A medical image intelligent management method and system for cloud medical workstation

Abstract

The present invention relates to the technical field of intelligent management of medical images, and is a method and system for intelligent management of medical images for a cloud medical workstation. The specific method comprises: collecting personal information data and diagnosis and treatment data of patients who have daily online consultations; screening and matching corresponding medical images according to the personal information data of the patients, and performing interpolation and completion processing on abnormal pixel points in the medical images; building a cloud medical workstation database, and clustering and classifying the medical images in the cloud medical workstation and labeling them; the present invention solves the problem in the prior art that the cloud medical image management platform has poor protection of patient privacy data.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of medical images, and is a method and system for intelligent management of medical images for a cloud medical workstation. Background Art

[0002] Remote auscultation online medical care refers to medical services provided by medical service providers to patients through the Internet and communication technology. Patients can communicate with doctors remotely through mobile phones, computers and other devices to obtain diagnosis, prescriptions, consultation and other services. Patients do not need to go to medical institutions, saving time and transportation costs, especially for patients living in remote areas or with limited mobility. In addition, patients can communicate with doctors anytime and anywhere to obtain timely medical advice and diagnosis, which helps to detect and treat diseases early. Remote auscultation online medical care can also better allocate medical resources, reduce the pressure on medical institutions, and alleviate the problem of uneven medical resources. How to establish a workstation platform that can protect patient privacy data and enable doctors to achieve accurate diagnosis and treatment through medical images stored on online platforms is a difficult problem that needs to be solved in the industry.

[0003] The existing technology has a weak purpose in processing the huge amount of medical imaging data in the cloud medical platform. Most of the patients who use the cloud medical platform for a long time and frequently are patients who are diagnosed and treated for long-term diseases. The cloud medical platform in the existing technology lacks complex monitoring of the improvement of the disease, and there are few cloud medical platforms that implement different medical imaging management strategies based on the length of the patient's disease course.

[0004] In the existing disclosed invention technologies, for example, the patent with publication number CN117316392A discloses a medical imaging information management system and method based on smart medical care, which includes the following steps: collecting and storing medical imaging information, and establishing a medical imaging cloud database; obtaining target imaging information corresponding to the patient's basic information based on the medical imaging cloud database; determining a licensed medical institution based on the target imaging information, and the patient determines a target medical institution based on the licensed medical institution; reading the specific content of the target imaging information at the target medical institution based on the patient's basic information, and the medical staff of the target medical institution diagnoses the patient's condition based on the specific content of the target imaging information to obtain a diagnosis result for the patient.

[0005] The above patent targets the personal health data and privacy data in the patient's personal medical imaging information, and the protection mechanism is weak, posing the risk of data tampering and leakage. At the same time, it fails to take into account that the basis for patients to choose target medical institutions may not only be medical imaging information, but also include personal preferences, medical experience and other factors. Summary of the invention

[0006] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0007] The technical problem to be solved by the present invention is that in the prior art, the cloud medical image management platform has poor protection for patient privacy data and weak purpose in processing huge medical image data. A medical image intelligent management method and system for a cloud medical workstation are proposed.

[0008] In order to achieve the above-mentioned object, the technical solution of a medical image intelligent management method for a cloud medical workstation of the present invention comprises the following steps:

[0009] S1: Collect and store the personal information data of patients who have online consultations every day and the diagnosis and treatment data of online consultations;

[0010] S2: Access the medical image data storage backend of each hospital that has joined the Cloud Medical Workstation through the Cloud Medical Workstation, select and match the corresponding medical images according to the patient's personal information data, and perform interpolation and completion processing on abnormal pixels in the medical images;

[0011] S3: Based on S1 and S2, a cloud medical workstation database is constructed, and the medical images in the cloud medical workstation are clustered into two clusters: short-term disease diagnosis and treatment and long-term disease diagnosis and treatment. The medical images in the two clusters are further classified and labeled.

[0012] When the medical image in the cloud medical workstation belongs to the short-term diagnosis and treatment cluster, the intelligent management of the medical image is terminated;

[0013] When the medical images in the cloud medical workstation belong to the long-term diagnosis and treatment cluster, step S4 is executed;

[0014] S4: extract historical medical images of the patient to whom the medical image belongs from the cloud medical workstation database, form a customized medical image data package for the patient, and intelligently evaluate the patient's condition improvement;

[0015] S5: Conduct weekly condition monitoring based on the patient’s condition improvement data.

[0016] Specifically, in S1, the patient's personal information data and online consultation diagnosis and treatment data include: the patient's personal information data set A, A = (a1, a2...a n ...a N ), a nIncluding the patient's name, gender and ID number information; online consultation diagnosis and treatment data set B, B = (b1, b2...b n ...b N ), b n It includes the patient's past medical history information and current diagnosis information; where n is a subscript, indicating the nth patient who consults online every day, and N is the total number of patients who consult online every day.

[0017] Specifically, in S2, the screening and matching of corresponding medical images according to the patient's personal information data includes the following specific steps:

[0018] S201: First, extract the name information from the patient's personal information data, and perform a first screening in the hospital's medical image data storage background according to the name information. When the number of screened medical images is 1, output the first screening result and end the screening; otherwise, execute step S201;

[0019] S202: extracting the gender information from the patient's personal information data, and performing a second screening based on the gender information in the first screening result. When the number of screened medical images is 1, output the second screening result and end the screening; otherwise, execute step S203;

[0020] S203: Extract the ID card number information from the patient's personal information data, perform a third screening based on the ID card number information in the second screening result, and output the third screening result to form a medical image data set C, where C = (c1, c2...c n ...c N ), c n All medical imaging information of patients in the hospital's medical imaging data storage background;

[0021] S204: Complete the screening and matching process of medical images.

[0022] Specifically, in S2, the interpolation completion process includes:

[0023] S211: grayscale the medical images in the medical image dataset C, and identify X pixels L with abnormal clarity in the medical images by setting a grayscale threshold. n，x , where L n，x is the xth pixel with abnormal clarity identified in all medical images of the nth patient who consults online, 0≤x≤X;

[0024] S212: Selecting pixel point c with abnormal clarity n，x The nearest neighboring pixels of the nearby R pixels are interpolated and completed for the pixel with abnormal clarity through interpolation and completion strategy;

[0025] The interpolation completion strategy is as follows:

[0026] Among them, α1+α2=0.5, α1, α2 are weight coefficients of the interpolation and completion process respectively;

[0027] L′ n，x The xth pixel point with abnormal clarity after interpolation and completion in the latest medical image of the nth patient who is undergoing online consultation;

[0028] They are the closest neighboring pixels of R pixels, with a distance of L n，x The farthest pixel and the closest pixel, d1, d2 are both Manhattan distances, and d1>d2;

[0029] S213: Obtain the medical image data set C′ after the interpolation and completion processing, C′=(c′1,c′2 . . . c′ n ...c′ N ).

[0030] Specifically, in S3, the clustering criteria for the two clusters of short-term diagnosis and treatment and long-term diagnosis and treatment are:

[0031] S31: sorting the medical images of each online consultation patient in the medical image dataset C′ after the interpolation and completion processing in S213 according to the report generation date, and obtaining the number of times J that the medical image of each online consultation patient appears in the medical image dataset C′, wherein the medical image generated by the same patient on the same day is counted as appearing only once;

[0032] S32: performing medical image clustering processing;

[0033] When J < 3, the medical images are clustered into short-term disease diagnosis and treatment clusters;

[0034] When J ≥ 3, the medical images are clustered into long-term disease diagnosis and treatment clusters.

[0035] Specifically, in S3, the labeling process includes: the department type, image type and organ system type of the cloud medical workstation.

[0036] Specifically, in S4, the calculation strategy of the patient's condition improvement degree is as follows:

[0037]

[0038] Among them, j, j-1, j-2 are all subscripts;

[0039] S jis the lesion area of ​​the medical image that appears for the jth time in the medical images that are sorted according to the generation date of the medical image reports in step S31; j-1 and S j-2 are the lesion areas of the medical images appearing at the j-1st and j-2nd times, respectively;

[0040] G j G is the lesion density of the medical image that appears for the jth time in the medical images that have been sorted according to the generation date of the medical image reports in step S31; j-1 and G j-2 are the lesion densities of the medical images that appeared for the j-1st and j-2nd times respectively.

[0041] Specifically, in S5, the weekly condition monitoring includes:

[0042] When the patient's condition improvement degree Φ is lower than the improvement degree threshold Φ0, the patient's location information is obtained, and the patient's location information and the information of the department to which the patient is visiting are searched to obtain the top three hospitals with the annual number of online consultations in the department near the patient;

[0043] The patient will be sent a reexamination reminder and the official website information of the three hospitals, and asked about the hospital of their choice or the name of their preferred hospital. After obtaining their consent, the patient's customized medical imaging data package in the cloud medical workstation will be encrypted and shared with the patient's intended hospital or preferred hospital.

[0044] In addition, the present invention provides a medical image intelligent management system for a cloud medical workstation, including the following modules:

[0045] Data acquisition module, medical image screening module, image clustering module, medical image data package customization module, and condition monitoring module;

[0046] The data collection module is used to collect and store the personal information data and online consultation treatment data of patients who have daily online consultations;

[0047] The medical image screening module accesses the medical image data storage backend of each hospital that joins the cloud medical workstation through the cloud medical workstation, screens and matches the corresponding medical images according to the patient's personal information data, and performs interpolation and completion processing on abnormal pixel points in the medical images;

[0048] The image clustering module is used to construct a cloud medical workstation database, cluster the medical images in the cloud medical workstation into two clusters of short-term disease diagnosis and treatment and long-term disease diagnosis and treatment, and further classify and label the medical images in the two clusters;

[0049] The medical image data package customization module is used to extract the historical medical images of the patient to whom the medical image belongs from the cloud medical workstation database, form a patient-customized medical image data package, and intelligently evaluate the patient's condition improvement;

[0050] The condition monitoring module performs weekly condition monitoring based on the patient's condition improvement data.

[0051] Compared with the prior art, the technical effects of the present invention are as follows:

[0052] 1. The present invention obtains permissions and screens medical images one by one through the name information, gender information and ID number information in the patient's personal information data; compared with the traditional medical image matching and screening mechanism, the screening mechanism adopted by the present invention reduces the exposure of the patient's personal privacy information by collecting all the patient's personal information for medical image matching, and better protects the patient's privacy data.

[0053] 2. The present invention clusters the medical images in the cloud medical workstation into two clusters: short-term disease diagnosis and treatment and long-term disease diagnosis and treatment, and further classifies and labels the medical images in the two clusters, thereby realizing personalized services and management for patients and improving the universality of the present invention.

[0054] 3. The present invention takes into account the huge amount of medical imaging data in the cloud medical platform and the high utilization rate of the cloud medical platform by patients undergoing long-term disease diagnosis and treatment, and shifts the management focus to patients undergoing long-term disease diagnosis and treatment, thereby alleviating the data processing pressure of the platform. At the same time, different medical imaging management strategies are implemented according to the length of the patient's disease course, thereby improving the work efficiency of the medical workstation. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0056] in:

[0057] Figure 1 A schematic diagram of a process of a medical image intelligent management method for a cloud medical workstation according to the present invention;

[0058] Figure 2 It is a structural schematic diagram of a medical image intelligent management system for a cloud medical workstation according to the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0062] Embodiment 1:

[0063] like Figure 1 As shown, a medical image intelligent management method for a cloud medical workstation according to an embodiment of the present invention includes the following specific steps:

[0064] S1: Collect and store the personal information data of patients who have online consultations every day and the diagnosis and treatment data of online consultations;

[0065] In S1, the patient's personal information data and online consultation diagnosis and treatment data include: the patient's personal information data set A, A = (a1, a2...a n ...a N ), a n Including the patient's name, gender and ID number information; online consultation diagnosis and treatment data set B, B = (b1, b2...b n ...b N ), b n It includes the patient's past medical history information and current diagnosis information; where n is a subscript, indicating the nth patient who consults online every day, and N is the total number of patients who consult online every day.

[0066] S2: Access the medical image data storage backend of each hospital that has joined the Cloud Medical Workstation through the Cloud Medical Workstation, select and match the corresponding medical images according to the patient's personal information data, and perform interpolation and completion processing on abnormal pixels in the medical images;

[0067] In S2, the screening and matching of corresponding medical images according to the patient's personal information data includes the following specific steps:

[0068] S201: First, extract the name information from the patient's personal information data, and perform a first screening in the hospital's medical image data storage background according to the name information. When the number of screened medical images is 1, output the first screening result and end the screening; otherwise, execute step S201;

[0069] S202: extracting the gender information from the patient's personal information data, and performing a second screening based on the gender information in the first screening result. When the number of screened medical images is 1, output the second screening result and end the screening; otherwise, execute step S203;

[0070] S203: Extract the ID card number information from the patient's personal information data, perform a third screening based on the ID card number information in the second screening result, and output the third screening result to form a medical image data set C, where C = (c1, c2...c n ...c N ), c n All medical imaging information of patients in the hospital's medical imaging data storage background;

[0071] S204: Complete the screening and matching process of medical images.

[0072] In S2, the interpolation completion process includes:

[0073] S211: grayscale the medical images in the medical image dataset C, and identify X pixels L with abnormal clarity in the medical images by setting a grayscale threshold. n，x , where L n，x is the xth pixel with abnormal clarity identified in all medical images of the nth patient who consults online, 0≤x≤X;

[0074] S212: Selecting pixel point c with abnormal clarity n，x The nearest neighboring pixels of the nearby R pixels are interpolated and completed for the pixel with abnormal clarity through interpolation and completion strategy;

[0075] The interpolation completion strategy is as follows:

[0076] Among them, α1+α2=0.5, α1, α2 are weight coefficients of the interpolation and completion process respectively;

[0077] L′ n，x The xth pixel point with abnormal clarity after interpolation and completion in the latest medical image of the nth patient who is undergoing online consultation;

[0078] They are the closest neighboring pixels of R pixels, with a distance of Ln，x The farthest pixel and the closest pixel, d1, d2 are both Manhattan distances, and d1>d2;

[0079] S213: Obtain the medical image data set C′ after the interpolation and completion processing, C′=(c′1,c′2 . . . c′ n ...c′ N ).

[0080] S3: Based on S1 and S2, a cloud medical workstation database is constructed, and the medical images in the cloud medical workstation are clustered into two clusters: short-term disease diagnosis and treatment and long-term disease diagnosis and treatment. The medical images in the two clusters are further classified and labeled.

[0081] In S3, the clustering criteria for the two clusters of short-term diagnosis and treatment and long-term diagnosis and treatment are:

[0082] S31: sorting the medical images of each online consultation patient in the medical image dataset C′ after the interpolation and completion processing in S213 according to the report generation date, and obtaining the number of times J that the medical image of each online consultation patient appears in the medical image dataset C′, wherein the medical image generated by the same patient on the same day is counted as appearing only once;

[0083] S32: performing medical image clustering processing;

[0084] When J < 3, the medical images are clustered into short-term disease diagnosis and treatment clusters;

[0085] When J ≥ 3, the medical images are clustered into long-term disease diagnosis and treatment clusters.

[0086] In S3, the labeling process includes: the department type, image type, and organ system type of the cloud medical workstation;

[0087] In this embodiment, the types of departments of the cloud medical workstation include: cardiovascular department, oncology department, neurology department, orthopedics department, obstetrics and gynecology department, and gastrointestinal department; the image types include: X-ray films, ultrasound images, CT scans, and MRI images; the organ system types include: head images, chest images, abdominal images, and bone images.

[0088] Determine the course of the patient's illness:

[0089] When the medical image in the cloud medical workstation belongs to the short-term diagnosis and treatment cluster, the intelligent management of the medical image is terminated;

[0090] When the medical images in the cloud medical workstation belong to the long-term diagnosis and treatment cluster, step S4 is executed;

[0091] S4: extract historical medical images of the patient to whom the medical image belongs from the cloud medical workstation database, form a customized medical image data package for the patient, and intelligently evaluate the patient's condition improvement;

[0092] In S4, the calculation strategy of the patient's condition improvement degree is as follows:

[0093]

[0094] Among them, j, j-1, j-2 are all subscripts;

[0095] S j is the lesion area of ​​the medical image that appears for the jth time in the medical images that are sorted according to the generation date of the medical image reports in step S31; j-1 and S j-2 are the lesion areas of the medical images appearing at the j-1st and j-2nd times, respectively;

[0096] G j G is the lesion density of the medical image that appears for the jth time in the medical images that have been sorted according to the generation date of the medical image reports in step S31; j-1 and G j-2 are the lesion densities of the medical images that appeared for the j-1st and j-2nd times respectively.

[0097] S5: Conduct weekly condition monitoring based on the patient’s condition improvement data.

[0098] In S5, the weekly condition monitoring includes:

[0099] When the patient's condition improvement degree Φ is lower than the improvement degree threshold Φ0, the patient's location information is obtained, and the patient's location information and the information of the department to which the patient is visiting are searched to obtain the top three hospitals with the annual number of online consultations in the department near the patient;

[0100] The patient will be sent a reexamination reminder and the official website information of the three hospitals, and asked about the hospital of their choice or the name of their preferred hospital. After obtaining their consent, the patient's customized medical imaging data package in the cloud medical workstation will be encrypted and shared with the patient's intended hospital or preferred hospital.

[0101] Embodiment 2:

[0102] like Figure 2 As shown, a medical image intelligent management system for a cloud medical workstation according to an embodiment of the present invention includes the following modules:

[0103] Data acquisition module, medical image screening module, image clustering module, medical image data package customization module, and condition monitoring module;

[0104] The data collection module is used to collect and store the personal information data and online consultation treatment data of patients who have daily online consultations;

[0105] The medical image screening module accesses the medical image data storage backend of each hospital that joins the cloud medical workstation through the cloud medical workstation, screens and matches the corresponding medical images according to the patient's personal information data, and performs interpolation and completion processing on abnormal pixel points in the medical images;

[0106] The image clustering module is used to construct a cloud medical workstation database, cluster the medical images in the cloud medical workstation into two clusters of short-term disease diagnosis and treatment and long-term disease diagnosis and treatment, and further classify and label the medical images in the two clusters;

[0107] The medical image data package customization module is used to extract the historical medical images of the patient to whom the medical image belongs from the cloud medical workstation database, form a patient-customized medical image data package, and intelligently evaluate the patient's condition improvement;

[0108] The condition monitoring module performs weekly condition monitoring based on the patient's condition improvement data.

[0109] Embodiment three:

[0110] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0111] The processor executes the above-mentioned medical image intelligent management method for a cloud medical workstation by calling a computer program stored in the memory.

[0112] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a medical image intelligent management method for a cloud medical workstation provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0113] Embodiment 4:

[0114] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0115] When the computer program runs on a computer device, the computer device executes the above-mentioned medical image intelligent management method for a cloud medical workstation.

[0116] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0117] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0118] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0119] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0120] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0125] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0126] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0127] 1. The present invention obtains permissions and screens medical images one by one through the name information, gender information and ID number information in the patient's personal information data; compared with the traditional medical image matching and screening mechanism, the screening mechanism adopted by the present invention reduces the exposure of the patient's personal privacy information by collecting all the patient's personal information for medical image matching, and better protects the patient's privacy data.

[0128] 2. The present invention clusters the medical images in the cloud medical workstation into two clusters: short-term disease diagnosis and treatment and long-term disease diagnosis and treatment, and further classifies and labels the medical images in the two clusters, thereby realizing personalized services and management for patients and improving the universality of the present invention.

[0129] 3. The present invention takes into account the huge amount of medical imaging data in the cloud medical platform and the high utilization rate of the cloud medical platform by patients undergoing long-term disease diagnosis and treatment, and shifts the management focus to patients undergoing long-term disease diagnosis and treatment, thereby alleviating the data processing pressure of the platform. At the same time, different medical imaging management strategies are implemented according to the length of the patient's disease course, thereby improving the work efficiency of the medical workstation.

[0130] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A medical image intelligent management method for a cloud medical workstation, characterized in that: The method comprises the following specific steps: S1: Collect and store the personal information data of patients who have online consultations every day and the diagnosis and treatment data of online consultations; S2: Access the medical image data storage backend of each hospital that has joined the Cloud Medical Workstation through the Cloud Medical Workstation, select and match the corresponding medical images according to the patient's personal information data, and perform interpolation and completion processing on abnormal pixels in the medical images; S3: Based on S1 and S2, a cloud medical workstation database is constructed, and the medical images in the cloud medical workstation are clustered into two clusters: short-term disease diagnosis and treatment and long-term disease diagnosis and treatment. The medical images in the two clusters are further classified and labeled. When the medical image in the cloud medical workstation belongs to the short-term diagnosis and treatment cluster, the intelligent management of the medical image is terminated; When the medical images in the cloud medical workstation belong to the long-term diagnosis and treatment cluster, step S4 is executed; S4: extract historical medical images of the patient to whom the medical image belongs from the cloud medical workstation database, form a customized medical image data package for the patient, and intelligently evaluate the patient's condition improvement; S5: Conduct weekly condition monitoring based on the patient's condition improvement data; In S2, the screening and matching of corresponding medical images according to the patient's personal information data includes the following specific steps: S201: first extract the name information from the patient's personal information data, and perform a first screening in the hospital's medical image data storage background according to the name information. When the number of screened medical images is 1, output the first screening result and end the screening; Otherwise, execute step S202; S202: extracting the gender information from the patient's personal information data, and performing a second screening based on the gender information in the first screening result. When the number of screened medical images is 1, outputting the second screening result, and ending the screening; Otherwise, execute step S203; S203: Extract the ID card number information from the patient's personal information data, perform a third screening based on the ID card number information in the second screening result, output the third screening result, and form a medical image data set , , All medical imaging information of patients in the hospital's medical imaging data storage background; S204: completing the screening and matching process of medical images; In S2, the interpolation completion process includes: S211: Medical Imaging Datasets The medical images in the image are grayed out and the grayscale threshold is set to identify the Pixels with abnormal clarity ,in, For the The first Pixels with abnormal clarity, ; S212: Select pixels with abnormal clarity Nearby The nearest neighboring pixel of each pixel is used to interpolate and complete the pixel with abnormal clarity through interpolation and completion strategy; The interpolation completion strategy is as follows: ; in, , are the weight coefficients of the interpolation and completion process respectively; For the The latest medical images of patients who consulted online were interpolated and completed. Pixels with abnormal clarity; They are Among the nearest neighboring pixels of a pixel, the distance The farthest pixel and the closest pixel, are Manhattan distances, and ; S213: Obtaining the medical image data set after interpolation and completion processing , ; In S3, the clustering criteria for the two clusters of short-term diagnosis and treatment and long-term diagnosis and treatment are: S31: The medical image dataset after interpolation and completion processing in S213 The medical images of each online consultation patient in the medical image dataset are sorted according to the report generation date, and the medical images of each online consultation patient are obtained. Number of times it appears in , where medical images generated for the same patient on the same day are counted only once; S32: performing medical image clustering processing; when When , the medical images are clustered into short-term disease diagnosis and treatment clusters; when When , the medical images are clustered into long-term disease diagnosis and treatment clusters; In S3, the labeling process includes: the department type, image type, and organ system type of the cloud medical workstation; In S4, the calculation strategy of the patient's condition improvement degree is as follows: ； in, All are subscript; In step S31, among the medical images sorted according to the generation date of the medical image reports, The lesion area of ​​the medical image that appeared for the first time; and Respectively and The lesion area of ​​the medical image that appeared for the first time; In step S31, among the medical images sorted according to the generation date of the medical image reports, The density of lesions in the medical images that appeared at the second time; and Respectively and The density of lesions in the medical images that appeared at the second time; In S5, the weekly condition monitoring includes: When the patient's condition improves Below the improvement threshold When the patient's location information is obtained, the patient's location information and the information of the department to which the patient is visiting are searched to obtain the top three hospitals with the highest number of online consultations in the year in the department near the patient; The patient will be sent a reexamination reminder and the official website information of the three hospitals, and asked about the hospital of their choice or the name of their preferred hospital. After obtaining their consent, the patient's customized medical imaging data package in the cloud medical workstation will be encrypted and shared with the patient's intended hospital or preferred hospital.

2. According to claim 1, a medical image intelligent management method for a cloud medical workstation is characterized in that: In S1, the patient's personal information data and online consultation treatment data include: the patient's personal information data set , , Including the patient's name, gender and ID number information; online consultation diagnosis and treatment data set , , Includes the patient's medical history information and current diagnosis information; among them, Subscript, indicating the daily Patients who consulted online The total number of patients who consult online every day.

3. A medical image intelligent management system for a cloud medical workstation, which is implemented based on a medical image intelligent management method for a cloud medical workstation as claimed in any one of claims 1 to 2, characterized in that: The system includes the following modules: Data acquisition module, medical image screening module, image clustering module, medical image data package customization module, and condition monitoring module; The data collection module is used to collect and store the personal information data and online consultation treatment data of patients who have daily online consultations; The medical image screening module accesses the medical image data storage backend of each hospital that joins the cloud medical workstation through the cloud medical workstation, screens and matches the corresponding medical images according to the patient's personal information data, and performs interpolation and completion processing on abnormal pixel points in the medical images; The image clustering module is used to construct a cloud medical workstation database, cluster the medical images in the cloud medical workstation into two clusters of short-term disease diagnosis and treatment and long-term disease diagnosis and treatment, and further classify and label the medical images in the two clusters; The medical image data package customization module is used to extract the historical medical images of the patient to whom the medical image belongs from the cloud medical workstation database, form a patient-customized medical image data package, and intelligently evaluate the patient's condition improvement; The condition monitoring module performs weekly condition monitoring based on the patient's condition improvement data.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a medical image intelligent management method for a cloud medical workstation as described in any one of claims 1-2 is implemented.

5. An electronic device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instruction so that the device performs the operation of implementing a medical image intelligent management method for a cloud medical workstation as described in any one of claims 1-2.

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