Medical image data analysis and management system based on Internet

Abstract

The invention belongs to the technical field of image analysis and management, and particularly relates to an internet-based medical image data analysis and management system, which comprises a medical image acquisition module, a medical image elimination management and control module, a medical image processing and analysis module, a monitoring and evaluation module, a report display module and a background supervision terminal, according to the invention, the image quality condition of the medical image is evaluated through the medical image elimination management and control module, and the corresponding medical image is deleted when the elimination early warning signal is generated, so that the accuracy and high efficiency of the medical analysis result are ensured; when the image qualified signal is generated, the medical image processing and analyzing module processes and analyzes the medical image to realize focus detection, so that rich diagnosis information is provided for doctors; and the operation of the medical image processing and analyzing module is monitored through the monitoring and evaluating module to remind a manager to check and optimize the medical image processing and analyzing module in time, so that the management difficulty of the manager is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis management, and in particular to an Internet-based medical image data analysis management system. Background Art

[0002] Medical imaging refers to internal tissue images of the human body or a part of the human body obtained in a non-invasive manner for medical treatment or medical research. A medical imaging data analysis and management system is disclosed in the Chinese invention patent with publication number CN117711578A, which protects data security through a homomorphic encryption model, realizes data standardization and interoperability through a semantic network system, and realizes intelligent analysis and diagnosis functions through a convolutional neural network data analysis algorithm and a medical diagnosis algorithm based on deep learning;

[0003] However, in actual use, it cannot reasonably judge the quality of the corresponding medical images and automatically eliminate the medical images with unqualified quality before processing and analyzing them, and it cannot gradually and progressively evaluate the urgency of inspection and optimization of the diagnostic module, which increases the difficulty of the management personnel's work and is not conducive to ensuring the accuracy and efficiency of the diagnostic analysis results;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an Internet-based medical image data analysis and management system, which solves the problem that the prior art cannot reasonably judge the quality status of the corresponding medical images and automatically eliminate medical images of unqualified quality before processing and analyzing the medical images, and cannot gradually and progressively evaluate the urgency of inspection optimization for the diagnostic module, which is not conducive to ensuring the accuracy and efficiency of the diagnostic analysis results and is difficult to manage.

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

[0007] An Internet-based medical image data analysis and management system, comprising a medical image acquisition module, a medical image rejection control module, a medical image processing and analysis module, a monitoring and evaluation module, a report display module and a background supervision terminal;

[0008] The medical image acquisition module acquires medical images through imaging examination equipment and sends the acquired medical images to the medical image rejection control module;

[0009] The medical image rejection control module evaluates the image quality status when receiving the corresponding medical image, and generates a rejection warning signal or an image qualification signal of the corresponding medical image accordingly, deletes the corresponding medical image when the rejection warning signal is generated, and sends the corresponding medical image to the medical image processing and analysis module when the image qualification signal is generated;

[0010] The medical image processing and analysis module processes and analyzes the received medical images through built-in image processing algorithms and artificial intelligence analysis models to achieve lesion detection, and sends the lesion detection results to the report display module for display;

[0011] The monitoring and evaluation module monitors the operation of the medical image processing and analysis module, and determines through analysis whether to generate an optimization inspection warning signal, and sends the optimization inspection warning signal to the background supervision end. The background supervision end issues a warning when receiving the optimization inspection warning signal.

[0012] Furthermore, the specific analysis process of the medical image rejection control module includes:

[0013] Obtain the body parts actually covered in the corresponding medical image, mark the body parts to be covered as target parts, compare the body parts actually covered with all target parts, and if there are target parts not actually covered in the corresponding medical image, generate a rejection warning signal for the corresponding medical image;

[0014] If there is no target part that is not actually covered in the corresponding medical image, the corresponding medical image is identified for artifacts, thereby obtaining all artifacts existing in the corresponding medical image; a number of sub-regions are set in the corresponding medical image, and if there are artifacts in the corresponding sub-regions, the coverage area of ​​the corresponding artifacts is numerically compared with a preset artifact area threshold of the corresponding sub-region, and if the coverage area of ​​the corresponding artifact exceeds the corresponding preset artifact area threshold, the corresponding artifact is marked as a high-impact artifact; if there are high-impact artifacts in the corresponding medical image, a rejection warning signal for the corresponding medical image is generated;

[0015] If there are high-impact artifacts in the corresponding medical image, the image rejection alarm value of the corresponding medical image is obtained through analysis, and the image rejection alarm value is numerically compared with the preset image rejection alarm threshold. If the image rejection alarm value exceeds the preset image rejection alarm threshold, a rejection warning signal for the corresponding medical image is generated; if the image rejection alarm value does not exceed the preset image rejection alarm threshold, an image qualification signal for the corresponding medical image is generated.

[0016] Furthermore, the analysis and acquisition method of the image rejection alarm value is as follows:

[0017] The contrast of the corresponding medical image is obtained, the contrast is calculated to be different from the median of the set standard contrast range and the absolute value is taken to obtain a contrast test value, and the clarity and noise data of the corresponding medical image are obtained and marked as a clarity test value and a noise data test value respectively;

[0018] The ratio of the coverage area of ​​the corresponding artifact to the corresponding preset artifact area threshold is marked as the artifact occupancy detection value, the average of all artifact occupancy detection values ​​is calculated to obtain the artifact occupancy value, and the number of artifacts in the corresponding medical image is marked as the artifact number detection value; the image rejection alarm value is obtained by numerically calculating the contrast detection value, clarity detection value, noise data detection value, artifact occupancy value and artifact number detection value.

[0019] Furthermore, the medical image rejection control module is connected to the acquisition abnormality diagnosis module in communication, and the medical image rejection control module sends the rejection warning signal or image qualification signal of the corresponding medical image to the acquisition abnormality diagnosis module; the acquisition abnormality diagnosis module obtains all imaging examination devices for medical image acquisition, and marks the corresponding imaging examination device as the target device;

[0020] A detection period is set, and the image acquisition management performance of the corresponding target device during the detection period is analyzed. A normal acquisition signal or an abnormal acquisition signal of the corresponding target device is generated through analysis, and the normal acquisition signal or the abnormal acquisition signal of the corresponding target device is sent to the background supervision end. The background supervision end issues a warning when receiving the abnormal acquisition signal.

[0021] Furthermore, the specific analysis process of the acquisition abnormality diagnosis module is as follows:

[0022] All medical images corresponding to the rejection warning signal associated with the corresponding target device during the detection period are obtained, and the ratio of the number of medical images corresponding to the rejection warning signal associated with the corresponding target device during the detection period is marked as an image non-compliance value, and the image rejection alarm values ​​of all medical images of the image qualified signal associated with the corresponding target device during the detection period are averaged to obtain the image rejection performance value;

[0023] The imaging abnormality diagnosis value is obtained by numerically calculating the image non-compliance value and the image rejection performance value, and the imaging abnormality diagnosis value is numerically compared with the preset imaging abnormality diagnosis threshold. If the imaging abnormality diagnosis value exceeds the preset imaging abnormality diagnosis threshold, an acquisition abnormality signal of the corresponding target device is generated; if the imaging abnormality diagnosis value does not exceed the preset imaging abnormality diagnosis threshold, a normal acquisition signal of the corresponding target device is generated.

[0024] Furthermore, the specific analysis process of the monitoring and evaluation module is as follows:

[0025] The time when the last optimization inspection for the medical image processing and analysis module was performed is obtained and marked as the adjacent inspection time, and the period between the current time and the adjacent inspection time is marked as the monitoring period; the duration of the monitoring period is collected and marked as the monitoring duration, and the monitoring duration is numerically compared with the preset monitoring duration threshold. If the monitoring duration exceeds the preset monitoring duration threshold, an optimization inspection warning signal is generated.

[0026] Furthermore, if the monitoring time does not exceed the preset monitoring time threshold, the time difference between the time when the medical image processing and analysis module receives the corresponding medical image and the time when the processing and analysis starts is calculated to obtain the analysis delay value, and the average of all analysis delay values ​​within the monitoring period is calculated to obtain the delay value;

[0027] And the time difference between the time when the medical image processing and analysis module starts processing and analysis and the time when the processing and analysis ends is calculated to obtain the analysis duration value, and the average of all analysis duration values ​​within the monitoring period is calculated to obtain the persistence value;

[0028] The number of analysis interruptions that occur during the processing and analysis of the medical image processing and analysis module during the monitoring period is obtained and marked as an interruption frequency value, and the number of occurrences of the duration of the analysis interruption during the monitoring period exceeding the corresponding preset duration threshold is marked as a high interruption value;

[0029] The monitoring evaluation value is obtained by numerically calculating the monitoring duration, extended analysis value, sustained analysis value, frequency cutoff value and high frequency cutoff value, and the monitoring evaluation value is numerically compared with the preset monitoring evaluation value range. If the monitoring evaluation value exceeds the maximum value of the preset monitoring evaluation value range, an optimization inspection warning signal is generated; if the monitoring evaluation value does not exceed the minimum value of the preset monitoring evaluation value range, no optimization inspection warning signal is generated.

[0030] Furthermore, if the monitoring evaluation value is within the preset monitoring evaluation value range, the medical image processing and analysis module is tested using several groups of standard medical images. At the end of the test for the corresponding standard medical images, the lesion recognition accuracy, lesion location accuracy and lesion area measurement accuracy are obtained. The lesion recognition accuracy for all standard medical images is averaged to obtain the lesion recognition value, and the lesion location detection value and lesion surface detection value are obtained in the same way.

[0031] The operation decision value is obtained by numerically calculating the lesion identification value, the lesion position detection value and the lesion surface detection value, and the operation decision value is numerically compared with the preset operation decision threshold. If the operation decision value exceeds the preset operation decision threshold, an optimization inspection warning signal is generated; if the operation decision value does not exceed the preset operation decision threshold, no optimization inspection warning signal is generated.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. In the present invention, the image quality of medical images is evaluated through the medical image rejection control module, and the corresponding medical images are deleted when the rejection warning signal is generated to ensure the accuracy and efficiency of the medical analysis results. When the qualified image signal is generated, the medical image is processed and analyzed to achieve lesion detection, providing doctors with rich diagnostic information, and the operation of the medical image processing and analysis module is monitored through the monitoring and evaluation module to achieve timely optimization of the medical image processing and analysis module, ensuring the subsequent analysis accuracy and operation efficiency, and significantly reducing the management difficulty of management personnel;

[0034] 2. In the present invention, all imaging examination devices for medical image acquisition are acquired through the acquisition abnormality diagnosis module, the corresponding imaging examination devices are marked as target devices, the image acquisition management performance of the corresponding target devices during the detection period is analyzed, and the acquisition normal signal or acquisition abnormal signal of the corresponding target devices is generated through the analysis. When the acquisition abnormality signal is generated, the background management personnel are reminded to strengthen the operation supervision and operation supervision of the corresponding target devices to ensure the quality of the medical images collected, thereby further reducing the management difficulty of the management personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0036] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0037] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0039] Embodiment 1: Figure 1 As shown, the present invention proposes an Internet-based medical image data analysis and management system, including a medical image acquisition module, a medical image rejection management and control module, a medical image processing and analysis module, a monitoring and evaluation module, a report display module and a background supervision terminal;

[0040] The medical image acquisition module obtains medical images through imaging examination equipment (such as CT, MRI, X-ray, ultrasound, etc.), and records important information such as the patient's basic information, examination time, examination site, examination items, etc., and sends the collected medical images to the medical image rejection management and control module via the Internet.

[0041] When the medical image rejection control module receives the corresponding medical image, it evaluates the image quality status and generates a rejection warning signal or an image qualification signal for the corresponding medical image. When the rejection warning signal is generated, the corresponding medical image is deleted. It can reasonably analyze the quality status of the medical image and automatically reject the medical images with unqualified quality, thereby ensuring the accuracy and efficiency of the subsequent medical analysis results. The specific analysis process of the medical image rejection control module is as follows:

[0042] The body parts actually covered in the corresponding medical image are obtained, the body parts to be covered are marked as target parts, and the body parts actually covered are compared with all target parts. If there are target parts that are not actually covered in the corresponding medical image, it indicates that the body position on the medical image is incorrect and the quality of the corresponding medical image is poor, then a rejection warning signal for the corresponding medical image is generated;

[0043] If there is no target part that is not actually covered in the corresponding medical image, indicating that the body position on the medical image is correct, then the corresponding medical image is subjected to artifact recognition, thereby obtaining all artifacts existing in the corresponding medical image (such as motion artifacts, metal artifacts, etc., which may cover up the lesion and mislead the diagnosis);

[0044] A number of sub-regions are set in the corresponding medical image. If an artifact exists in the corresponding sub-region, the coverage area of ​​the corresponding artifact is numerically compared with a preset artifact area threshold of the corresponding sub-region. If the coverage area of ​​the corresponding artifact exceeds the corresponding preset artifact area threshold, it indicates that the existence of the corresponding artifact has a greater adverse effect on the accuracy of the medical analysis result, and the corresponding artifact is marked as a high-impact artifact; if a high-impact artifact exists in the corresponding medical image, it indicates that the quality of the corresponding medical image is poor, and a rejection warning signal of the corresponding medical image is generated;

[0045] If there are high-impact artifacts in the corresponding medical image, the contrast of the corresponding medical image is obtained, and the contrast is calculated by difference with the median of the set standard contrast range and the absolute value is taken to obtain the contrast test value, and the clarity and noise data of the corresponding medical image are obtained (an image with too much noise will affect the accuracy of diagnosis) and marked as the clarity test value and the noise data test value respectively; it should be noted that the higher the clarity, the clearer the details and textures in the image, which is more beneficial for the diagnosis of the disease. The contrast refers to the difference between the light and dark areas in the image, which should be within an appropriate range;

[0046] and marking the ratio of the coverage area of ​​the corresponding artifact to the corresponding preset artifact area threshold as an artifact occupancy detection value, calculating the average of all artifact occupancy detection values ​​to obtain an artifact occupancy value, and marking the number of artifacts in the corresponding medical image as an artifact number detection value;

[0047] The contrast test value FY, the clarity test value NP, the noise data test value QS, the artifact occupancy value RF and the artifact number test value YK are numerically calculated by the formula LP=h1*FY+h2 / NP+h3*QS+(h4*RF+h5*YK) / (h1+h2+h3) to obtain the image rejection alarm value LP; wherein, h1, h2, h3, h4, h5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the image rejection alarm value LP is, the worse the quality of the corresponding medical image is in general;

[0048] The image rejection alarm value LP is numerically compared with the preset image rejection alarm threshold. If the image rejection alarm value LP exceeds the preset image rejection alarm threshold, it indicates that the quality status of the corresponding medical image is generally poor, and a rejection warning signal for the corresponding medical image is generated; if the image rejection alarm value LP does not exceed the preset image rejection alarm threshold, it indicates that the quality status of the corresponding medical image is generally good, and an image qualification signal for the corresponding medical image is generated.

[0049] When a qualified image signal is generated, the corresponding medical image is sent to the medical image processing and analysis module. The medical image processing and analysis module processes and analyzes the received medical image through the built-in image processing algorithm and artificial intelligence analysis model to achieve lesion detection, and sends the lesion detection result to the report display module for display, providing doctors with rich diagnostic information, thereby improving doctors' work efficiency and diagnostic accuracy.

[0050] The monitoring and evaluation module monitors the operation of the medical image processing and analysis module, and determines whether to generate an optimization inspection warning signal through analysis, and sends the optimization inspection warning signal to the background supervision end. When the background supervision end receives the optimization inspection warning signal, it issues a warning. After receiving the corresponding warning, the background management personnel promptly inspect and optimize the medical image processing and analysis module to ensure the subsequent analysis accuracy and operation efficiency of the medical image processing and analysis module, and significantly reduce the management difficulty of the management personnel; the specific analysis process of the monitoring and evaluation module is as follows:

[0051] The time when the medical image processing and analysis module was optimized and checked is obtained and marked as the adjacent inspection time, and the period between the current time and the adjacent inspection time is marked as the monitoring period; the duration of the monitoring period is collected and marked as the monitoring duration, and the monitoring duration is numerically compared with the preset monitoring duration threshold. If the monitoring duration exceeds the preset monitoring duration threshold, it indicates that the medical image processing and analysis module needs to be optimized and checked, and an optimization inspection warning signal is generated;

[0052] If the monitoring time does not exceed the preset monitoring time threshold, the time difference between the time when the medical image processing and analysis module receives the corresponding medical image and the time when the processing and analysis starts is calculated to obtain the analysis delay value, and the average of all analysis delay values ​​within the monitoring period is calculated to obtain the delay value;

[0053] And the time difference between the time when the medical image processing and analysis module starts processing and analysis and the time when the processing and analysis ends is calculated to obtain the analysis duration value, and the average of all analysis duration values ​​within the monitoring period is calculated to obtain the persistence value;

[0054] The number of analysis interruptions that occur during the processing and analysis of the medical image processing and analysis module during the monitoring period is obtained and marked as an interruption frequency value, and the number of occurrences of the duration of the analysis interruption during the monitoring period exceeding the corresponding preset duration threshold is marked as a high interruption value;

[0055] By formula The monitoring time HN, the extended value HM, the sustained value HS, the cut-off value HW and the high cut-off value HF are numerically calculated to obtain the monitoring evaluation value SL; wherein, uw1, uw2, uw3, uw4, and uw5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the monitoring evaluation value SL is, the more timely it is necessary to check and optimize the medical image processing and analysis module;

[0056] The monitoring evaluation value SL is numerically compared with the preset monitoring evaluation value range. If the monitoring evaluation value SL exceeds the maximum value of the preset monitoring evaluation value range, it indicates that the medical image processing and analysis module needs to be inspected and optimized, and an optimization inspection warning signal is generated; if the monitoring evaluation value SL does not exceed the minimum value of the preset monitoring evaluation value range, it indicates that the medical image processing and analysis module does not need to be inspected and optimized, and no optimization inspection warning signal is generated.

[0057] Further, if the monitoring evaluation value is within the preset monitoring evaluation value range, several groups of standard medical images (i.e., medical images with various lesion abnormalities) are used to test the medical image processing and analysis module. At the end of the test for the corresponding standard medical images, the lesion recognition accuracy (i.e., the data volume value of the ratio of the number of accurately identified lesions to the total number of existing lesions), the lesion positioning accuracy (i.e., the data volume value of the average value of the error of the identified lesion position, the larger the value of the lesion positioning accuracy, the smaller the error distance of the identified lesion position) and the lesion area measurement accuracy (i.e., the data volume value of the deviation value of the area of ​​the identified lesion from its actual area) for the corresponding standard medical images are obtained;

[0058] The lesion recognition value is obtained by calculating the average of the lesion recognition accuracy rates for all standard medical images, the lesion location detection value is obtained by calculating the average of the lesion positioning accuracy rates for all standard medical images, and the lesion surface detection value is obtained by calculating the average of the lesion area measurement accuracy rates for all standard medical images;

[0059] The lesion identification value QM, the lesion position detection value XL and the lesion surface detection value FD are numerically calculated by the formula QR=(wq1*QM+wq2*XL+wq3*FD) / 3 to obtain the operation decision value QR; wherein wq1, wq2 and wq3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the operation decision value QR is, the higher the processing and analysis accuracy of the medical image processing and analysis module is;

[0060] The operation decision value QR is numerically compared with the preset operation decision threshold. If the operation decision value QR exceeds the preset operation decision threshold, it indicates that the processing and analysis accuracy of the medical image processing and analysis module is poor, and the medical image processing and analysis module needs to be inspected and optimized, then an optimization inspection warning signal is generated; if the operation decision value QR does not exceed the preset operation decision threshold, it indicates that the processing and analysis accuracy of the medical image processing and analysis module is high, then no optimization inspection warning signal is generated.

[0061] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the medical image rejection control module is communicatively connected to the acquisition abnormality diagnosis module, and the medical image rejection control module sends the rejection warning signal or image qualification signal of the corresponding medical image to the acquisition abnormality diagnosis module; the acquisition abnormality diagnosis module obtains all imaging examination devices for medical image acquisition, and marks the corresponding imaging examination device as the target device;

[0062] A detection period is set, preferably, the detection period is ten days; the image acquisition management performance of the corresponding target device during the detection period is analyzed, and a normal acquisition signal or an abnormal acquisition signal of the corresponding target device is generated through analysis, and the normal acquisition signal or the abnormal acquisition signal of the corresponding target device is sent to the background monitoring end, and the background monitoring end issues an early warning when receiving the abnormal acquisition signal, so as to remind the background management personnel to strengthen the operation supervision and operation supervision of the corresponding target device in time, ensure the quality of the medical images collected, and further reduce the management difficulty of the management personnel; the specific analysis process of the abnormal acquisition diagnosis module is as follows:

[0063] All medical images corresponding to the rejection warning signal associated with the corresponding target device during the detection period are obtained, and the ratio of the number of medical images corresponding to the rejection warning signal associated with the corresponding target device during the detection period is marked as an image non-compliance value, and the image rejection alarm values ​​of all medical images of the image qualified signal associated with the corresponding target device during the detection period are averaged to obtain the image rejection performance value;

[0064] By formula The image non-conformity value Thg and the image rejection performance value Wfg are numerically calculated to obtain the abnormal imaging diagnosis value Hsd; wherein b1 and b2 are preset proportional coefficients, b1>b2>0; and the larger the value of the abnormal imaging diagnosis value Hsd, the worse the inspection operation condition of the corresponding target device during the detection period is overall;

[0065] The imaging abnormality diagnosis value Hsd is numerically compared with the preset imaging abnormality diagnosis threshold. If the imaging abnormality diagnosis value Hsd exceeds the preset imaging abnormality diagnosis threshold, it indicates that the inspection operation condition of the corresponding target device during the detection period is generally poor, and an acquisition abnormality signal of the corresponding target device is generated; if the imaging abnormality diagnosis value Hsd does not exceed the preset imaging abnormality diagnosis threshold, it indicates that the inspection operation condition of the corresponding target device during the detection period is generally good, and a normal acquisition signal of the corresponding target device is generated.

[0066] The working principle of the present invention is as follows: when in use, medical images are collected through the medical image acquisition module, the medical image rejection control module evaluates the image quality status of the corresponding medical images, and deletes the corresponding medical images when a rejection warning signal is generated. The quality status of the medical images can be reasonably analyzed and medical images with unqualified quality can be automatically rejected, thereby ensuring the accuracy and efficiency of subsequent medical analysis results. When a qualified image signal is generated, the medical image is processed and analyzed by the medical image processing and analysis module to achieve lesion detection, and the lesion detection results are sent to the report display module for display, providing doctors with rich diagnostic information, thereby improving the doctors' work efficiency and diagnostic accuracy, and monitoring the operation of the medical image processing and analysis module through the monitoring and evaluation module, and judging whether to generate an optimization inspection warning signal through analysis, so as to remind management personnel to timely inspect and optimize the medical image processing and analysis module, thereby ensuring the subsequent analysis accuracy and operation efficiency of the medical image processing and analysis module, and significantly reducing the management difficulty of management personnel.

[0067] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A medical image data analysis and management system based on the Internet, characterized in that: It includes a medical image acquisition module, a medical image rejection control module, a medical image processing and analysis module, a monitoring and evaluation module, a report display module and a background supervision terminal; the medical image acquisition module obtains medical images through imaging examination equipment and sends the collected medical images to the medical image rejection control module; The medical image rejection control module evaluates the image quality status when receiving the corresponding medical image, and generates a rejection warning signal or an image qualification signal of the corresponding medical image accordingly, deletes the corresponding medical image when the rejection warning signal is generated, and sends the corresponding medical image to the medical image processing and analysis module when the image qualification signal is generated; The medical image processing and analysis module processes and analyzes the received medical images through the built-in image processing algorithm and artificial intelligence analysis model to achieve lesion detection, and sends the lesion detection results to the report display module for display; the monitoring and evaluation module monitors the operation of the medical image processing and analysis module, determines through analysis whether to generate an optimization inspection warning signal, and sends the optimization inspection warning signal to the background supervision end.

2. The Internet-based medical image data analysis and management system according to claim 1, characterized in that: The specific analysis process of the medical image rejection control module includes: Obtain the body parts actually covered in the corresponding medical image, mark the body parts to be covered as target parts, and if there are target parts that are not actually covered in the corresponding medical image, generate a rejection warning signal for the corresponding medical image; If there is no target part that is not actually covered in the corresponding medical image, the corresponding medical image is subjected to artifact recognition, thereby obtaining all artifacts existing in the corresponding medical image; if the coverage area of ​​the corresponding artifact exceeds the corresponding preset artifact area threshold, the corresponding artifact is marked as a high-impact artifact; if there is a high-impact artifact in the corresponding medical image, a rejection warning signal for the corresponding medical image is generated; If there are high-impact artifacts in the corresponding medical image, the image rejection alarm value of the corresponding medical image is obtained through analysis. If the image rejection alarm value exceeds the preset image rejection alarm threshold, a rejection warning signal for the corresponding medical image is generated; if the image rejection alarm value does not exceed the preset image rejection alarm threshold, an image qualification signal for the corresponding medical image is generated.

3. The Internet-based medical image data analysis and management system according to claim 2, characterized in that: The analysis and acquisition method of the image rejection alarm value is as follows: The ratio of the coverage area of ​​the corresponding artifact to the corresponding preset artifact area threshold is marked as the artifact occupancy detection value, the average of all artifact occupancy detection values ​​is calculated to obtain the artifact occupancy value, and the number of artifacts in the corresponding medical image is marked as the artifact number detection value; the image rejection alarm value is obtained by numerically calculating the contrast detection value, clarity detection value, noise data detection value, artifact occupancy value and artifact number detection value.

4. The Internet-based medical image data analysis and management system according to claim 1, characterized in that: The medical image rejection control module is connected to the acquisition abnormality diagnosis module in communication. The acquisition abnormality diagnosis module obtains all imaging examination devices for medical image acquisition and marks the corresponding imaging examination devices as target devices. A detection period is set, and the image acquisition management performance of the corresponding target device during the detection period is analyzed. A normal acquisition signal or an abnormal acquisition signal of the corresponding target device is generated through the analysis, and the normal acquisition signal or the abnormal acquisition signal of the corresponding target device is sent to the background supervision end.

5. The Internet-based medical image data analysis and management system according to claim 4, characterized in that: The specific analysis process of the acquisition abnormality diagnosis module is as follows: The imaging abnormality diagnosis value is obtained by numerically calculating the image non-compliance value and the image rejection performance value. If the imaging abnormality diagnosis value exceeds the preset imaging abnormality diagnosis threshold, an acquisition abnormality signal of the corresponding target device is generated; if the imaging abnormality diagnosis value does not exceed the preset imaging abnormality diagnosis threshold, a normal acquisition signal of the corresponding target device is generated.

6. The Internet-based medical image data analysis and management system according to claim 1, characterized in that: The specific analysis process of the monitoring and evaluation module is as follows: Obtain the time when the optimization inspection for the medical image processing and analysis module was performed and mark it as the adjacent inspection time, and mark the period between the current time and the adjacent inspection time as the monitoring period; The duration of the monitoring period is collected and marked as the monitoring duration. If the monitoring duration exceeds the preset monitoring duration threshold, an optimization inspection warning signal is generated.

7. The Internet-based medical image data analysis and management system according to claim 6, characterized in that: If the monitoring time does not exceed the preset monitoring time threshold, the monitoring evaluation value is obtained by numerically calculating the monitoring time, delay value, duration value, frequency cutoff value and high cutoff value. If the monitoring evaluation value exceeds the maximum value of the preset monitoring evaluation value range, an optimization inspection warning signal is generated.

8. The Internet-based medical image data analysis and management system according to claim 7, characterized in that: If the monitoring evaluation value is within the preset monitoring evaluation value range, the medical image processing and analysis module is tested using several sets of standard medical images, and the operation decision value is obtained by numerically calculating the lesion identification value, lesion position detection value and lesion surface detection value. If the operation decision value exceeds the preset operation decision threshold, an optimization inspection warning signal is generated.

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