Knee osteoarthritis morbidity state detection method and system

Abstract

The invention relates to the technical field of orthopedic disease diagnosis, in particular to a knee osteoarthritis morbidity state detection method and system. By combining multiple groups of indexes and image group feature data, orthopedic diseases, especially knee osteoarthritis, can be effectively evaluated, score data reflecting the state of the knee osteoarthritis can be obtained, and the incidence probability and the staging result degree of the knee osteoarthritis can be more intuitively determined through the score data. Therefore, the overall automatic prediction of the attack of knee osteoarthritis is realized.

Description

Technical Field

[0001] The present application relates to the technical field of orthopedic disease diagnosis, and specifically to a method and system for detecting the pathogenesis of knee osteoarthritis. Background Art

[0002] Knee osteoarthritis (KOA) is one of the most common causes of chronic disability and a major public health issue. Clinically, KOA presents with knee pain, stiffness, and limited mobility, with a significant risk of long-term disability. The pathogenesis of KOA, a disease affecting the entire knee joint, remains incompletely elucidated. The involvement of the femoral and tibial subchondral bone in the pathogenesis of KOA is gaining increasing attention.

[0003] Local or systemic molecular biomarkers can help identify patients at increased risk for developing knee osteoarthritis. Molecular biomarkers can detect knee osteoarthritis before severe joint damage occurs, providing accurate early diagnosis or predicting disease progression. However, most molecular biomarkers lack sensitivity and specificity, and further research is needed. Summary of the Invention

[0004] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide an auxiliary diagnosis method and system for the diagnosis of orthopedic diseases by combining computer medical images and multiple clinical indicators. By extracting imaging group features from images, the extracted features and clinical data are automatically evaluated based on a predictive model.

[0005] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0006] In a first aspect, a method for detecting the onset status of knee osteoarthritis is provided, the method comprising: obtaining clinical data of a patient to be diagnosed and imaging group characteristic data of a target area of ​​the patient to be diagnosed; the imaging group characteristic data comprises MRI image characteristic data, and the clinical data comprises hypertension status, lower limb deep vein valve function index, platelet-to-lymphocyte ratio, and D-dimer data; inputting the clinical data and the imaging group characteristic data into a status evaluation model to calculate a status value; the status evaluation model is a nomogram model.

[0007] Furthermore, the image group feature data includes multiple shape features, multiple first-order features, multiple gray-level co-occurrence matrix features, multiple gray-level run-length matrix features, and multiple gray-level size area matrix features.

[0008] Furthermore, the plurality of shape features include the maximum 2D diameter of the original sequence, the second largest axis length of the original sequence, and the surface area of ​​the original sequence.

[0009] Furthermore, the first-order features include the first-order root mean square of the original sequence, the first-order interquartile range of the LHL sequence, the first-order mean absolute deviation of the LHH sequence, the first-order mean of the LHH sequence, and the first-order root mean square of the LHH sequence.

[0010] Furthermore, the gray level co-occurrence matrix features include normalization of the original sequence gray level co-occurrence matrix, difference entropy of the original sequence gray level co-occurrence matrix, related information measure 1 of the LHL sequence gray level co-occurrence matrix, and joint average of the LHL sequence gray level co-occurrence matrix.

[0011] Furthermore, the grayscale run length matrix features include low grayscale run emphasis of the original sequence grayscale run length matrix and grayscale run length matrix run percentage.

[0012] Furthermore, the grayscale size region matrix features include normalized region size unevenness of the HHH sequence grayscale size region features and region size unevenness of the original sequence grayscale size region features.

[0013] Furthermore, the clinical data and the imaging group characteristic data are input into the state evaluation model to calculate the state value, including: obtaining the first score, second score, third score, fourth score and fifth score corresponding to the hypertension state, lower limb deep vein valve function index, platelet-to-lymphocyte ratio, D-dimer data and the imaging group characteristic data based on the nomogram model, as well as the corresponding total score.

[0014] Furthermore, the fifth score corresponding to the characteristic data of the image group is obtained, including: obtaining the sub-scores corresponding to the maximum 2D diameter of the original sequence, the second largest axis length of the original sequence, the surface area of ​​the original sequence, the first-order root mean square of the original sequence, the first-order interquartile range of the LHL sequence, the first-order mean absolute deviation of the LHH sequence, the first-order mean of the LHH sequence, the first-order root mean square of the LHH sequence, the normalization of the grayscale co-occurrence matrix of the original sequence, the difference entropy of the grayscale co-occurrence matrix of the original sequence, the related information measure 1 of the grayscale co-occurrence matrix of the LHL sequence, the joint average of the grayscale co-occurrence matrix of the LHL sequence, the low grayscale run emphasis of the grayscale run length matrix of the original sequence, the run percentage of the grayscale run length matrix, the normalized regional size unevenness of the grayscale size area feature of the HHH sequence, and the regional size unevenness of the grayscale size area feature of the original sequence, and updating the sub-scores according to the calculated weights corresponding to the above data and weighting them to obtain the fifth score.

[0015] In a second aspect, a knee osteoarthritis disease status detection system is provided, the system comprising: a data acquisition unit for acquiring clinical data of a patient to be diagnosed and imaging group characteristic data of a target area of ​​the patient to be diagnosed; a risk prediction unit for inputting the clinical data and the imaging group characteristic data into a status evaluation model to calculate a status value; the status evaluation model is a nomogram model.

[0016] In the technical solution provided in the embodiment of the present application, by combining multiple groups of indicators and imaging group feature data, orthopedic diseases, especially knee osteoarthritis, can be effectively evaluated, and score data reflecting the status of knee osteoarthritis can be obtained. The probability of occurrence of knee osteoarthritis and the degree of staging results can be determined more intuitively through this score data, thereby realizing an overall automated prediction of the incidence of knee osteoarthritis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] The methods, systems, and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numerals represent similar structures in the various views of the drawings.

[0019] Figure 1 This is a flow chart of the method for predicting the onset of knee osteoarthritis provided in an embodiment of the present application.

[0020] Figure 2 Schematic diagram of the segmentation result provided in an embodiment of the present application.

[0021] Figure 3 It is a schematic diagram of the nomogram model in the embodiment of the present application.

[0022] Figure 4 It is a schematic diagram of the system structure provided in an embodiment of the present application.

[0023] Figure 5 This is a schematic diagram of the terminal device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0025] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, compositions, and / or circuits have been described at a relatively high level, without detail, to avoid unnecessarily obscuring aspects of the present application.

[0026] Flowcharts are used in this application to illustrate the execution processes performed by the system according to the embodiments of the present application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. Instead, these execution processes may be executed in reverse order or simultaneously. In addition, at least one additional execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0027] Before further explaining the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0028] (1) In response to, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0029] (2) Based on, used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0030] An embodiment of the present application provides a method for detecting the onset of knee osteoarthritis, which obtains an MRI image by performing magnetic resonance imaging on a target area of ​​a patient, and extracts and filters image group features in the MRI image to obtain multiple image group feature data. The multiple image group feature data are processed with multiple clinical data based on a joint scoring model to obtain a predicted score for the risk of knee osteoarthritis.

[0031] This embodiment provides an automated method for predicting the risk of knee osteoarthritis, which uses MRI images and multiple clinical indicators to establish a joint evaluation model. Based on this joint evaluation model, knee osteoarthritis prediction and the corresponding degree are judged, with a high detection rate and accuracy.

[0032] For more information on this forecasting method, see Figure 1 , including the following steps:

[0033] Step S11: Acquire clinical data of the patient to be diagnosed and image group feature data of the target area of ​​the patient to be diagnosed.

[0034] In this embodiment, this method adopts a combined prediction method to predict orthopedic diseases, especially knee osteoarthritis, and especially to judge and determine the degree of knee osteoarthritis. The logic of the combined prediction method is to obtain the score corresponding to each indicator based on the nomogram through the prediction model, and then obtain the total score. The total score is used to determine the possibility and degree of knee osteoarthritis.

[0035] The indicators in this embodiment include clinical data and MRI image group feature data, wherein the image group feature data is MRI image feature data in multiple sequences and multiple frequency bands, and the state evaluation model is a nomogram model.

[0036] Specifically, the clinical data included in the indicators include hypertension, lower extremity deep vein valve function index, platelet-to-lymphocyte ratio, and D-dimer data. The acquisition of imaging feature data begins with image segmentation and region-of-interest extraction of the MRI images. In existing technologies, image segmentation typically involves manual segmentation, which relies heavily on physician judgment, is time-consuming, and, because it relies on physician experience, can negatively impact the results.

[0037] In order to solve this problem, in this embodiment, computer technology is used to segment the MRI image, and the segmentation of the regional image is achieved through automated segmentation means.

[0038] Specifically, in this embodiment, a segmentation model is configured, and the segmentation of the tumor area in the MRI image is completed based on this segmentation model. Among them, the segmentation model adopts an adaptive CNN network with a 2DnnU-Net architecture, including an encoder and a decoder. Among them, the segmentation model and the segmentation results can be referred to Figure 2 As shown in the figure, four subchondral bone regions of the weight-bearing articular surface of the knee joint were selected as regions of interest (ROI): the medial femur, the lateral femur, the medial tibial plateau, and the lateral tibial plateau. The medial 2 / 5 of the widest part of the femur and tibia was defined as the medial side, and the lateral 2 / 5 was defined as the lateral side.

[0039] The MRI image can be segmented by the segmentation model, and the imageomics toolkit is used to extract the imageomics features of each image corresponding to multiple sequences based on the segmented image. LASSO regression is used to reduce the dimension of the features, and the imageomics features with a weight of 0 are removed to reduce overfitting caused by high-dimensional data.

[0040] A total of 16 imageomics features are screened through the above process, including multiple shape features, multiple first-order features, multiple gray level co-occurrence matrix features, multiple gray level run length matrix features, and multiple gray level size zone matrix features.

[0041] Among them, the shape features include the maximum 2D diameter of the original sequence, the second largest axis length of the original sequence, and the area of the original sequence; the first-order features include the first-order root mean square of the original sequence, the first-order quartile range of the LHL sequence, the first-order mean absolute deviation of the LHH sequence, the first-order mean of the LHH sequence, and the first-order root mean square of the LHH sequence; the gray level co-occurrence matrix features include the gray level co-occurrence matrix normalization of the original sequence, the gray level co-occurrence matrix difference entropy of the original sequence, the gray level co-occurrence matrix correlation information measure 1 of the LHL sequence, and the gray level co-occurrence matrix joint average of the LHL sequence; the gray level run length matrix features include the gray level run length matrix low gray level run emphasis and the gray level run length matrix run percentage of the original sequence; and the gray level size zone matrix features include the gray level size zone feature normalized region size non-uniformity of the HHH sequence and the gray level size zone feature region size non-uniformity of the original sequence.

[0042] Step S12. Input the clinical data and the imageomics feature data into a state evaluation model to calculate a state value.

[0043] In this embodiment, the state evaluation model is a nomogram model. The nomogram model obtains a total score based on the mapping scores corresponding to the multiple clinical data and the imageomics feature data obtained in step S11, and the scores corresponding to each index and the imageomics feature data. The total score is the state value in this embodiment, which is used to represent the classification and the degree of the risk of knee osteoarthritis.

[0044] Specifically, the first score, the second score, the third score, the fourth score, and the fifth score corresponding to the hypertension state, the deep vein valve function index of the lower extremity, the platelet to lymphocyte ratio (PLR), the D-dimer data, and the imageomics feature data are obtained, as well as the corresponding total score.

[0045] The results of the nomogram model can be referred to in Figure 3wherein for radscore is the overall calculated score of the image group feature data, i.e. the sixth score value. The calculation of the radscore score is realized based on the following formula: Radscore = -0.0055638389177*original sequence max 2D diameter - 0.01352954*original sequence second largest axis length + 0.010503*original sequence table area - 0.02673876*original sequence first order root mean square + 0.0011265*LHL sequence first order quartile range + 0.002902*LHH sequence first order mean absolute deviation + 0.001029*LHH sequence first order mean - 0.0078229*LHH sequence first order root mean square - 0.0017543*original sequence gray level co-occurrence matrix normalization - 0.006327*original sequence gray level co-occurrence matrix difference entropy + 0.026483*LHL sequence gray level co-occurrence matrix correlation information measure 1 - 0.026497*LHL sequence gray level co-occurrence matrix joint mean + 0.00582631*original sequence gray run length matrix low gray run length emphasis + 0.013129*gray run length matrix run percentage - 0.03823356*HHH sequence gray size region feature normalized region size heterogeneity + 0.497683*original sequence gray size region feature region size heterogeneity.

[0046] And the specific calculation process of nomogram in the embodiment can be directly referred to Figure 3 As shown in the figure, the calculation logic of nomogram can be realized, and in the embodiment, no longer be described in detail.

[0047] In summary, in the embodiment, the incidence possibility and the incidence degree of knee osteoarthritis can be effectively evaluated in the early stage by combining the above-mentioned multiple groups of indexes, and the incidence probability and the incidence degree of knee osteoarthritis can be more directly determined through the score data, so as to realize the overall automatic prediction of knee osteoarthritis.

[0048] Referring to Figure 4 , for the method provided in steps S11-S12, in the embodiment, a prediction system 40 is further provided, and the system comprises:

[0049] A data acquisition unit 41 is configured to acquire clinical data of a patient to be diagnosed and image group feature data of a target region of the patient to be diagnosed.

[0050] A prediction unit 42 is configured to input the clinical data and the image group feature data into a risk prediction model to calculate a risk value.

[0051] In the embodiment, the risk prediction model is a nomogram model.

[0052] Referring to Figure 5The above method can also be integrated into the terminal device 50 provided in the application. The terminal device can have great differences due to different configurations or performances. The terminal device can include one or more processors 501 and memories 502. The memories 502 can store one or more stored application programs or data. The memories 502 can be temporary memories or persistent memories. The application programs stored in the memories 402 can include one or more modules (not shown in the figure). Each module can include a series of computer executable instructions in the terminal device. Further, the processor 501 can be configured to communicate with the memories 502, and execute the series of computer executable instructions in the memories 502 on the terminal device. The terminal device can also include one or more power supplies 403, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, one or more keyboards 506, and the like.

[0053] In one specific embodiment, the terminal device includes a memory, and one or more programs stored in the memory. The one or more programs can include one or more modules, and each module can include a series of computer executable instructions in the terminal device. The one or more processors are configured to execute the one or more programs, and the one or more programs include computer executable instructions for performing the following operations:

[0054] Obtaining clinical data of a patient to be diagnosed and image group feature data of a target region of the patient to be diagnosed;

[0055] Inputting the clinical data and the image group feature data into a state evaluation model to calculate a risk value. The state evaluation model is a nomogram model.

[0056] The various components of the processor will be described in detail as follows:

[0057] In this embodiment, the processor is an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the application, for example, one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs).

[0058] Alternatively, the processor can perform various functions by running or executing software programs stored in the memory, and calling data stored in the memory, for example, executing the above Figure 1 The method shown in the figure.

[0059] In a particular implementation, as one example, the processor can include one or more microprocessors.

[0060] The memory is configured to store a software program for implementing the scheme of the present application, and the processor is configured to control the execution of the software program. The specific implementation can refer to the method embodiments described above, and thus will not be described here.

[0061] Optionally, the memory can be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magneto-optical disk, a magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to this. The memory can be integrated with the processor or can exist independently and be coupled to the processing unit through the interface circuit of the processor. The embodiments of the present application are not limited in this regard.

[0062] It should be noted that the structure of the processor shown in the present embodiment does not constitute a limitation on the device, and the actual device can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0063] In addition, the technical effects of the processor can refer to the technical effects of the methods described in the method embodiments described above, and thus will not be described here.

[0064] It should be understood that the processor in the embodiments of the present application can be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0065] It should also be understood that the memory in the embodiments of the present application can be volatile or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), or flash memory, among others. The volatile memory can be random access memory (RAM), which is used as external cache. By way of example, and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0066] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. 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 a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0067] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0068] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 application.

[0069] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0071] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0072] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0073] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0074] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the essential part or part of the technical solutions that make contributions to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0075] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for detecting the pathogenesis of knee osteoarthritis, characterized in that: The method comprises: Acquiring clinical data of a patient to be diagnosed and imaging group characteristic data of a target area of ​​the patient to be diagnosed; the imaging group characteristic data includes MRI image characteristic data, and the clinical data includes hypertension status, lower extremity deep vein valve function index, platelet-to-lymphocyte ratio, and D-dimer data; The clinical data and the imaging group characteristic data are input into a state evaluation model to calculate a state value; the state evaluation model is a nomogram model.

2. The method for detecting the pathogenesis of knee osteoarthritis according to claim 1, wherein: The image group feature data includes multiple shape features, multiple first-order features, multiple grayscale co-occurrence matrix features, multiple grayscale run length matrix features, and multiple grayscale size area matrix features.

3. The method for detecting the pathogenesis of knee osteoarthritis according to claim 2, wherein: The plurality of shape features include the largest 2D diameter of the original sequence, the second largest axis length of the original sequence, and the surface area of ​​the original sequence.

4. The method for detecting the pathogenesis of knee osteoarthritis according to claim 3, wherein: The first-order features include the first-order root mean square of the original sequence, the first-order interquartile range of the LHL sequence, the first-order mean absolute deviation of the LHH sequence, the first-order mean of the LHH sequence, and the first-order root mean square of the LHH sequence.

5. The method for detecting the pathogenesis of knee osteoarthritis according to claim 4, characterized in that: The gray level co-occurrence matrix features include normalization of the original sequence gray level co-occurrence matrix, difference entropy of the original sequence gray level co-occurrence matrix, related information measure 1 of the LHL sequence gray level co-occurrence matrix, and joint average of the LHL sequence gray level co-occurrence matrix.

6. The method for detecting the onset of knee osteoarthritis according to claim 5, characterized in that: The grayscale run length matrix features include original sequence grayscale run length matrix low grayscale run emphasis and grayscale run length matrix run percentage.

7. The method for detecting the pathogenesis of knee osteoarthritis according to claim 6, characterized in that: The grayscale size region matrix features include normalized region size unevenness of HHH sequence grayscale size region features and region size unevenness of original sequence grayscale size region features.

8. The method for detecting the pathogenesis of knee osteoarthritis according to claim 7, wherein: The inputting of the clinical data and the imaging group characteristic data into the state evaluation model to calculate the state value includes: obtaining the first score, second score, third score, fourth score and fifth score corresponding to the hypertension state, lower limb deep vein valve function index, platelet-to-lymphocyte ratio, D-dimer data and the imaging group characteristic data based on the nomogram model, as well as the corresponding total score.

9. The method for detecting the onset of knee osteoarthritis according to claim 8, characterized in that: The fifth score corresponding to the characteristic data of the image group is obtained, including: obtaining the sub-scores corresponding to the maximum 2D diameter of the original sequence, the second largest axis length of the original sequence, the surface area of ​​the original sequence, the first-order root mean square of the original sequence, the first-order interquartile range of the LHL sequence, the first-order mean absolute deviation of the LHH sequence, the first-order mean of the LHH sequence, the first-order root mean square of the LHH sequence, the normalization of the grayscale co-occurrence matrix of the original sequence, the difference entropy of the grayscale co-occurrence matrix of the original sequence, the related information measure 1 of the grayscale co-occurrence matrix of the LHL sequence, the joint average of the grayscale co-occurrence matrix of the LHL sequence, the low grayscale run emphasis of the grayscale run length matrix of the original sequence, the run percentage of the grayscale run length matrix, the normalized regional size unevenness of the grayscale size area feature of the HHH sequence, and the regional size unevenness of the grayscale size area feature of the original sequence, and updating the sub-scores according to the calculated weights corresponding to the above data and weighting them to obtain the fifth score.

10. A knee osteoarthritis disease status detection system, characterized in that: The system comprises: A data acquisition unit, configured to acquire clinical data of a patient to be diagnosed and imaging feature data of a target area of ​​the patient to be diagnosed; The prediction unit is used to input the clinical data and the imaging group feature data into a state evaluation model to calculate a state value; the state evaluation model is a nomogram model.

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