of orthopedic images, characterized in that: The system includes the following modules: Bone image analysis module: perform image detection on the bone fracture area of ​​the rehabilitation patient to obtain the bone fracture area image of the rehabilitation patient, overlap and compare the bone fracture area image of the rehabilitation patient with the normal rehabilitation bone area image to obtain image comparison data, and analyze the image comparison data to obtain the bone overlap value; The bone offset area is obtained as follows: Based on the bone fracture area image of the rehabilitation patient, the bone fracture area image of the rehabilitation patient is divided into several areas, marked as fracture rehabilitation areas, and the center position of the patient's bone in each fracture rehabilitation area is obtained and marked as the center point of the fracture rehabilitation area. Based on the normal rehabilitation bone area image, the normal rehabilitation bone area image is divided into several areas, marked as normal bone areas, and the center position of the normal bone in each normal bone area is obtained and marked as the center point of the normal bone area. The normal rehabilitation bone area image and the bone fracture area image of the rehabilitation patient are overlapped and paired, and the center point of the fracture rehabilitation area and the center point of the normal bone area in the same position area are connected to obtain the center distance value, and the center distance value is compared with the center distance threshold. The comparison process is as follows: If the center distance value is greater than or equal to the center distance threshold, a bone offset signal is generated, and the fracture rehabilitation area corresponding to the bone offset signal is marked as a bone offset area; The method to obtain the bone offset value is: Obtain the number of bone offset regions, calculate the ratio of the number of bone offset regions to the number of normal bone image regions, and obtain a bone offset value; The area occupied by the bone offset is obtained as follows: Obtain the area occupied by the bone offset region, add the areas occupied by the bone offset region to obtain the area value of the bone offset region, and calculate the ratio of the area value of the bone offset region to the area of ​​the normal bone image region to obtain the area value of the bone offset; The process of obtaining the bone coincidence value is as follows: Substitute the number of bone offsets and the area occupied by the bone offsets into the formula: C = (Pg + Pv) × α, and calculate the bone coincidence value C, where Pg represents the number of bone offsets, Pv represents the area occupied by the bone offsets, and α represents the preset proportional coefficient; Rehabilitation effect evaluation module: based on the bone coincidence value, the bone coincidence value is compared with the bone coincidence threshold to generate a bone rehabilitation effect signal; If the bone coincidence value is greater than or equal to the bone coincidence threshold, a bone rehabilitation effect poor signal is generated; Fracture end stability analysis module: Based on the bone rehabilitation effect difference signal, the bone displacement area data is obtained, the fracture end stability value is analyzed, the fracture end stability value is compared with the fracture end stability threshold, and a rehabilitation stability signal is generated; Key rehabilitation area determination module: based on the rehabilitation instability signal, obtain the bone offset area value, compare all the bone offset area values, extract the maximum value of the bone offset area, mark the bone offset area corresponding to the maximum value of the bone offset area as the patient's fracture key review area, and complete the determination of the patient's fracture key review area; The bone offset area value is obtained as follows: Based on the fracture end stability value, the fracture end stability value is calculated by ratio with the fracture end stability threshold to obtain the fracture end stability coefficient; Based on the center spacing value, the center spacing value is calculated by ratio with the center spacing threshold to obtain the center spacing coefficient; The fracture end stability coefficient and the center spacing coefficient are obtained, and the fracture end stability coefficient and the center spacing coefficient corresponding to the bone offset area are added to obtain the bone offset area value.

2. The intelligent learning and diagnosis system for orthopedic image intelligent analysis according to claim 1, characterized in that: The method for obtaining the stability value of the fracture end is: Obtain the displacement degree value of the fracture end, substitute the displacement degree value of the fracture end into the formula L=Yw×β, and calculate the fracture end stability value L, where Yw represents the displacement degree value of the fracture end, and β represents the preset proportional coefficient.

3. The intelligent learning and diagnosis system for orthopedic image intelligent analysis according to claim 2 is characterized by: The process of obtaining the fracture end displacement degree value is as follows: The fracture end movement angle value and the fracture end movement distance value are obtained, and the fracture end movement angle value and the fracture end movement distance value are added together to obtain the fracture end displacement degree value.

4. The intelligent learning and diagnosis system for orthopedic image intelligent analysis according to claim 3 is characterized by: The method for obtaining the fracture end movement angle value is as follows: All the tangent values ​​of the offset angles are added together and averaged to obtain the fracture end movement angle value.

5. The intelligent learning and diagnosis system for orthopedic image intelligent analysis according to claim 4 is characterized by: The method for obtaining the offset angle tangent value is as follows: Based on the bone offset area and the normal bone area, the center point of the fracture end in the bone offset area and the center point of the longitudinal axis of the backbone in the normal bone area image are obtained, the center point of the fracture end in the bone offset area and the center point of the longitudinal axis of the backbone in the normal bone area image are connected by a straight line to obtain a center point line, and the center point line is extended to form an offset angle with the longitudinal axis of the backbone in the normal bone area image to obtain the tangent value of the offset angle.

6. The intelligent learning and diagnosis system for orthopedic image intelligent analysis according to claim 5 is characterized by: The method for obtaining the fracture end movement distance value is as follows: The length of the center point connection line is obtained, and the center point connection line length values ​​are added and averaged to obtain the movement distance value of the fracture end.

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