Shared ophthalmology examination report image management system

Abstract

The present invention discloses a shared ophthalmic examination report image management system, belonging to the field of image management technology. The present invention solves the problem that existing technologies have difficulty in achieving image sharing and intelligent analysis. By constructing an image sharing platform, ophthalmic examination report images can be centrally stored and managed based on the platform. Quantum algorithms are used to encrypt and protect images and optimize them, improving image standardization, thereby preventing misdiagnosis by doctors with less clinical experience and ensuring data security and privacy. A quantum simulation model is also used to perform intelligent analysis on stored ophthalmic examination report images, assisting doctors in making rapid diagnoses of the reports, thereby improving diagnostic accuracy and consultation speed, and shortening waiting times for subsequent patients. A role-based access control mechanism is also established to ensure that each user can only access data within their authority, preventing unauthorized access and improving the confidentiality and practicality of the image sharing platform.

Description

Technical Field

[0001] The present invention relates to the technical field of image management, in particular to a shared ophthalmological examination report image management system. Background Art

[0002] Ophthalmological examinations can assess eye function. Common visual function tests include visual psychophysical tests, such as visual acuity, visual field, color vision, dark adaptation, stereoscopic vision, contrast sensitivity, and visual electrophysiological tests. With the advancement of medical informatization, the management of ophthalmological examination reports and images has gradually shifted from traditional paper records to digital management, aiming to improve the efficiency and quality of medical services.

[0003] In existing technologies, for example, the system used by the East Campus of a certain hospital includes image management and data analysis functions, and can also be exported into Excel spreadsheets to facilitate data collection and paper writing. However, since this system does not run in the doctor's workstation in the clinic, and outpatient patients often forget to bring their previous eye examination results, the doctor must check the corresponding machine when conducting a consultation, which not only increases the doctor's clinical diagnosis process, but also delays the waiting time of subsequent patients.

[0004] Therefore, the existing needs are not met, so we propose a shared ophthalmic examination report image management system. Summary of the Invention

[0005] The purpose of the present invention is to provide a shared ophthalmic examination report image management system. By constructing an image sharing platform, ophthalmic examination report images can be centrally stored and managed based on the platform; quantum algorithms are used to encrypt and protect images and optimize them to improve the standardization of images, thereby preventing misdiagnosis by doctors with less clinical experience, while ensuring the security and privacy of data; and quantum simulation models are used to perform intelligent analysis of stored ophthalmic examination report images to assist doctors in making rapid diagnoses of reports, thereby improving diagnostic accuracy and consultation speed, and shortening the waiting time for subsequent patients; and by setting a role-based access control mechanism, it is ensured that various users can only access data within their authority, preventing unauthorized access, improving the confidentiality and practicality of the image sharing platform, and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A shared ophthalmic examination report image management system, the system comprising: an image sharing platform, the image sharing platform comprising an image collection unit, a quantum management unit, a security management unit and a simulation processing unit;

[0008] The image collection unit is configured to collect ophthalmic examination report images in a two-dimensional operating plane and upload the ophthalmic examination report images to an image sharing platform for centralized storage and management; wherein the image sharing platform supports uploading and downloading of image files in multiple formats, making it convenient for users to access and use them at any time;

[0009] The quantum management unit is configured to use a quantum key distribution protocol and a quantum one-time pad encryption algorithm to encrypt and store the ophthalmic examination report image to ensure data security and privacy; and use a quantum annealing optimization algorithm to optimize the quality of the ophthalmic examination report image and generate a standardized image with reduced noise and distortion;

[0010] The security management unit is configured to set up a medical authority management and identity authentication mechanism; if the medical authority is verified, the medical authority is granted the right to use the image sharing platform; otherwise, the medical authority is denied;

[0011] The simulation processing unit is configured to embed a quantum simulation model in the image sharing platform, quickly analyze the ophthalmic examination report images through the quantum simulation model, and provide personalized treatment recommendations based on the analysis results to assist doctors in making accurate diagnoses.

[0012] Furthermore, the quantum management unit includes:

[0013] An environment deployment module is configured to deploy a quantum key distribution device and a quantum storage database in the image sharing platform. The quantum key distribution device includes a QKD transmitter and receiver. The quantum one-time pad encryption algorithm library and image processing tools are integrated into the image sharing platform to support quantum key generation and image encryption operations, ensuring the security of data during transmission and storage.

[0014] A key distribution module is configured to use a quantum key distribution protocol to generate a shared quantum key between a sender and a receiver, and ensure the security of key distribution through a quantum channel;

[0015] an image encryption module configured to convert the ophthalmic examination report image into a binary data stream for encryption processing; and to process the ophthalmic examination report image in blocks to meet the requirements of the quantum one-time pad encryption algorithm; and to encrypt the ophthalmic examination report image bit by bit using the generated quantum key to ensure the uniqueness of each bit encryption key;

[0016] The image storage module is configured to store the encrypted ophthalmological examination report image in the quantum storage database to support efficient retrieval and management.

[0017] Furthermore, the quantum management unit further includes:

[0018] a conditional building module configured to transform the image quality optimization problem into an objective function, wherein the objective function is to minimize noise and distortion in the image; use pixel values ​​or local regions of the image as optimization variables, and set constraints based on image characteristics, wherein the constraints include a range of pixel values ​​and local smoothness;

[0019] The quantum annealing module is configured to map the objective function and constraints into the Hamiltonian of the quantum annealer, use the quantum annealer to solve the problem, find the optimal pixel value or local area, and gradually optimize the image quality through multiple iterations.

[0020] Furthermore, the quantum management unit further includes:

[0021] a preprocessing module configured to convert the color image of the eye examination report into a grayscale image and use a Gaussian filter image processing technique to preliminarily detect noise in the image, thereby simplifying subsequent processing steps;

[0022] The post-processing module is configured to perform denoising and enhancement processing on the image based on the quantum annealing optimization results to improve the clarity and contrast of the image; and convert the optimized image into a standardized format that meets the requirements of medical diagnosis.

[0023] Furthermore, the image collection unit includes:

[0024] an atlas acquisition module configured to read the collected ophthalmological examination report image, determine a first image element composition of the ophthalmological examination report image, and divide the ophthalmological examination report image into a multi-element report atlas according to the first image element composition;

[0025] a positioning module configured to retrieve an ophthalmic examination report image template, locate the image template in the ophthalmic examination report image template according to the first image component element, and simultaneously obtain a second image component element of the ophthalmic examination report image template;

[0026] The judgment processing module is configured as follows:

[0027] Determining whether the first image component element corresponds to the second image component element one-to-one according to the positioning result;

[0028] When there is no one-to-one correspondence between the first image component element and the second image component element, the collected ophthalmic examination report image is annotated, and the ophthalmic examination report image is re-collected according to the annotated result;

[0029] When the first image component elements correspond to the second image component elements one-to-one, dividing the ophthalmological examination report image template into a multi-element template report atlas based on the second image component elements;

[0030] A matching module is configured to match the multi-element report atlas with the multi-element template report atlas, and determine a target matching degree between each element report diagram in the multi-element report atlas and a corresponding element template report diagram in the multi-element template report atlas;

[0031] The qualification judgment module is configured as follows:

[0032] Obtaining a matching degree threshold, and comparing the target matching degree with the matching degree threshold to determine whether the element report diagram in the element report diagram set is qualified;

[0033] When the target matching degree is equal to or greater than the matching degree threshold, the element report diagram is judged to be qualified;

[0034] Otherwise, the element report diagram is judged to be unqualified;

[0035] a correction module configured to, when an element report diagram is unqualified, read an element template report diagram corresponding to the unqualified element report diagram, locate a correction point in the unqualified element report diagram based on the element template report diagram, and correct the unqualified element report diagram according to the correction point and the element template report diagram until it conforms to the element template report diagram;

[0036] The uploading module is configured to upload the collected ophthalmic examination report images to the image sharing platform when all the multi-element template report atlases are qualified.

[0037] Furthermore, the image storage module includes:

[0038] The identification building module is configured to set identification information for each patient, including: patient ID, name, ID number or medical record number; and set filtering conditions based on each patient's medical records, including: examination date and examination type, to narrow the search scope;

[0039] The sharing module is configured so that medical staff can select the ophthalmic examination report image they need to retrieve from the search results; and use the same quantum key to decrypt the ciphertext bit by bit to restore the original image data. The images include: fundus images, OCT images and text reports; and based on personal permissions, they can view and download the ophthalmic examination report image or send the report to other medical staff, patients or platforms through the built-in sharing function.

[0040] Furthermore, the simulation processing unit includes:

[0041] The model building module is configured to obtain an ophthalmological examination report image processed by a quantum annealing optimization algorithm for use in building a quantum simulation model; convert the image into a quantum Hamiltonian form as an input set for the quantum simulation model; and then set parameters of the quantum simulation model, including the number of quantum bits and the number of iterations;

[0042] A simulation processing module is configured to run a quantum simulation model to quickly analyze the input image set and extract key features, including lesion area or vascular distribution; and then convert the quantum simulation results into classical data, including feature vectors or image labels;

[0043] The result evaluation module is configured to evaluate the processing results using image quality assessment indicators. After ensuring that the processing effect is correct, it is output to the image sharing platform for ophthalmologists to check and use.

[0044] Furthermore, the security management unit includes:

[0045] An access control module is configured to define different roles according to the responsibilities of medical staff and assign corresponding permissions to each role;

[0046] The identity authentication module is configured to verify the identity and permissions of the current person when the medical staff logs into the image sharing platform based on the account and password authentication method; if the permissions are verified, the person is granted permission to use the image sharing platform; if the permissions are not verified, access is denied and a log is recorded;

[0047] The logging module is configured to record the operation logs of all users, regularly audit access records, detect and handle abnormal behaviors, and ensure traceability.

[0048] Furthermore, the image sharing platform further includes:

[0049] A human-computer interaction interface is configured to provide doctors and patients with an intuitive operation interface in a two-dimensional operation plane, facilitating viewing of ophthalmological examination report images, analysis results, and treatment recommendations;

[0050] The performance optimization unit is configured to regularly optimize the image sharing platform through parallel computing and efficient algorithms, so as to improve the overall performance of the image sharing platform and adapt to large-scale data processing needs.

[0051] Furthermore, the image collection unit includes:

[0052] Information acquisition module, configured as follows:

[0053] When uploading an ophthalmological examination report image to the image sharing platform, obtaining the total number of uploaded ophthalmological examination report images and image information of each ophthalmological examination report image;

[0054] Obtaining the total number of ophthalmological examination report images received and real-time image information of each ophthalmological examination report image received based on the image sharing platform;

[0055] a first calculation module configured to calculate a target similarity between image information of each ophthalmological examination report image and real-time image information of each ophthalmological examination report image received by the image sharing platform;

[0056] Accurately judge the module, and configure it as follows:

[0057] Obtaining a preset similarity threshold, and comparing the target similarity with the preset similarity threshold to determine whether the ophthalmological examination report image received by the image sharing platform is accurate;

[0058] When the target similarity is less than or equal to the preset similarity threshold, it is determined that the ophthalmological examination report image received by the image sharing platform is inaccurate;

[0059] Otherwise, it is determined that the ophthalmological examination report image received by the image sharing platform is accurate;

[0060] According to the judgment results, the accurate number of ophthalmological examination report images received by the image sharing platform is extracted;

[0061] a second calculation module configured to calculate an upload accuracy rate of uploading the ophthalmic examination report image to the image sharing platform based on a total number of uploads of the ophthalmic examination report image, a total number of receipts of the ophthalmic examination report image by the image sharing platform, and a number of accurate receipts of the ophthalmic examination report image by the image sharing platform;

[0062] Upload the qualified judgment module, and configure it as follows:

[0063] Obtaining a preset accuracy threshold, and comparing the upload accuracy of the ophthalmic examination report image uploaded to the image sharing platform with the preset accuracy threshold to determine whether the ophthalmic examination report image uploaded to the image sharing platform is qualified;

[0064] If the upload accuracy of the ophthalmic examination report image uploaded to the image sharing platform is equal to or greater than the preset accuracy threshold, it is determined that uploading the ophthalmic examination report image to the image sharing platform is qualified;

[0065] Otherwise, uploading the ophthalmological examination report image to the image sharing platform is deemed unqualified;

[0066] The alarm module is configured to perform an alarm operation if the ophthalmic examination report image uploaded to the image sharing platform is unqualified.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] In the present invention, by constructing an image sharing platform, ophthalmic examination report images can be centrally stored and managed based on the platform; quantum algorithms are used to encrypt and protect images and optimize them to improve image standardization, thereby preventing misdiagnosis by doctors with less clinical experience and ensuring data security and privacy; and quantum simulation models are used to intelligently analyze the stored ophthalmic examination report images to assist doctors in making rapid diagnoses of the reports, thereby improving diagnostic accuracy and consultation speed and shortening the waiting time for subsequent patients; and by setting a role-based access control mechanism, it is ensured that various users can only access data within their authority, preventing unauthorized access and improving the confidentiality and practicality of the image sharing platform; based on this, by introducing quantum simulation technology, the shared ophthalmic examination report image management system will be more intelligent and efficient.

[0069] In the present invention, by performing element splitting on the collected ophthalmic examination report images, a multi-element report image set is locked. At the same time, an ophthalmic examination report image template is retrieved, and the multi-element report image set is positioned in the retrieved ophthalmic examination report image template, thereby verifying the correspondence between a first image component element in the ophthalmic examination report image and a second image component element in the ophthalmic examination report image template according to the positioning result. Secondly, when the two do not match, the ophthalmic examination report image is collected again, and when the two match, the target matching degree between each element report diagram in the multi-element report atlas and the corresponding element template report diagram in the multi-element template report atlas is determined, thereby achieving accurate and effective judgment on whether the element report diagram in the element report atlas set is qualified based on the size relationship between the matching degree and the matching degree threshold. Finally, when the element report diagram is unqualified, the element template report diagram is corrected until it meets the element template report diagram, and finally, the qualified ophthalmic examination report image is uploaded to the image sharing platform, thereby achieving strict and effective testing of the ophthalmic examination report image, and ensuring the rigor, reliability and accuracy of the finally obtained ophthalmic examination report image.

[0070] In the present invention, by reading the total number of uploads of ophthalmic examination report images and the image information of each ophthalmic examination report image and the total number of receptions of ophthalmic examination report images accurately obtained based on the image platform and the real-time image information of each ophthalmic examination report image received, the target similarity is effectively calculated, which is conducive to accurately obtaining the accurate number of receptions of ophthalmic examination report images received by the image sharing platform, and thus accurately calculating the upload accuracy of uploading the ophthalmic examination report images to the image sharing platform according to the total number of uploads of ophthalmic examination report images, the total number of receptions of ophthalmic examination report images by the image sharing platform and the accurate number of receptions of ophthalmic examination report images by the image sharing platform, and then effectively determining whether the ophthalmic examination report images uploaded to the image sharing platform are qualified, effectively ensuring the grasp of the accuracy of the uploaded ophthalmic examination report images, thereby providing reliable data guarantee for the subsequent management and simulation of ophthalmic examination report images, and improving the accuracy of ophthalmic examination report image management. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a diagram showing the modules of the shared ophthalmic examination report image management system of the present invention. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0073] To solve the technical problem that the system used by a hospital is not running on the doctor's workstation in the clinic, and outpatients often forget to bring their previous eye examination results; as a result, doctors need to check the corresponding machine during the consultation, which not only increases the doctor's clinical diagnosis process, but also delays the waiting time of subsequent patients, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0074] A shared ophthalmic examination report image management system includes an image sharing platform that supports uploading and downloading of image files in various formats, making it convenient for users to access and use them at any time. The image sharing platform includes an image collection unit, a quantum management unit, a security management unit, a simulation processing unit, a human-computer interaction interface, and a performance optimization unit.

[0075] The image collection unit is configured to collect ophthalmic examination report images in a two-dimensional operating plane and upload the ophthalmic examination report images to an image sharing platform for centralized storage and management. Specifically, in this embodiment, the image collection unit is connected to the hospital's ophthalmic examination equipment through a port to obtain the ophthalmic examination report images of each patient and upload the images to the image sharing platform based on wireless transmission technology. The image collection unit is also used to collect historical ophthalmic examination report images, and perform pre-processing such as cleaning and denoising on them, and use them as data samples for training and testing the quantum simulation model to ensure the performance of the model.

[0076] The quantum management unit is configured to use a quantum key distribution protocol and a quantum one-time pad encryption algorithm to encrypt and store ophthalmic examination report images to ensure data security and privacy; and uses a quantum annealing optimization algorithm to optimize the quality of ophthalmic examination report images and generate standardized images with reduced noise and distortion. The quantum management unit includes:

[0077] The environment deployment module is configured to deploy quantum key distribution devices and quantum storage databases in the image sharing platform. The quantum key distribution devices include: QKD transmitters and receivers. Specifically, the QKD transmitters and receivers generate and distribute keys through quantum mechanics principles, such as quantum entanglement or quantum state non-cloning, to ensure that the keys cannot be eavesdropped or cracked during transmission. The keys generated by the QKD transmitters and receivers are random and unpredictable, and can effectively resist attacks faced by traditional encryption technologies, such as brute force or quantum computing attacks. Secondly, the quantum one-time pad encryption algorithm library and image processing tools are integrated into the image sharing platform to support quantum key generation and image encryption operations, ensuring the security of data during transmission and storage. Specifically, the quantum one-time pad encryption algorithm library uses the keys generated by the QKD transmitter and receiver to encrypt image data, ensuring that each data block uses a unique key, ensuring that the performance of the image sharing platform is not affected while ensuring security.

[0078] The key distribution module is configured to use a quantum key distribution protocol, such as the BB84 protocol, to generate a shared quantum key between a sender, such as a hospital server, and a receiver, such as a cloud storage system. 、 、 、 ; Quantum bits are transmitted through quantum channels, such as optical fibers. Optical fibers can efficiently transmit quantum bits. At the same time, quantum relay technology can extend the transmission distance, thereby ensuring the security of key distribution.

[0079] The image encryption module is configured to first pre-process the ophthalmic examination report image, including format conversion, such as JPEG and PNG to RAW, to reduce data complexity; then convert the ophthalmic examination report image into a binary data stream, that is, each pixel value is converted into an 8-bit binary number to form a continuous bit sequence for encryption processing; and then block the ophthalmic examination report image to meet the requirements of the quantum one-time pad encryption algorithm; use the generated quantum key to encrypt the ophthalmic examination report image bit by bit, that is, divide the binary data stream into fixed-size data blocks, such as 128 bits or 256 bits, and the block size can be adjusted according to actual needs; if the last data block is less than the fixed size, use a padding algorithm, such as PKCS7, to ensure that all data blocks are of the same size; generate a quantum key with the same length as the data block through the quantum key distribution protocol, namely the BB84 protocol, and then perform an XOR operation on each bit of the data block with the corresponding bit of the key to generate ciphertext, so that the encryption key for each data block is different, ensuring the high security of the encryption process and the uniqueness of each bit encryption key.

[0080] The image storage module is configured to recombine the encrypted ciphertext data blocks into a complete binary data stream, and then store the encrypted ophthalmic examination report image in the quantum storage database to support efficient retrieval and management, thereby ensuring the high security of image data during transmission and storage.

[0081] Image storage module, including:

[0082] The identification construction module is configured to set identification information for each patient, including: patient ID, name, ID number or medical record number; and set filtering conditions based on each patient's medical records, including: examination date and examination type, so as to narrow the search scope.

[0083] The sharing module is configured so that medical staff can select the ophthalmic examination report image they need to retrieve from the search results; and use the same quantum key to decrypt the ciphertext bit by bit to restore the original image data. The images include: fundus images, OCT images and text reports; and based on personal permissions, they can view and download the ophthalmic examination report image or send the report to other medical staff, patients or platforms through the built-in sharing function.

[0084] The preprocessing module is configured to convert the color image of the eye examination report into a grayscale image and use Gaussian filtering image processing technology to preliminarily detect noise in the image, thereby simplifying subsequent processing steps.

[0085] The conditional construction module is configured to transform the image quality optimization problem into an objective function, where the objective function is: minimizing the noise and distortion in the image, that is, reducing the noise by minimizing the variance or gradient amplitude of the pixel values ​​in the image, and reducing the distortion by minimizing the difference between the original image and the optimized image; using the pixel value or local area of ​​the image as the optimization variable, if the pixel value can be directly adjusted through each pixel value to reduce the noise and distortion; setting constraints according to the image characteristics, the constraints include: the range of pixel values ​​and local smoothness, constraining the optimized pixel values ​​to be within a reasonable range, such as: 0 to 255, to avoid image distortion caused by over-adjustment; constraining the difference between adjacent pixel values ​​to ensure the smoothness of the local area of ​​the image and avoid artifacts or over-sharpening.

[0086] The quantum annealing module is configured to map the objective function and constraints into the Hamiltonian of a quantum annealer, and use a quantum annealer, such as a D-Wave system, to solve the problem. The optimal pixel value or local area is found in the solution space through the quantum tunneling effect and thermal annealing process. The quantum annealer gradually evolves the quantum state at a low temperature and eventually converges to the minimum value of the objective function, that is, the optimal pixel value or local area. The image quality is gradually optimized through multiple iterations. That is, the image quality is gradually optimized by running the quantum annealer multiple times. After each iteration, the objective function or constraints are adjusted according to the optimization results to further improve the image quality. At the same time, convergence conditions are also set, such as when the change in the objective function value is less than a threshold. When the condition is met, the iteration is stopped and the final optimization result is output.

[0087] The post-processing module is configured to denoise and enhance the image based on the quantum annealing optimization results to improve the image clarity and contrast; and convert the optimized image into a standardized format that meets medical diagnosis requirements, thereby assisting doctors with less clinical experience to make accurate judgments and reduce misdiagnosis.

[0088] The security management unit is configured to set up medical authority management and identity authentication mechanisms. If the medical authority is verified, the medical authority is granted access to the image sharing platform; otherwise, the medical authority is denied access. The security management unit includes:

[0089] The access control module is configured to define different roles according to the responsibilities of medical staff and assign corresponding permissions to each role. In this embodiment, the roles are such as doctors, nurses, administrators, etc., and the permissions are assigned as follows: doctors can view, download and share encrypted images; nurses can view encrypted images but cannot download or share them; administrators can manage user permissions and system settings. By setting up a role-based access control mechanism, it is ensured that each type of user can only access data within their permission range to prevent unauthorized access.

[0090] The identity authentication module is configured to verify the identity and permissions of the current person when the medical staff logs into the image sharing platform based on the account and password authentication method; if the permissions are verified, they are granted permission to use the image sharing platform; if the permissions are not verified, access is denied and a log is recorded.

[0091] The logging module is configured to record the operation logs of all users, regularly audit access records, detect and handle abnormal behaviors, and ensure their traceability.

[0092] A simulation processing unit is configured to embed a quantum simulation model in the image sharing platform, rapidly analyze ophthalmic examination report images through the quantum simulation model, and provide personalized treatment recommendations based on the analysis results to assist doctors in making accurate diagnoses. The simulation processing unit includes:

[0093] The model building module is configured to obtain an ophthalmological examination report image processed by a quantum annealing optimization algorithm for use in building a quantum simulation model; and convert the image into a quantum Hamiltonian form as an input set for the quantum simulation model; the quantum Hamiltonian is usually expressed in a matrix form, describing the energy state of the quantum system; that is, mapping the pixel values ​​or local areas of the image to the state of the quantum bit, such as: or , based on the statistical characteristics of the image, such as gradient and texture, the matrix elements of the Hamiltonian are constructed; then the parameters of the quantum simulation model are set, including the number of quantum bits and the number of iterations; the number of quantum bits of the quantum simulation model is set according to the complexity of the image and the simulation requirements; the number of iterations is used to control the convergence and calculation time of the simulation process. The more iterations, the more accurate the simulation results.

[0094] The simulation processing module is configured to run a quantum simulation model to quickly analyze the images of the input set and extract key features therein, including: lesion areas or blood vessel distribution; then convert the quantum simulation results into classical data, including: feature vectors or image labels; specifically, the behavior of the image in the quantum system is simulated through quantum state evolution, and the state of the quantum bit evolves over time to reflect the key features of the image; and by analyzing the high-probability areas in the quantum simulation results, the lesion areas in the image are identified; for example, the lesion areas may correspond to high-probability values ​​of certain specific bits in the quantum state; for example, the entanglement characteristics in the quantum simulation results are used to analyze the blood vessel distribution in the image; the entangled state reflects the correlation between different areas in the image, thereby extracting the topological structure of the blood vessels.

[0095] The result evaluation module is configured to use image quality assessment indicators such as PSNR and SSIM to evaluate the processing results. After ensuring the processing effect is correct, it is output to the image sharing platform for ophthalmologists to check and use, thereby improving the storage and sharing efficiency of ophthalmic examination reports, reducing repeated examinations, and improving diagnostic accuracy.

[0096] The human-computer interaction interface is configured to provide doctors and patients with an intuitive operation interface in a two-dimensional operation plane, facilitating viewing of ophthalmic examination report images, analysis results, and treatment recommendations.

[0097] The performance optimization unit is configured to regularly optimize the image sharing platform through parallel computing and efficient algorithms, so as to improve the overall performance of the image sharing platform and adapt to large-scale data processing needs.

[0098] The beneficial effects achieved by the above content: By introducing quantum simulation technology, the shared ophthalmic examination report image management system will be more intelligent and efficient.

[0099] Working principle: By building an image sharing platform, ophthalmic examination report images can be centrally stored and managed based on the platform; quantum algorithms are used to encrypt and optimize images to improve image standardization, thereby preventing misdiagnosis by doctors with less clinical experience, while ensuring data security and privacy; and quantum simulation models are used to perform intelligent analysis of stored ophthalmic examination report images to assist doctors in making rapid diagnoses of the reports, thereby improving diagnostic accuracy and consultation speed, and shortening the waiting time for subsequent patients; and by setting up a role-based access control mechanism, it is ensured that various users can only access data within their authority, preventing unauthorized access and improving the confidentiality and practicality of the image sharing platform.

[0100] In one embodiment, a shared ophthalmological examination report image management system is provided, wherein the image collection unit comprises:

[0101] an atlas acquisition module configured to read the collected ophthalmological examination report image, determine a first image element composition of the ophthalmological examination report image, and divide the ophthalmological examination report image into a multi-element report atlas according to the first image element composition;

[0102] a positioning module configured to retrieve an ophthalmic examination report image template, locate the image template in the ophthalmic examination report image template according to the first image component element, and simultaneously obtain a second image component element of the ophthalmic examination report image template;

[0103] The judgment processing module is configured as follows:

[0104] Determining whether the first image component element corresponds to the second image component element one-to-one according to the positioning result;

[0105] When there is no one-to-one correspondence between the first image component element and the second image component element, the collected ophthalmic examination report image is annotated, and the ophthalmic examination report image is re-collected according to the annotated result;

[0106] When the first image component elements correspond to the second image component elements one-to-one, dividing the ophthalmological examination report image template into a multi-element template report atlas based on the second image component elements;

[0107] A matching module is configured to match the multi-element report atlas with the multi-element template report atlas, and determine a target matching degree between each element report diagram in the multi-element report atlas and a corresponding element template report diagram in the multi-element template report atlas;

[0108] The qualification judgment module is configured as follows:

[0109] Obtaining a matching degree threshold, and comparing the target matching degree with the matching degree threshold to determine whether the element report diagram in the element report diagram set is qualified;

[0110] When the target matching degree is equal to or greater than the matching degree threshold, the element report diagram is judged to be qualified;

[0111] Otherwise, the element report diagram is judged to be unqualified;

[0112] a correction module configured to, when an element report diagram is unqualified, read an element template report diagram corresponding to the unqualified element report diagram, locate a correction point in the unqualified element report diagram based on the element template report diagram, and correct the unqualified element report diagram according to the correction point and the element template report diagram until it conforms to the element template report diagram;

[0113] The uploading module is configured to upload the collected ophthalmic examination report images to the image sharing platform when all the multi-element template report atlases are qualified.

[0114] In this embodiment, the first image element composition refers to a collection of different types or visual objects contained in the ophthalmological examination report image, such as an eyeball image, an eye examination item description, and an eye examination result.

[0115] In this embodiment, the multi-element report atlas refers to the result obtained by dividing the ophthalmological examination report image according to the obtained first image elements, and each category of image elements is an element report image.

[0116] In this embodiment, positioning in the ophthalmological examination report image template according to the first image component element refers to matching the corresponding element filling position from the ophthalmological examination report image template according to features such as visualization features or structure of the first image component element.

[0117] In this embodiment, the second image component element refers to a collection of different types or visual objects included in the ophthalmological examination report image template.

[0118] In this embodiment, the multi-element template report atlas refers to the result obtained by dividing the ophthalmological examination report image template according to the second image component elements.

[0119] In this embodiment, the target matching degree refers to the degree of adaptation between each element report diagram in the multi-element report diagram set and the corresponding element template report diagram in the multi-element template report diagram set. The larger the value, the more adapted the two are.

[0120] In this embodiment, the matching degree threshold is set in advance and is the minimum standard for measuring whether the target matching degree meets the requirements and can be adjusted.

[0121] In this embodiment, an unqualified element report diagram refers to an element report diagram that has no element template report diagram matching it.

[0122] In this embodiment, the correction point refers to an image point that needs to be modified or adjusted in the unqualified element report image.

[0123] The working principle and beneficial effects of the above technical solution are: by performing element splitting on the collected ophthalmic examination report image, the multi-element report image set is locked, and at the same time, the ophthalmic examination report image template is retrieved, and the multi-element report image set is positioned in the retrieved ophthalmic examination report image template, so as to realize the correspondence verification between the first image component element in the ophthalmic examination report image and the second image component element in the ophthalmic examination report image template according to the positioning result, and secondly, when the two do not match, the ophthalmic examination report image is collected again, and when the two match, the multi-element report is determined. The target matching degree between each element report diagram in the atlas and the corresponding element template report diagram in the multi-element template report atlas is achieved, so that the element report diagram in the element report atlas is accurately and effectively judged whether it is qualified according to the size relationship between the matching degree and the matching degree threshold. Finally, when the element report diagram is unqualified, the element template report diagram is corrected until it meets the element template report diagram, and finally the qualified ophthalmic examination report image is uploaded to the image sharing platform, and the ophthalmic examination report image is strictly and effectively tested, ensuring the rigor, reliability and accuracy of the final ophthalmic examination report image.

[0124] In one embodiment, a shared ophthalmological examination report image management system is provided, wherein the image collection unit includes:

[0125] Information acquisition module, configured as follows:

[0126] When uploading an ophthalmological examination report image to the image sharing platform, obtaining the total number of uploaded ophthalmological examination report images and image information of each ophthalmological examination report image;

[0127] Obtaining the total number of ophthalmological examination report images received and real-time image information of each ophthalmological examination report image received based on the image sharing platform;

[0128] a first calculation module configured to calculate a target similarity between image information of each ophthalmological examination report image and real-time image information of each ophthalmological examination report image received by the image sharing platform;

[0129] ;

[0130] in, Represents the target similarity between the image information of the ophthalmological examination report image and the real-time image information of the corresponding ophthalmological examination report image received by the image sharing platform; Represents an image of an eye examination report before uploading; Indicates that the image sharing platform has received the corresponding real-time ophthalmological examination report image; Indicates the length of the ophthalmological examination report image, and is consistent with the length of the corresponding real-time ophthalmological examination report image received by the image sharing platform; Indicates the width of the ophthalmological examination report image, and is consistent with the width of the corresponding real-time ophthalmological examination report image received by the image sharing platform; Indicates the pixel coordinates of the eye examination report image before uploading; Indicates the horizontal coordinate point; Indicates the vertical coordinate point; Indicates the pixel coordinate point of the corresponding real-time ophthalmological examination report image received by the image sharing platform;

[0131] Accurately judge the module, and configure it as follows:

[0132] Obtaining a preset similarity threshold, and comparing the target similarity with the preset similarity threshold to determine whether the ophthalmological examination report image received by the image sharing platform is accurate;

[0133] When the target similarity is less than or equal to the preset similarity threshold, it is determined that the ophthalmological examination report image received by the image sharing platform is inaccurate;

[0134] Otherwise, it is determined that the ophthalmological examination report image received by the image sharing platform is accurate;

[0135] According to the judgment results, the accurate number of ophthalmological examination report images received by the image sharing platform is extracted;

[0136] a second calculation module configured to calculate an upload accuracy rate of uploading the ophthalmic examination report image to the image sharing platform based on a total number of uploads of the ophthalmic examination report image, a total number of receipts of the ophthalmic examination report image by the image sharing platform, and a number of accurate receipts of the ophthalmic examination report image by the image sharing platform;

[0137] ;

[0138] in, Indicates the upload accuracy of ophthalmological examination report images uploaded to the image sharing platform; Indicates the total number of ophthalmology examination report images received by the image sharing platform; represents the total number of eye exam report images uploaded, and ; Indicates the impact factor of the total number of uploads and the total number of receives on the upload accuracy, and the value range is (0.9, 1); Indicates the exact number of ophthalmology examination report images received by the image sharing platform; Indicates the influence factor of the total number of received and the number of accurate received on the upload accuracy, and the value range is (0.9, 1);

[0139] Upload the qualified judgment module, and configure it as follows:

[0140] Obtaining a preset accuracy threshold, and comparing the upload accuracy of the ophthalmic examination report image uploaded to the image sharing platform with the preset accuracy threshold to determine whether the ophthalmic examination report image uploaded to the image sharing platform is qualified;

[0141] If the upload accuracy of the ophthalmic examination report image uploaded to the image sharing platform is equal to or greater than the preset accuracy threshold, it is determined that uploading the ophthalmic examination report image to the image sharing platform is qualified;

[0142] Otherwise, uploading the ophthalmological examination report image to the image sharing platform is deemed unqualified;

[0143] The alarm module is configured to perform an alarm operation if the ophthalmic examination report image uploaded to the image sharing platform is unqualified.

[0144] In this embodiment, the image information and real-time image information of the ophthalmological examination report image include the size of the image, pixels, and the horizontal and vertical coordinates of the pixels.

[0145] In this embodiment, the preset similarity threshold is set in advance and is used as a criterion for determining whether the ophthalmic examination report image received by the image sharing platform is accurate.

[0146] In this embodiment, the preset accuracy threshold is set in advance and is used as a criterion for determining whether the ophthalmic examination report image uploaded to the image sharing platform is qualified.

[0147] In this embodiment, the alarm operation includes but is not limited to sending an alarm text message, sound, light, etc.

[0148] The working principle and beneficial effects of the above technical solution are: by reading the total number of uploads of ophthalmic examination report images and the image information of each ophthalmic examination report image and the total number of receptions of ophthalmic examination report images accurately obtained based on the image platform and the real-time image information of each ophthalmic examination report image, the target similarity is effectively calculated, which is conducive to accurately obtaining the accurate number of receptions of ophthalmic examination report images received by the image sharing platform, and then accurately calculating the upload accuracy of uploading the ophthalmic examination report images to the image sharing platform based on the total number of uploads of ophthalmic examination report images, the total number of receptions of ophthalmic examination report images by the image sharing platform and the accurate number of receptions of ophthalmic examination report images received by the image sharing platform, and then effectively determining whether the ophthalmic examination report images uploaded to the image sharing platform are qualified, effectively ensuring the grasp of the accuracy of the uploaded ophthalmic examination report images, thereby providing reliable data guarantee for the subsequent management and simulation of ophthalmic examination report images, and improving the accuracy of ophthalmic examination report image management.

[0149] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "including," "having," or any other variations thereof are intended to cover non-exclusive possessors, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or includes elements that are inherent to such process, method, article, or apparatus.

[0150] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions, and alterations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A shared ophthalmic examination report image management system, characterized in that: The system includes: an image sharing platform, which includes an image collection unit and a quantum management unit; The image collection unit is configured to collect ophthalmic examination report images and upload them to an image sharing platform for centralized storage and management. In the process of collecting the report images, the unit includes: matching image elements with element templates, and comparing the matching degree with a matching degree threshold to determine whether the report images are qualified; calculating the upload accuracy rate based on the total number of report images uploaded, the total number of received, and the number of accurately received images, to determine whether the uploaded report images are qualified; The quantum management unit is configured to encrypt and store the ophthalmic examination report image using a quantum key distribution protocol and a quantum one-time pad encryption algorithm; and to optimize the ophthalmic examination report image using a quantum annealing optimization algorithm to generate a standardized image; The quantum management unit comprises: An environment deployment module is configured to deploy a quantum key distribution device and a quantum storage database in the image sharing platform. The quantum key distribution device includes a QKD transmitter and receiver. The quantum one-time pad encryption algorithm library and image processing tools are integrated into the image sharing platform to support quantum key generation and image encryption operations, ensuring the security of data during transmission and storage. a key distribution module configured to use a quantum key distribution protocol to generate a shared quantum key between a sender and a receiver and transmit quantum bits through a quantum channel; an image encryption module configured to convert the ophthalmic examination report image into a binary data stream and perform block processing on the ophthalmic examination report image; and encrypt the ophthalmic examination report image bit by bit using the generated quantum key to ensure the uniqueness of each bit encryption key; an image storage module configured to store the encrypted ophthalmological examination report image in a quantum storage database to support efficient retrieval and management; a conditional building module configured to transform the image quality optimization problem into an objective function, wherein the objective function is to minimize noise and distortion in the image; use pixel values ​​or local regions of the image as optimization variables, and set constraints based on image characteristics, wherein the constraints include a range of pixel values ​​and local smoothness; A quantum annealing module is configured to map the objective function and constraints into the Hamiltonian of a quantum annealer, use the quantum annealer to solve the problem, find the optimal pixel value or local area, and gradually optimize the image quality through multiple iterations; a preprocessing module configured to convert the color image of the eye examination report into a grayscale image and preliminarily detect noise in the image using a Gaussian filter image processing technique; The post-processing module is configured to perform denoising and enhancement processing on the image according to the quantum annealing optimization result; and convert the optimized image into a standardized format that meets the requirements of medical diagnosis.

2. The shared ophthalmic examination report image management system according to claim 1, characterized in that: The image collection unit comprises: an atlas acquisition module configured to read the collected ophthalmological examination report image, determine a first image element composition of the ophthalmological examination report image, and divide the ophthalmological examination report image into a multi-element report atlas according to the first image element composition; a positioning module configured to retrieve an ophthalmic examination report image template, locate the image template in the ophthalmic examination report image template according to the first image component element, and simultaneously obtain a second image component element of the ophthalmic examination report image template; The judgment processing module is configured as follows: Determining whether the first image component element corresponds to the second image component element one-to-one according to the positioning result; When there is no one-to-one correspondence between the first image component element and the second image component element, the collected ophthalmic examination report image is annotated, and the ophthalmic examination report image is re-collected according to the annotated result; When the first image component elements correspond to the second image component elements one-to-one, dividing the ophthalmological examination report image template into a multi-element template report atlas based on the second image component elements; The matching module is configured to match the multi-element report atlas with the multi-element template report atlas, and determine the target matching degree between each element report diagram in the multi-element report atlas and the corresponding element template report diagram in the multi-element template report atlas.

3. The shared ophthalmic examination report image management system according to claim 2, characterized in that: The image collection unit further includes: The qualification judgment module is configured as follows: Obtaining a matching degree threshold, and comparing the target matching degree with the matching degree threshold to determine whether the element report diagram in the element report diagram set is qualified; When the target matching degree is equal to or greater than the matching degree threshold, the element report diagram is judged to be qualified; Otherwise, the element report diagram is judged to be unqualified; a correction module configured to, when an element report diagram is unqualified, read an element template report diagram corresponding to the unqualified element report diagram, locate a correction point in the unqualified element report diagram based on the element template report diagram, and correct the unqualified element report diagram according to the correction point and the element template report diagram until it conforms to the element template report diagram; The uploading module is configured to upload the collected ophthalmic examination report images to the image sharing platform when all the multi-element template report atlases are qualified.

4. The shared ophthalmic examination report image management system according to claim 3, characterized in that: The image collection unit further includes: Information acquisition module, configured as follows: When uploading an ophthalmological examination report image to the image sharing platform, obtaining the total number of uploaded ophthalmological examination report images and image information of each ophthalmological examination report image; Acquire the total number of received ophthalmic examination report images and real-time image information of each received ophthalmic examination report image based on the image sharing platform; a first calculation module configured to calculate a target similarity between image information of each ophthalmological examination report image and real-time image information of each ophthalmological examination report image received by the image sharing platform; Accurately judge the module, and configure it as follows: Obtaining a preset similarity threshold, and comparing the target similarity with the preset similarity threshold to determine whether the ophthalmological examination report image received by the image sharing platform is accurate; When the target similarity is less than or equal to the preset similarity threshold, it is determined that the ophthalmological examination report image received by the image sharing platform is inaccurate; Otherwise, it is determined that the ophthalmological examination report image received by the image sharing platform is accurate; According to the judgment results, the accurate number of ophthalmological examination report images received by the image sharing platform is extracted.

5. The shared ophthalmic examination report image management system according to claim 4, characterized in that: The image collection unit further includes: a second calculation module configured to calculate an upload accuracy rate of uploading the ophthalmic examination report image to the image sharing platform based on a total number of uploads of the ophthalmic examination report image, a total number of receipts of the ophthalmic examination report image by the image sharing platform, and a number of accurate receipts of the ophthalmic examination report image by the image sharing platform; Upload the qualified judgment module, and configure it as follows: Obtaining a preset accuracy threshold, and comparing the upload accuracy of the ophthalmic examination report image uploaded to the image sharing platform with the preset accuracy threshold to determine whether the ophthalmic examination report image uploaded to the image sharing platform is qualified; If the upload accuracy of the ophthalmic examination report image uploaded to the image sharing platform is equal to or greater than the preset accuracy threshold, it is determined that uploading the ophthalmic examination report image to the image sharing platform is qualified; Otherwise, uploading the ophthalmological examination report image to the image sharing platform is judged to be unqualified; The alarm module is configured to perform an alarm operation if the ophthalmic examination report image uploaded to the image sharing platform is unqualified.

6. The shared ophthalmic examination report image management system according to claim 1, characterized in that: The image storage module includes: The identification building module is configured to set identification information for each patient, including: patient ID, name, ID number or medical record number; and set filtering conditions based on each patient's medical records, including: examination date and examination type, to narrow the search scope; The sharing module is configured so that medical staff can select the ophthalmic examination report image they need to retrieve from the search results; and use the same quantum key to decrypt the ciphertext bit by bit to restore the original image data. The images include: fundus images, OCT images and text reports; and based on personal permissions, they can view and download the ophthalmic examination report image or send the report to other medical staff, patients or platforms through the built-in sharing function.

7. The shared ophthalmic examination report image management system according to claim 1, characterized in that: The image sharing platform further includes: a simulation processing unit configured to embed a quantum simulation model in the image sharing platform, analyze the ophthalmological examination report image, and provide personalized treatment recommendations based on the analysis results; the simulation processing unit includes: The model building module is configured to obtain an ophthalmological examination report image processed by a quantum annealing optimization algorithm for use in building a quantum simulation model; convert the image into a quantum Hamiltonian form as an input set for the quantum simulation model; and then set parameters of the quantum simulation model, including the number of quantum bits and the number of iterations; A simulation processing module is configured to run a quantum simulation model to quickly analyze the input image set and extract key features, including lesion area or vascular distribution; and then convert the quantum simulation results into classical data, including feature vectors or image labels; The result evaluation module is configured to evaluate the processing results using image quality assessment indicators. After ensuring that the processing effect is correct, it is output to the image sharing platform for ophthalmologists to check and use.

8. The shared ophthalmic examination report image management system according to claim 1, characterized in that: The image sharing platform further includes: a security management unit configured to set up medical authority management and identity authentication mechanisms; the security management unit includes: An access control module is configured to define different roles according to the responsibilities of medical staff and assign corresponding permissions to each role; The identity authentication module is configured to verify the identity and authority of the current person when the medical staff logs into the image sharing platform based on the account and password authentication method; The logging module is configured to record the operation logs of all users, regularly audit access records, and detect and handle abnormal behaviors.

9. The shared ophthalmic examination report image management system according to claim 1, characterized in that: The image sharing platform further includes: A human-computer interaction interface configured to provide an intuitive operation interface for doctors and patients in a two-dimensional operation plane; A performance optimization unit is configured to regularly perform system optimization on the image sharing platform through parallel computing and efficient algorithms.

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