Automatic scoring system for SPARCC and SPARCC SSS scoring of ankylosing spondylitis

Abstract

The invention discloses an automatic scoring system for SPARCC and SPARCC SSS scoring of ankylosing spondylitis, and belongs to the technical field of medical informatization, and the system comprises an image collection and processing module which is responsible for receiving and preprocessing an MRI image of a patient, including image denoising and enhanced comparison, and providing high-quality image data for subsequent scoring analysis; the feature extraction module is used for automatically extracting features related to the SPARCC score and the SPARCCSSS score from the processed image based on a preset algorithm, such as a bone marrow edema region and a subchondral sclerosis degree; and the score calculation module is used for automatically calculating and generating a score result according to the extracted features and the standards of SPARCC scores and SPARCCSSS scores. The score calculation comprises comprehensive consideration of multiple dimensions such as an accumulation range, edema intensity, edema depth and the like; and the result display and report generation module is used for displaying the scoring result to a doctor in an intuitive manner. According to the automatic scoring system for the SPARCC and SPARCC SSS scoring of the ankylosing spondylitis, rapid judgment and automatic scoring can be realized.

Description

Technical Field

[0001] The invention belongs to the field of medical information technology, in particular to an automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scoring. Background Art

[0002] Ankylosing spondylitis is an autoimmune disease of unknown cause characterized by chronic inflammation of the axial joints, which is common in adolescents.

[0003] The SPARCC scoring system is an important tool for evaluating the severity of sacroiliac joint lesions in patients with ankylosing spondylitis, including the inflammation score (SPARCC) and the structural score (SPARCCSSS). The traditional scoring method relies on manual film reading, which is time-consuming and easily affected by subjective factors.

[0004] Therefore, the development of an automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scores has important clinical and scientific research value. Summary of the invention

[0005] In view of the problems in the prior art such as the complicated, highly subjective and inefficient SPARCC and SPARCCSSS scoring processes, the present invention aims to provide an automated scoring tool to achieve rapid and accurate scoring of imaging data of patients with ankylosing spondylitis.

[0006] To achieve the above object, the present invention provides the following technical solution: an automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scoring, comprising:

[0007] Image acquisition and processing module: responsible for receiving and preprocessing the patient's MRI images, including image denoising and contrast enhancement, to provide high-quality image data for subsequent scoring analysis;

[0008] Feature extraction module: Based on the preset algorithm, it automatically extracts features related to the SPARCC score and SPARCCSSS score from the processed images, such as the bone marrow edema area and the degree of subchondral sclerosis;

[0009] Scoring calculation module: Based on the extracted features, the scoring results are automatically calculated and generated according to the SPARCC scoring and SPARCCSSS scoring standards. The scoring calculation includes comprehensive consideration of multiple dimensions such as the extent of involvement, edema intensity, and edema depth;

[0010] Result display and report generation module: The scoring results are presented to doctors in an intuitive manner, and a detailed scoring report is automatically generated, including scoring details, image comparison, etc., to facilitate doctors' condition analysis and treatment decisions.

[0011] The method for using the automated scoring system for the SPARCC and SPARCC SSS scores for ankylosing spondylitis comprises the following steps:

[0012] Step 1: Import the patient’s MRI images into the tool;

[0013] Step 2: The tool automatically performs image preprocessing and feature extraction;

[0014] Step 3: Based on the extracted features, the tool automatically calculates the SPARCC score and SPARCCSSS score;

[0015] Step 4: Display the scoring results and generate a detailed scoring report for the doctor's reference.

[0016] Compared with the prior art, the technical effects and advantages of the present invention are as follows:

[0017] The automatic scoring system for SPARCC and SPARCC SSS scoring of ankylosing spondylitis can improve scoring efficiency. The automated scoring process greatly shortens the scoring time and improves work efficiency.

[0018] The automatic scoring system for SPARCC and SPARCC SSS scores of ankylosing spondylitis can enhance the accuracy of scoring, reduce the interference of human factors, and improve the objectivity and accuracy of scoring;

[0019] The automatic scoring system of SPARCC and SPARCC SSS for ankylosing spondylitis can assist clinical decision-making, provide doctors with a more accurate and comprehensive basis for disease assessment, and help formulate more reasonable treatment plans;

[0020] The automatic scoring system of SPARCC and SPARCC SSS for ankylosing spondylitis can promote the development of scientific research. The standardized scoring tool provides reliable data support for the scientific research of ankylosing spondylitis and helps to promote the research progress in this field.

[0021] The automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scores can quickly judge and automatically score. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a framework diagram of the present invention. DETAILED DESCRIPTION

[0023] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0024] See also Figure 1 ,

[0025] An automated scoring system for the SPARCC and SPARCC SSS scores for ankylosing spondylitis, including:

[0026] Image acquisition and processing module: responsible for receiving and preprocessing the patient's MRI images, including image denoising and contrast enhancement, to provide high-quality image data for subsequent scoring analysis;

[0027] Feature extraction module: Based on the preset algorithm, it automatically extracts features related to the SPARCC score and SPARCCSSS score from the processed images, such as the bone marrow edema area and the degree of subchondral sclerosis;

[0028] Scoring calculation module: Based on the extracted features, the scoring results are automatically calculated and generated according to the SPARCC scoring and SPARCCSSS scoring standards. The scoring calculation includes comprehensive consideration of multiple dimensions such as the extent of involvement, edema intensity, and edema depth;

[0029] Result display and report generation module: The scoring results are presented to doctors in an intuitive manner, and a detailed scoring report is automatically generated, including scoring details, image comparison, etc., to facilitate doctors' condition analysis and treatment decisions.

[0030] The method for using the automated scoring system for the SPARCC and SPARCC SSS scores for ankylosing spondylitis comprises the following steps:

[0031] Step 1: Import the patient’s MRI images into the tool;

[0032] Step 2: The tool automatically performs image preprocessing and feature extraction;

[0033] Step 3: Based on the extracted features, the tool automatically calculates the SPARCC score and SPARCCSSS score;

[0034] Step 4: Display the scoring results and generate a detailed scoring report for the doctor's reference.

[0035] Detailed implementation steps of the scoring tool:

[0036] S1, system initialization and image import: start the ankylosing spondylitis automatic scoring system, load the preset scoring protocol and algorithm model; at the same time, import the patient's sacroiliac joint MRI images into the system; these images should contain clear sacroiliac joint structures and have undergone necessary preprocessing, such as denoising and contrast enhancement;

[0037] S2, image preprocessing and segmentation: The system uses deep learning algorithms to further preprocess the imported images, including image registration, artifact removal, and automatic segmentation of the sacroiliac joint area; this step aims to accurately extract the sacroiliac joint area from the complex background, laying the foundation for subsequent analysis;

[0038] S3, feature extraction and quantification: The system uses deep learning models, such as convolutional neural networks (CNN), to extract features from the segmented sacroiliac joint images; these features include but are not limited to typical pathological changes of ankylosing spondylitis, such as bone erosion, sclerosis, joint space narrowing, and new bone formation; the system will quantify these features, such as calculating the erosion area and degree of sclerosis, to form a detailed data set for scoring;

[0039] S4, SPARCC and SPARCCSSS score calculation: Based on the extracted feature data set, the system uses a pre-trained scoring model to automatically calculate the SPARCC and SPARCCSSS scores; the SPARCC score focuses on bone marrow edema of the sacroiliac joint, while the SPARCCSSS score focuses more on structural changes in the sacroiliac joint; the system will weight each feature according to the scoring protocol and ultimately derive a total score;

[0040] S5, Scoring result display and report generation: The system will display the automatically generated SPARCC and SPARCCSSS scoring results to the user in an intuitive way, such as through charts, color coding, etc. At the same time, the system will also generate a detailed report containing the scoring results, image analysis details and diagnostic suggestions, so that doctors can quickly understand the pathological conditions of the patient's sacroiliac joints;

[0041] S6, Data preservation and remote access: The scoring results and generated reports will be securely stored in the system's database for subsequent review and scientific research analysis; in addition, the system also supports remote access, and doctors can view patients' scoring results and reports anytime and anywhere via the Internet.

[0042] In order to further improve the functionality of the system, the following is a detailed supplement to its workflow:

[0043] 1. In step S1, the scoring protocol and algorithm model loaded by the system should be based on the latest research results of ankylosing spondylitis to ensure the accuracy and reliability of the scoring.

[0044] 2. In step S2, the accuracy of image segmentation is crucial for subsequent analysis. The system should adopt advanced deep learning algorithms such as U-Net, MaskR-CNN, etc. to improve segmentation accuracy.

[0045] 3. In step S3, the process of feature extraction and quantification should fully consider the pathological characteristics of ankylosing spondylitis to ensure that the extracted features can fully reflect the pathological changes of the sacroiliac joint.

[0046] 4. In step S4, the scoring model should be trained and validated with a large amount of clinical data to ensure the accuracy and consistency of the scoring. At the same time, the system should also provide the function of manually adjusting the scoring to meet the scoring needs in special circumstances.

[0047] 5. In step S5, the report generated by the system should contain rich image analysis details and diagnostic suggestions to assist doctors in making more accurate diagnosis and treatment decisions.

[0048] 6. In step S6, the system should ensure the security and privacy of data and adopt encrypted storage and transmission technology to prevent data leakage.

[0049] In addition, the system should have the following features:

[0050] High degree of automation: The system can automatically complete the entire process of image preprocessing, feature extraction, score calculation and report generation, reducing manual intervention and improving work efficiency.

[0051] High accuracy: The system uses deep learning algorithms for image analysis and scoring calculations, which can accurately identify pathological changes in the sacroiliac joint and provide reliable scoring results.

[0052] Strong scalability: The system supports the updating and optimization of algorithm models to adapt to new research results and clinical needs.

[0053] Strong user-friendliness: The system interface is simple and clear, easy to operate, and easy for doctors to use.

[0054] The software not only demonstrates strong generalization in design, but also ensures seamless integration in various medical environments by supporting multiple image formats (such as DICOM, etc.) and adapting to different brands and models of magnetic resonance equipment. We look forward to seeing this automatic scoring tool being widely used in more hospitals across the country and even around the world, helping doctors to complete the SPARCC and SPARCCSSS scoring of ankylosing spondylitis patients more efficiently and accurately. This will not only greatly improve the accuracy of medical diagnosis and the personalization of treatment plans, but is also expected to promote the standardization and sharing of medical data, and further promote scientific research progress and clinical practice in the field of ankylosing spondylitis.

[0055] Detailed definition of input terminal and scope of patent protection

[0056] The automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scores of the present invention fully considers wide compatibility and safety in the design of the input end, and is specifically defined as follows:

[0057] 1. Supported file formats: This tool strictly follows the international standards in the field of medical imaging and mainly supports MRI image files in DICOM (version 3.0 and above) format. DICOM files must contain complete metadata, including but not limited to key information such as patient name (PatientName), examination date (StudyDate), sequence description (SequenceDescription), etc., to ensure the integrity and traceability of image data.

[0058] 2. Input method:

[0059] Local file import: Users can select DICOM files stored in the local file system through the graphical user interface (GUI). The files should be located in the preset folder, and the file name should follow the naming convention of "patient ID_examination date_time stamp.dcm" to ensure orderly file management and efficient data retrieval.

[0060] Network transmission: This tool also provides HTTP REST API interface, which supports remote systems to send image data through POST requests. The request body must be in JSON format, including Base64-encoded DICOM image file content and necessary metadata fields, such as patient ID, examination type, etc. The API interface uses HTTPS protocol to ensure the security and integrity of data transmission.

[0061] 3. User interface design: The GUI interface design is intuitive and easy to use, including clear file import buttons, progress indicators, error prompts and other elements. Users only need to click the import button and select or enter the file path to automatically complete the loading and preprocessing of image data.

[0062] 4. Claims: The claims of the present invention clearly define that any unauthorized modification of the input format (including but not limited to DICOM version, metadata requirements), file naming rules, API interface definition (including request method, parameter type, return value format) or GUI interface layout shall be deemed as an infringement of this patent right. The scope of protection of this patent is not limited to the above-mentioned specific implementation methods, but also includes any substantially equivalent input means and technical solutions.

[0063] 5. Examples and diagrams: To facilitate understanding and implementation, this patent application document is accompanied by screenshots of DICOM file samples, GUI interface layout diagrams, and API call sample codes to intuitively demonstrate the specific requirements and operation procedures of the input end.

[0064] In summary, the present invention aims to ensure the wide applicability of the automatic scoring tool in different medical environments and the effectiveness of patent protection through detailed and specific input end definitions.

[0065] Ankylosing spondylitis

[0066] Ankylosing spondylitis (AS) is an autoimmune disease with unknown causes and mainly chronic inflammation of the axial joints. Its research involves many aspects, including etiology, clinical manifestations, diagnostic methods, treatment strategies, and disease assessment tools. The following is an expanded introduction to the research status of ankylosing spondylitis:

[0067] 1. Etiology

[0068] Although the specific cause of ankylosing spondylitis is not yet fully understood, genetic and environmental factors are considered to be important factors in its onset. The HLA-B27 gene is strongly associated with AS, and about 90% of AS patients carry this gene, but not all people who carry the HLA-B27 gene will develop the disease, indicating that there are other unknown factors at work. In addition, infection, immune disorders, etc. are also believed to be involved in the pathogenesis of AS.

[0069] 2. Clinical manifestations

[0070] Classic symptoms of AS include stiffness and inflammation of the back, with characteristic manifestations of syndesmophytes and spinal ankylosis. As the disease progresses, patients may experience changes in posture, fatigue, and pain and stiffness in the sacroiliac joints and spine. In advanced stages, patients may show significant kyphosis and spinal fusion, leading to significant movement and functional impairment.

[0071] 3. Diagnostic Methods

[0072] The diagnosis of AS mainly relies on clinical manifestations, imaging examinations and laboratory tests. Sacroiliitis is a characteristic radiological manifestation of AS on X-ray examinations, but MRI can detect early inflammatory changes before they appear on conventional X-rays, so MRI is particularly important in diagnosis. Although the detection of HLA-B27 gene is helpful for diagnosis, it is not a necessary condition for confirmation. In addition, the detection of inflammatory markers such as erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) is also often used to monitor disease activity.

[0073] IV. Treatment Strategies

[0074] Treatment strategies for AS include medication, physical therapy, and surgery. Medication mainly involves nonsteroidal anti-inflammatory drugs (NSAIDs) for pain relief and inflammation, disease-modifying antirheumatic drugs (DMARDs) for disease progression control, and biologics such as tumor necrosis factor (TNF) inhibitors and interleukin 17 (IL-17) inhibitors that target inflammatory mechanisms. Physical therapy emphasizes regular exercise, stretching, and posture maintenance to maintain spinal flexibility and function. In severe cases, spinal surgery or hip replacement surgery may be required.

[0075] 5. Disease Assessment Tools

[0076] The SPARCC scoring system is an important tool for assessing the severity of sacroiliac joint lesions in patients with ankylosing spondylitis, including the inflammatory score (SPARCC) and the structural score (SPARCCSSS). However, the traditional scoring method relies on manual film reading, which is time-consuming and easily affected by subjective factors. Therefore, the development of automated scoring tools has important clinical and scientific research value. In addition, assessment tools such as ASDAS (Ankylosing Spondylitis Disease Activity Score) and BASDAI (Bath Ankylosing Spondylitis Activity Index) are also widely used to assess the disease activity and treatment effect of AS patients.

[0077] 6. Research Progress

[0078] With the continuous deepening of medical research, the understanding of AS has gradually changed from vague to clear, and treatment methods are also constantly innovating. For example, the emergence of biological agent therapy has provided new treatment options for AS patients and significantly improved the prognosis of patients. At the same time, in-depth research on the pathogenesis of AS has also provided the possibility of developing new therapeutic targets. In addition, with the development of medical technology, the development and application of automated scoring tools will further improve the accuracy and efficiency of AS disease assessment.

[0079] In summary, the research on ankylosing spondylitis involves many aspects, including etiology, clinical manifestations, diagnostic methods, treatment strategies, and disease assessment tools. With the continuous progress of medical research and the development of technology, it is believed that more new treatment methods and assessment tools will be introduced in the future, bringing better treatment effects and quality of life to AS patients.

[0080] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated scoring system for SPARCC and SPARCC SSS scores for ankylosing spondylitis, characterized by: include: Image acquisition and processing module: responsible for receiving and preprocessing the patient's MRI images, including image denoising and contrast enhancement, to provide high-quality image data for subsequent scoring analysis; Feature extraction module: Based on the preset algorithm, it automatically extracts features related to the SPARCC score and SPARCCSSS score from the processed images, such as the bone marrow edema area and the degree of subchondral sclerosis; Scoring calculation module: Based on the extracted features, the scoring results are automatically calculated and generated according to the SPARCC scoring and SPARCCSSS scoring standards. The scoring calculation includes comprehensive consideration of multiple dimensions such as the extent of involvement, edema intensity, and edema depth; Result display and report generation module: The scoring results are presented to doctors in an intuitive manner, and a detailed scoring report is automatically generated, including scoring details, image comparison, etc., to facilitate doctors' condition analysis and treatment decisions.

2. A method for using the automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scoring, which is used to use the automatic scoring system for ankylosing spondylitis SPARCC and SPARCC SSS scoring according to claim 1, characterized in that: The following steps are involved: Step 1: Import the patient’s MRI images into the tool; Step 2: The tool automatically performs image preprocessing and feature extraction; Step 3: Based on the extracted features, the tool automatically calculates the SPARCC score and SPARCCSSS score; Step 4: Display the scoring results and generate a detailed scoring report for the doctor's reference.

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