Method and system for generating image detection scheme

Abstract

The invention provides an image detection scheme generation method and system, and the method comprises the steps: obtaining the initial detection information of a user and the illness state analysis information of a medical worker for the user, and recognizing the user physical sign data of the user and the illness state question information of the user based on the initial detection information of the user; identifying a user abnormal area of the user and user abnormal information of the user on the basis of the illness state analysis information of the user, and identifying an image detection parameter range corresponding to each image detection type adapted to the user through an image adaptation network on the basis of the user sign data of the user and illness state question information of the user; and generating a target inspection area of the user based on the user abnormal area of the user and the user abnormal information of the user, and generating an image detection scheme of the user based on the image detection parameter range corresponding to each image detection type adapted to the user and the target inspection area of the user. By adopting the scheme, the examination applicability and the examination accuracy of the patient can be improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of structural modeling and medical assistance, and particularly to a method and system for generating an imaging detection scheme. Background Art

[0002] In existing medical technologies, patient examinations are an important way to understand and analyze a patient's condition. The comprehensiveness of examinations can effectively guide medical staff in determining a patient's condition. Therefore, accurate and comprehensive examinations are one of the important factors in improving the analysis and understanding of a patient's condition. Among them, imaging examinations of patients are one of the main ways to conduct a comprehensive examination of the patient's body. Different from other examination methods, this examination method has certain physical risks and cost considerations. Therefore, how to ensure reasonable imaging detection of patients is the current research focus.

[0003] Traditional disease detection methods are that medical staff directly judge whether to conduct imaging detection on a patient based on the patient's condition. However, this method is limited by the subjective judgment of medical staff, with low accuracy and generally high costs. For some patients, the physical impact is relatively large, resulting in poor applicability of the examination to patients. Summary of the Invention

[0004] The main objective of the present invention is to provide a method and system for generating an imaging detection scheme, aiming to solve the problems in the existing technology that, due to being limited by the subjective judgment of medical staff, the accuracy is relatively low, the cost is generally high, and for some patients, the physical impact is relatively large, resulting in poor applicability of the examination to patients.

[0005] To achieve the above objective, the present invention provides a method for generating an imaging detection scheme, and the method includes:

[0006] Obtain the initial examination information of a user and the condition analysis information of the user by medical staff, and based on the initial examination information of the user, identify the user's physical sign data and the user's condition question information;

[0007] Based on the condition analysis information of the user, identify the user's abnormal area and the user's abnormal information, and based on the user's physical sign data and the user's condition question information, through an image adaptation network, identify the range of imaging detection parameters corresponding to each imaging detection type adapted by the user;

[0008] Based on the user's abnormal area and the user's abnormal information, generate the user's target examination area, and based on the range of imaging detection parameters corresponding to each imaging detection type adapted by the user and the user's target examination area, generate the user's imaging detection scheme.

[0009] Optionally, identifying the user's physical sign data and the user's disease question information based on the user's preliminary examination information includes:

[0010] Splitting the user's preliminary examination information into detection data of each detection type and data change information of each physical sign type of the user, and based on the detection data of each detection type, through the detection analysis strategies of each detection type, identifying sub-detection abnormal information corresponding to each detection type;

[0011] Based on the data change information of each physical sign type, through the physical sign change analysis strategies of each preset physical sign type, identifying the current physical sign data of each physical sign type and the current physical sign state of each physical sign type, and taking the current physical sign data of each physical sign type and the current physical sign state of each physical sign type as the user's physical sign data;

[0012] Taking the sub-detection abnormal information corresponding to each detection type as the user's disease question information.

[0013] Optionally, identifying the user's abnormal area and the user's abnormal information based on the user's disease analysis information includes:

[0014] Identifying the area judgment results of each user body area from the user's disease analysis information, and for each user body area, based on the area judgment result of the user body area, identifying the area abnormal information and the area abnormal range of the user body area;

[0015] Based on the area abnormal information of the user body area, querying the abnormal probabilities of each area abnormal type of the user body area in the database, and based on each area abnormal type and the area abnormal range, screening the target area abnormal type of the user body area;

[0016] Taking the user body areas with target area abnormal types as the user abnormal areas, and taking the target area abnormal types of each user abnormal area and the area abnormal ranges of each user abnormal area as the user's abnormal information.

[0017] Optionally, identifying the range of image detection parameters corresponding to each image detection type adapted to the user through an image adaptation network based on the user's physical sign data and the user's disease question information includes:

[0018] Based on the current physical sign data of each physical sign type of the user, query the target imaging detection types suitable for the user in the detection taboo databases corresponding to each imaging detection type;

[0019] Based on the sub-detection abnormal information corresponding to each detection type, identify the abnormal degree values of each physical abnormality type of the user, and based on the abnormal degree values of each physical abnormality type, query the physical adaptation range corresponding to each physical abnormality type through the physical abnormality analysis strategy;

[0020] Based on the physical adaptation range corresponding to each physical abnormality type and the target imaging detection types suitable for the user, identify the imaging detection parameter ranges corresponding to the target imaging detection types suitable for the user through the imaging adaptation network.

[0021] Optionally, generating the target examination area of the user based on the user's abnormal area and the user's abnormal information includes:

[0022] Based on the target area abnormal types of each user abnormal area, query the initial area detection range of each user abnormal area through the detection database;

[0023] Based on the area abnormal range of each user abnormal area and the initial area detection range of each user abnormal area, screen the target area detection range of each user abnormal area through the range clustering strategy;

[0024] Take the target area detection ranges of all user abnormal areas as the target examination area of the user.

[0025] Optionally, generating the imaging detection plan of the user based on the imaging detection parameter ranges corresponding to the imaging detection types suitable for the user and the target examination area of the user includes:

[0026] For each imaging detection type, query the detection plans corresponding to the imaging detection type in the detection database of the imaging detection type based on the imaging detection parameter range of the imaging detection type;

[0027] Based on the initial detection plans of the imaging detection type, evaluate the detection evaluation values of each initial detection plan through the imaging detection index evaluation strategy, and screen the initial detection plan corresponding to the highest detection evaluation value as the target detection plan of the imaging detection type;

[0028] Based on the target area detection range of each user's abnormal area of the user, through the target detection scheme of the image detection type, generate a sub-image detection scheme of the user for the image detection type, and use the sub-image detection schemes of all image detection types as the image detection scheme of the user.

[0029] In addition, to achieve the above object, the present invention also provides an image detection scheme generation system, and the image detection scheme generation system includes:

[0030] An acquisition module, configured to acquire the initial examination information of the user and the condition analysis information of the medical staff for the user, and based on the initial examination information of the user, identify the user's physical sign data and the user's condition question information;

[0031] An identification module, configured to identify the user's abnormal area and the user's abnormal information based on the condition analysis information of the user, and identify the image detection parameter ranges corresponding to each image detection type adapted to the user through an image adaptation network based on the user's physical sign data and the user's condition question information;

[0032] A generation module, configured to generate the target examination area of the user based on the user's abnormal area and the user's abnormal information, and generate the image detection scheme of the user based on the image detection parameter ranges corresponding to each image detection type adapted to the user and the target examination area of the user.

[0033] Optionally, the acquisition module is specifically configured to:

[0034] Split the initial examination information of the user into detection data of each detection type and data change information of each physical sign type of the user, and based on the detection data of each detection type, identify the sub-detection abnormal information corresponding to each detection type through the detection analysis strategy of each detection type;

[0035] Based on the data change information of each physical sign type, identify the current physical sign data of each physical sign type and the current physical sign state of each physical sign type through the preset physical sign change analysis strategy of each physical sign type, and use the current physical sign data of each physical sign type and the current physical sign state of each physical sign type as the user's physical sign data;

[0036] Use the sub-detection abnormal information corresponding to each detection type as the user's condition question information.

[0037] Optionally, the identification module is specifically configured to:

[0038] Analyze the condition information of the user, identify the regional judgment results of each user's body area, and for each user's body area, based on the regional judgment results of the user's body area, identify the regional abnormal information of the user's body area and the regional abnormal range of the user's body area;

[0039] Based on the regional abnormal information of the user's body area, query the abnormal probabilities of each regional abnormal type of the user's body area in the database, and based on each regional abnormal type and the regional abnormal range, screen the target regional abnormal type of the user's body area;

[0040] Take the user's body area with the target regional abnormal type as the user's abnormal area, and take the target regional abnormal type of each user's abnormal area and the regional abnormal range of each user's abnormal area as the user's user abnormal information.

[0041] Optionally, the recognition module is specifically used for:

[0042] Based on the current physical sign data of each physical sign type of the user, query each target imaging detection type suitable for the user in the detection taboo database corresponding to each imaging detection type;

[0043] Based on the sub-detection abnormal information corresponding to each detection type, identify the abnormal degree value of each physical abnormal type of the user, and based on the abnormal degree value of each physical abnormal type, query the physical adaptation range corresponding to each physical abnormal type through the physical abnormal analysis strategy;

[0044] Based on the physical adaptation range corresponding to each physical abnormal type and each target imaging detection type suitable for the user, identify the imaging detection parameter range corresponding to each target imaging detection type suitable for the user through the imaging adaptation network.

[0045] Optionally, the generation module is specifically used for:

[0046] Based on the target regional abnormal type of each user's abnormal area, query the initial regional detection range of each user's abnormal area through the detection database;

[0047] Based on the regional abnormal range of each user's abnormal area and the initial regional detection range of each user's abnormal area, screen the target regional detection range of each user's abnormal area through the range clustering strategy;

[0048] Take the target regional detection ranges of all user abnormal areas as the target examination area of the user.

[0049] Optionally, the generation module is specifically used for:

[0050] For each image detection type, based on the image detection parameter range of the image detection type, query each detection scheme corresponding to the image detection type in the detection database of the image detection type;

[0051] Based on each initial detection scheme of the image detection type, through the image detection index evaluation strategy, evaluate the detection evaluation value of each initial detection scheme, and screen the initial detection scheme corresponding to the highest detection evaluation value as the target detection scheme of the image detection type;

[0052] Based on the target area detection range of each user abnormal area of the user, through the target detection scheme of the image detection type, generate a sub-image detection scheme of the user for the image detection type, and use the sub-image detection schemes of all image detection types as the image detection scheme of the user.

[0053] In a third aspect, the present application provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the first aspects are implemented.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium. A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0055] In a fifth aspect, the present application provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0056] The present invention provides a method and system for generating an imaging detection scheme. The method includes: obtaining the initial examination information of a user and the medical staff's analysis information on the user's condition, and based on the user's initial examination information, identifying the user's physical sign data and the user's condition question information; based on the medical staff's analysis information on the user's condition, identifying the user's abnormal area and the user's abnormal information, and based on the user's physical sign data and the user's condition question information, through an image adaptation network, identifying the range of imaging detection parameters corresponding to each imaging detection type suitable for the user; generating the user's target examination area based on the user's abnormal area and the user's abnormal information, and generating the user's imaging detection scheme based on the range of imaging detection parameters corresponding to each imaging detection type suitable for the user and the user's target examination area. In this scheme, by comprehensively judging the user's initial examination information and the medical staff's subjectively judged condition analysis information, the user's physical sign data, condition question information, and abnormal information of the user's abnormal area are identified. When performing imaging detection, it is not only possible to perform image adaptation based on the subjectively judged condition, but also to comprehensively adapt the imaging detection types suitable for the user by combining the user's physical signs and condition questions. This improves the detection effect of the imaging detection types suitable for the user and reduces the impact on the user's body image. Subsequently, by combining the above analysis data, the range of imaging detection parameters corresponding to each comprehensively adapted imaging detection type and the user's target examination area are used to not only further reduce the impact of each imaging detection type on the user's body image, but also effectively reduce the inspection cost loss. Finally, the generated user's imaging detection scheme can not only ensure meeting the actual needs of the imaging detection for the user's condition, but also effectively reduce the detection cost and the impact on the user's body image, thus comprehensively improving the inspection applicability and inspection accuracy for the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the solutions in the present application, the following will briefly introduce the drawings required for the description of the embodiments of the present application. Obviously, the following described drawings are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0058] Figure 1 is a flowchart of the method for generating an imaging detection scheme provided by an embodiment of the present invention;

[0059] Figure 2 is a schematic structural diagram of the system for generating an imaging detection scheme provided by an embodiment of the present invention;

[0060] Figure 3 Internal structure diagram of the computer device provided by the embodiment of the present invention. Specific implementation manners

[0061] The method for generating an image detection solution provided by the embodiment of the present invention is applied to an image detection solution generation system. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned accompanying drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned accompanying drawings are used to distinguish different objects and are not used to describe a specific order.

[0062] Referring to "embodiment" herein means that a specific feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0063] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0064] The method for generating an imaging detection solution provided by the embodiments of the present application can be applied to the application environment for generating an imaging detection solution. Among them, this method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, etc. Among them, the terminal makes a comprehensive judgment on the user's preliminary examination information and the condition analysis information subjectively judged by medical staff, so as to identify the user's physical sign data, condition question information, and user anomaly information in the user's abnormal area. When performing imaging detection, it is not only possible to adapt the imaging based on the condition situation subjectively judged, but also to comprehensively adapt the imaging detection type suitable for the user in combination with the user's physical signs and condition questions. This improves the detection effect of the imaging detection type suitable for the user and reduces the impact on the user's body imaging. Subsequently, by combining the above analysis data, this solution comprehensively adapts the imaging detection parameter range corresponding to each imaging detection type and the user's target examination area, so as to not only further reduce the degree of the user's body imaging by each imaging detection type, but also effectively reduce the inspection cost loss. Finally, the imaging detection solution for the user is generated, which can not only ensure that the actual needs of the imaging detection for the user's condition are met, but also effectively reduce the detection cost and the degree of the user's body imaging, thereby comprehensively improving the inspection applicability and inspection accuracy for the patient.

[0065] In one embodiment, as Figure 1 shown, a method for generating an imaging detection solution is provided. Taking the application of this method to a terminal as an example, the method includes the following steps:

[0066] Step S101, obtain the user's preliminary examination information and the medical staff's condition analysis information about the user, and based on the user's preliminary examination information, identify the user's physical sign data and the user's condition question information.

[0067] In this embodiment, the terminal obtains the preliminary examination information of the user by receiving the test results of each preliminary examination department. The preliminary examination departments include, but are not limited to, the blood test department, the excrement test department, and the basic test departments (such as blood pressure, electrocardiogram, weight, otolaryngology, body structure, etc.). Then, the terminal obtains the disease analysis results of the user by subsequent personnel, so as to obtain the disease analysis information of the user. The disease analysis information includes sub-analysis results for each body area of the user. Then, the terminal identifies the user's physical sign data and the user's disease question information based on the user's preliminary examination information. The user's physical sign data is used to represent the physical sign states and physical sign data of each physical sign type of the user. The physical sign types include, but are not limited to, the heart rate physical sign type, the respiratory physical sign type, the organ physical sign type, and the hormone secretion physical sign type. The physical sign states corresponding to different physical sign types depend on the physical sign data corresponding to each physical sign type. For example, if the heart rate data is slow, the physical sign state corresponding to the heart rate physical sign type may be a state such as sinus bradycardia. The specific identification process will be described in detail later.

[0068] Step S102: Based on the user's disease analysis information, identify the user's abnormal area and the user's abnormal information, and based on the user's physical sign data and the user's disease question information, identify the range of image detection parameters corresponding to each image detection type adapted to the user through an image adaptation network.

[0069] In this embodiment, the terminal identifies the user's abnormal area and the user's abnormal information based on the user's disease analysis information, and based on the user's physical sign data and the user's disease question information, identifies the range of image detection parameters corresponding to each image detection type adapted to the user through an image adaptation network. The image adaptation network is a convolutional neural network. The image detection types include, but are not limited to, X-ray and CT detection types, MRI nuclear magnetic resonance detection types, ultrasonic detection types, and nuclear medicine imaging detection types, etc.

[0070] Step S103: Based on the user's abnormal area and the user's abnormal information, generate the user's target examination area, and based on the range of image detection parameters corresponding to each image detection type adapted to the user and the user's target examination area, generate the user's image detection plan.

[0071] In this embodiment, the terminal generates a target examination area for the user based on the user's abnormal area and the user's abnormal information, and generates an image detection plan for the user based on the range of image detection parameters corresponding to each image detection type adapted to the user and the user's target examination area. Among them, the image detection plan includes sub-image detection plans for each image detection type applicable to the user. The sub-image detection plan includes the target area detection range for each abnormal area of the user and the detection plan for the image detection type. The detection plan is used to represent the detection sequence, detection angle, detection frequency and other plans of the detection plan. The specific recognition process will be described in detail later.

[0072] Based on the above solution, by comprehensively judging the user's preliminary examination information and the condition analysis information subjectively judged by medical staff, the user's physical sign data, condition question information, and abnormal information of the user's abnormal area are identified. When performing image detection, it is not only possible to perform image adaptation based on the condition situation judged subjectively, but also to comprehensively adapt the image detection type applicable to the user by combining the user's physical signs and condition questions. The detection effect of the image detection type applicable to the user is improved, and the physical image condition of the user is reduced. Then, by combining the above analysis data, the range of image detection parameters corresponding to each image detection type comprehensively adapted and the user's target examination area are used to not only further reduce the physical image degree of each image detection type on the user, but also effectively reduce the inspection cost loss. Finally, the generated image detection plan for the user can not only ensure that it meets the actual needs of the image detection for the user's condition, but also effectively reduce the detection cost and the physical image degree of the user, thus comprehensively improving the inspection applicability and inspection accuracy for the patient.

[0073] Optionally, based on the user's preliminary examination information, identifying the user's physical sign data and the user's condition question information includes: splitting the user's preliminary examination information into detection data of each detection type and data change information of each physical sign type of the user, and based on the detection data of each detection type, through the detection analysis strategy of each detection type, identifying the sub-detection abnormal information corresponding to each detection type; based on the data change information of each physical sign type, through the preset physical sign change analysis strategy of each physical sign type, identifying the current physical sign data of each physical sign type and the current physical sign state of each physical sign type, and taking the current physical sign data of each physical sign type and the current physical sign state of each physical sign type as the user's physical sign data; taking the sub-detection abnormal information corresponding to each detection type as the user's condition question information.

[0074] In this embodiment, the terminal splits the user's preliminary examination information into the detection data of each detection type and the data change information of each physical sign type of the user, and based on the detection data of each detection type, through the detection analysis strategies of each detection type, identifies the sub-detection abnormal information corresponding to each detection type. Among them, the detection analysis strategy of each detection type includes the detection abnormal information of each detection type corresponding to each detection data range of each detection type. Then, the terminal is configured to identify the self-detection abnormal information corresponding to each detection type through a range adaptation method.

[0075] Based on the data change information of each physical sign type, the terminal identifies the current physical sign data of each physical sign type and the current physical sign state of each physical sign type through the preset physical sign change analysis strategies of each physical sign type. Among them, the current physical sign data of each physical sign type is the physical sign data obtained by summing the data change information of each physical sign type and the initial physical sign data of each physical sign type of the user at the initial detection, and the physical sign change analysis strategy of each physical sign type includes the corresponding relationship between each physical sign data range and the physical sign state. The terminal adapts the physical sign state of this physical sign type based on this corresponding relationship.

[0076] The terminal takes the current physical sign data of each physical sign type and the current physical sign state of each physical sign type as the user's physical sign data, and takes the sub-detection abnormal information corresponding to each detection type as the user's condition query information.

[0077] Based on the above solution, by analyzing the data change information of each physical sign type, the current physical sign data and the current physical sign state of each physical sign type are identified, improving the comprehensiveness and accuracy of the identification of physical sign data and physical sign state.

[0078] Optionally, based on the user's condition analysis information, identifying the user's abnormal area and the user's abnormal information includes: identifying the area judgment results of each user body area from the user's condition analysis information, and for each user body area, based on the area judgment result of the user body area, identifying the area abnormal information and the area abnormal range of the user body area; based on the area abnormal information of the user body area, querying the abnormal probability of each area abnormal type of the user body area in the database, and based on each area abnormal type and the area abnormal range, screening the target area abnormal type of the user body area; taking the user body area with the target area abnormal type as the user's abnormal area, and taking the target area abnormal type of each user abnormal area and the area abnormal range of each user abnormal area as the user's abnormal information.

[0079] In this embodiment, the terminal analyzes the user's condition information, identifies the area judgment results of each user's body area, and for each user's body area, based on the area judgment results of the user's body area, identifies the area abnormality information of the user's body area and the area abnormality range of the user's body area. Among them, the area abnormality information includes the condition information of the user's body area, and the condition information includes the medical staff's condition diagnosis results of the user's body area. For example, the area abnormality information of the lung area is pneumonia, bronchitis, lung lesions, lung cancer, etc., and the area abnormality range is the area in the user's body area where there is area abnormality information. For example, the area abnormality range of pneumonia is the lung lobe area, and the area abnormality range of bronchitis is the bronchial area.

[0080] Based on the area abnormality information of the user's body area, the terminal queries the abnormality probabilities of each area abnormality type of the user's body area in the database, and based on each area abnormality type and the area abnormality range, filters the target area abnormality type of the user's body area. Among them, the area abnormality type includes but is not limited to the area abnormality types adapted to different user body areas. For example, the area abnormality types of the lung body area include but are not limited to lung inflammation types, lung infection types, lung structure change types, lung cancer types, and lung damage types, etc. And in the database, there is a corresponding relationship between the area abnormality information range of each area abnormality type of different user body areas and the abnormality probability of this area abnormality type.

[0081] After that, the terminal takes the user's body area with the target area abnormality type as the user's abnormal area, and takes the target area abnormality type of each user's abnormal area and the area abnormality range of each user's abnormal area as the user's user abnormality information.

[0082] Based on the above solution, by identifying the abnormality probabilities of each area abnormality type of different user body areas, the target area abnormality type of each user's abnormal area is filtered, and the area abnormality range of each user's abnormal area is identified, which improves the comprehensiveness and accuracy of the identification of the user's user abnormality information.

[0083] Optionally, based on the user's physical sign data and the user's disease question information, through the image adaptation network, identify the range of image detection parameters corresponding to each image detection type adapted by the user, including: based on the current physical sign data of each physical sign type of the user, query in the detection taboo database corresponding to each image detection type for each target image detection type adapted by the user; based on the sub-detection abnormal information corresponding to each detection type, identify the abnormal degree value of each physical abnormality type of the user, and based on the abnormal degree value of each physical abnormality type, query the body adaptation range corresponding to each physical abnormality type through the physical abnormality analysis strategy; based on the body adaptation range corresponding to each physical abnormality type and each target image detection type adapted by the user, through the image adaptation network, identify the range of image detection parameters corresponding to each target image detection type adapted by the user.

[0084] In this embodiment, the terminal queries, in the detection taboo database corresponding to each image detection type, each target image detection type adapted by the user based on the current physical sign data of each physical sign type of the user. Among them, the detection taboo database is the range of the current physical sign data of each physical sign type that is not suitable for this image detection type. Among them, the ranges that need to be considered are, for example,

[0085] Age and weight: Children and obese patients may need parameter adjustment.

[0086] Medical history and allergy history: Patients allergic to contrast agents need to avoid using iodine-containing or gadolinium-containing contrast agents.

[0087] Pregnancy status: Pregnant women need to carefully select imaging techniques to avoid unnecessary radiation exposure.

[0088] Subsequently, the terminal identifies the abnormal degree value of each physical abnormality type of the user based on the sub-detection abnormal information corresponding to each detection type, and based on the abnormal degree value of each physical abnormality type, queries the body adaptation range corresponding to each physical abnormality type through the physical abnormality analysis strategy. Among them, the abnormal degree value of each physical abnormality type has a corresponding relationship with the range of sub-detection abnormal information corresponding to each detection type, and the terminal identifies the abnormal degree value of each physical abnormality type through the above corresponding relationship.

[0089] Finally, the terminal identifies the range of image detection parameters corresponding to each target image detection type adapted by the user through the image adaptation network based on the body adaptation range corresponding to each physical abnormality type and each target image detection type adapted by the user.

[0090] The obtained range of image detection parameters corresponding to each target image detection type is, for example, for X-ray and CT: voltage (kVp) and current (mA): image contrast and resolution.

[0091] Slice thickness: Determines the fineness of the scan.

[0092] Scan range: The coverage area from the starting point to the ending point.

[0093] Reconstruction algorithm: Affects the image clarity and noise level.

[0094] MRI: Appropriate sequences can be selected to shorten the examination time.

[0095] Magnetic field strength: Usually 1.5T or 3T.

[0096] Sequence type: Such as T1-weighted, T2-weighted, FLAIR, etc.

[0097] Slice thickness and interval: Affect the image resolution and coverage.

[0098] Scan time: Adjusted according to the patient's tolerance and diagnostic needs.

[0099] Ultrasound:

[0100] Probe frequency: High frequency for superficial tissues and low frequency for deep tissues.

[0101] Gain and depth: Optimize the image clarity and display range.

[0102] Doppler setting: Used for blood flow assessment.

[0103] Nuclear medicine imaging:

[0104] Radioactive tracer dose: Adjusted according to the examination type and patient weight.

[0105] Acquisition time: Set according to the tracer distribution and metabolism.

[0106] Reconstruction algorithm: Affects the image resolution and noise level.

[0107] Based on the above scheme, by combining the current physical sign data of each physical sign type of the user, it is determined whether there is a suitable imaging detection type for the user, and the imaging detection parameter ranges corresponding to each target imaging detection type suitable for the user are screened, thereby further reducing the degree of physical imaging of the user by the imaging detection type and the imaging detection cost.

[0108] Optionally, based on the user's abnormal region and the user's abnormal information, a target inspection region of the user is generated, including: based on the target region abnormal type of each user abnormal region, querying the initial region detection range of each user abnormal region through a detection database; based on the region abnormal range of each user abnormal region and the initial region detection range of each user abnormal region, screening the target region detection range of each user abnormal region through a range clustering strategy; and using the target region detection ranges of all user abnormal regions as the target inspection region of the user.

[0109] In this embodiment, the terminal queries the initial region detection range of each user abnormal region through the detection database based on the target region abnormal type of each user abnormal region. Among them, the initial region detection range is the region detection range generally adapted to the target region abnormal type of the user. Among them, the region detection range is the range for detecting each physical abnormal region of the user.

[0110] Subsequently, the terminal screens the target region detection range of each user abnormal region through a range clustering strategy based on the region abnormal range of each user abnormal region and the initial region detection range of each user abnormal region. The target region detection range is the detection range that the user needs to perform region detection, and this detection range is the overlapping range of the region abnormal range and the initial region detection range.

[0111] Finally, the terminal uses the target region detection ranges of all user abnormal regions as the target inspection region of the user.

[0112] Based on the above solution, by screening the range, the actual image range of the patient is reduced, and the detection cost optimization for the patient and the optimization effect of the detected body image are improved.

[0113] Optionally, based on the image detection parameter ranges corresponding to each image detection type adapted to the user and the target inspection region of the user, an image detection scheme of the user is generated, including: for each image detection type, querying each detection scheme corresponding to the image detection type in the detection database of the image detection type based on the image detection parameter range of the image detection type; evaluating the detection evaluation value of each initial detection scheme through an image detection index evaluation strategy based on each initial detection scheme of the image detection type, and screening the initial detection scheme corresponding to the highest detection evaluation value as the target detection scheme of the image detection type; generating a sub-image detection scheme of the user in the image detection type through the target detection scheme of the image detection type based on the target region detection ranges of the user's abnormal regions, and using the sub-image detection schemes of all image detection types as the image detection scheme of the user.

[0114] In this embodiment, for each image detection type, the terminal queries each detection scheme corresponding to the image detection type in the detection database of the image detection type based on the image detection parameter range of the image detection type. Among them, in the detection database of each image detection type, the corresponding relationship between different image detection ranges and detection schemes is stored, and the terminal adapts each detection scheme corresponding to each image detection type based on this corresponding relationship.

[0115] Subsequently, based on each initial detection scheme of the image detection type, the terminal evaluates the detection evaluation value of each initial detection scheme through the image detection index evaluation strategy, and screens out the initial detection scheme corresponding to the highest detection evaluation value as the target detection scheme of the image detection type.

[0116] Finally, based on the target area detection range of each user abnormal area of the user, the terminal generates a sub-image detection scheme of the user for the image detection type through the target detection scheme of the image detection type, and uses all the sub-image detection schemes of the image detection types as the image detection scheme of the user.

[0117] Based on the above solution, by adapting the detection scheme and then evaluating the detection index, the target detection scheme is screened out, which improves the comprehensiveness and accuracy of the detection of the user.

[0118] It should be understood that although each step in the flowchart involved in the above-mentioned embodiments is displayed in sequence according to the indication of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0119] Based on the same inventive concept, the embodiment of the present application also provides an image detection scheme generation system for implementing the above-mentioned image detection scheme generation method. The solution provided by this system to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the image detection scheme generation system provided below can refer to the limitations on the image detection scheme generation method in the above text, and will not be repeated here.

[0120] Further reference Figure 2 to Figure 1For the implementation of the method described above, the present application provides an embodiment of a system 200 for generating an imaging detection solution. The system for generating an imaging detection solution includes an acquisition module 210, an identification module 220, and a generation module 230, where:

[0121] The acquisition module 210 is configured to acquire the initial examination information of the user and the medical staff's analysis information on the user's condition, and based on the initial examination information of the user, identify the user's physical sign data and the user's condition question information;

[0122] The identification module 220 is configured to, based on the medical staff's analysis information on the user's condition, identify the user's abnormal area and the user's abnormal information, and based on the user's physical sign data and the user's condition question information, through an image adaptation network, identify the range of imaging detection parameters corresponding to each imaging detection type adapted to the user;

[0123] The generation module 230 is configured to, based on the user's abnormal area and the user's abnormal information, generate the user's target examination area, and based on the range of imaging detection parameters corresponding to each imaging detection type adapted to the user and the user's target examination area, generate the user's imaging detection solution.

[0124] Optionally, the acquisition module 210 is specifically configured to:

[0125] Split the initial examination information of the user into the detection data of each detection type and the data change information of each physical sign type of the user, and based on the detection data of each detection type, through the detection analysis strategy of each detection type, identify the sub-detection abnormal information corresponding to each detection type;

[0126] Based on the data change information of each physical sign type, through the physical sign change analysis strategy of each preset physical sign type, identify the current physical sign data of each physical sign type and the current physical sign state of each physical sign type, and use the current physical sign data of each physical sign type and the current physical sign state of each physical sign type as the user's physical sign data;

[0127] Use the sub-detection abnormal information corresponding to each detection type as the user's condition question information.

[0128] Optionally, the identification module 220 is specifically configured to:

[0129] Analyze the condition information of the user, identify the area judgment results of each user's body area, and for each user's body area, based on the area judgment results of the user's body area, identify the area abnormal information of the user's body area and the area abnormal range of the user's body area;

[0130] Based on the area abnormal information of the user's body area, query the abnormal probabilities of each area abnormal type of the user's body area in the database, and based on each area abnormal type and the area abnormal range, screen the target area abnormal type of the user's body area;

[0131] Take the user's body area with the target area abnormal type as the user's abnormal area, and take the target area abnormal type of each user's abnormal area and the area abnormal range of each user's abnormal area as the user's user abnormal information.

[0132] Optionally, the recognition module 220 is specifically configured to:

[0133] Based on the current physical sign data of each physical sign type of the user, query each target imaging detection type suitable for the user in the detection taboo database corresponding to each imaging detection type;

[0134] Based on the sub-detection abnormal information corresponding to each detection type, identify the abnormal degree value of each body abnormal type of the user, and based on the abnormal degree value of each body abnormal type, query the body adaptation range corresponding to each body abnormal type through the body abnormal analysis strategy;

[0135] Based on the body adaptation range corresponding to each body abnormal type and each target imaging detection type suitable for the user, identify the imaging detection parameter range corresponding to each target imaging detection type suitable for the user through the imaging adaptation network.

[0136] Optionally, the generation module 230 is specifically configured to:

[0137] Based on the target area abnormal type of each user's abnormal area, query the initial area detection range of each user's abnormal area through the detection database;

[0138] Based on the area abnormal range of each user's abnormal area and the initial area detection range of each user's abnormal area, screen the target area detection range of each user's abnormal area through the range clustering strategy;

[0139] Take the target area detection ranges of all user abnormal areas as the target examination area of the user.

[0140] Optionally, the generation module 230 is specifically configured to:

[0141] For each image detection type, based on the image detection parameter range of the image detection type, query each detection scheme corresponding to the image detection type in the detection database of the image detection type;

[0142] Based on each initial detection scheme of the image detection type, through the image detection index evaluation strategy, evaluate the detection evaluation value of each initial detection scheme, and screen out the initial detection scheme corresponding to the highest detection evaluation value as the target detection scheme of the image detection type;

[0143] Based on the target area detection range of each user abnormal area of the user, through the target detection scheme of the image detection type, generate a sub-image detection scheme of the user for the image detection type, and use all the sub-image detection schemes of the image detection types as the image detection scheme of the user.

[0144] Each module in the above image detection scheme generation system can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0145] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input system connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication) or other technologies. When the computer program is executed by the processor, it realizes a method for generating an image detection scheme. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer device can be a touch layer covered on the display screen, or can be a button, a trackball or a touchpad set on the shell of the computer device, or can also be an external keyboard, a touchpad or a mouse, etc.

[0146] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0147] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any item of the first aspect are implemented.

[0148] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any item of the first aspect are implemented.

[0149] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the method described in any item of the first aspect are implemented.

[0150] It should be noted that the patient information (including but not limited to patient device information, patient personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the patient or fully authorized by all parties.

[0151] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0152] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0153] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for generating an image detection solution, characterized in that, The method includes: Obtaining the initial examination information of the user and the medical staff's analysis information on the user's condition, and based on the initial examination information of the user, identifying the user's physical sign data and the user's condition question information; Based on the medical staff's analysis information on the user's condition, identifying the user's abnormal area and the user's abnormal information, and based on the user's physical sign data and the user's condition question information, through an image adaptation network, identifying the range of image detection parameters corresponding to each image detection type suitable for the user; Based on the user's abnormal area and the user's abnormal information, generating the user's target examination area, and based on the range of image detection parameters corresponding to each image detection type suitable for the user and the user's target examination area, generating the user's image detection plan.

2. The method according to claim 1, characterized in that The identifying the user's physical sign data and the user's condition question information based on the user's initial examination information includes: Splitting the user's initial examination information into the detection data of each detection type and the data change information of each physical sign type of the user, and based on the detection data of each detection type, through the detection analysis strategy of each detection type, identifying the sub-detection abnormal information corresponding to each detection type; Based on the data change information of each physical sign type, through the physical sign change analysis strategy of each preset physical sign type, identifying the current physical sign data of each physical sign type and the current physical sign state of each physical sign type, and taking the current physical sign data of each physical sign type and the current physical sign state of each physical sign type as the user's physical sign data; Taking the sub-detection abnormal information corresponding to each detection type as the user's condition question information.

3. The method according to claim 1, wherein The identifying the user's abnormal area and the user's abnormal information based on the medical staff's analysis information on the user's condition includes: Identifying the regional judgment result of each user body area from the medical staff's analysis information on the user's condition, and for each user body area, based on the regional judgment result of the user body area, identifying the regional abnormal information and the regional abnormal range of the user body area; Based on the regional abnormal information of the user body area, querying in the database the abnormal probability of each regional abnormal type of the user body area, and based on each regional abnormal type and the regional abnormal range, screening the target regional abnormal type of the user body area; Taking the user body area with the target regional abnormal type as the user's abnormal area, and taking the target regional abnormal type of each user abnormal area and the regional abnormal range of each user abnormal area as the user's abnormal information.

4. The method according to claim 2, wherein The identifying the range of image detection parameters corresponding to each image detection type suitable for the user through an image adaptation network based on the user's physical sign data and the user's condition question information includes: Based on the current physical sign data of each physical sign type of the user, query the target imaging detection types suitable for the user in the detection taboo databases corresponding to each imaging detection type; Based on the sub-detection abnormal information corresponding to each detection type, identify the abnormal degree values of each physical abnormality type of the user, and based on the abnormal degree values of each physical abnormality type, query the physical adaptation ranges corresponding to each physical abnormality type through the physical abnormality analysis strategy; Based on the physical adaptation ranges corresponding to each physical abnormality type and the target imaging detection types suitable for the user, identify the imaging detection parameter ranges corresponding to the target imaging detection types suitable for the user through the imaging adaptation network.

5. The method according to claim 1, wherein The generating the target examination area of the user based on the user abnormal area and the user abnormal information of the user includes: Based on the target area abnormal types of the user abnormal areas, query the initial area detection ranges of each user abnormal area through the detection database; Based on the area abnormal ranges of each user abnormal area and the initial area detection ranges of each user abnormal area, screen the target area detection ranges of each user abnormal area through the range clustering strategy; Use the target area detection ranges of all user abnormal areas as the target examination area of the user.

6. The method according to claim 5, characterized in that The generating the imaging detection plan of the user based on the imaging detection parameter ranges corresponding to the imaging detection types suitable for the user and the target examination area of the user includes: For each imaging detection type, query the detection plans corresponding to the imaging detection type in the detection database of the imaging detection type based on the imaging detection parameter ranges of the imaging detection type; Based on the initial detection plans of the imaging detection type, evaluate the detection evaluation values of each initial detection plan through the imaging detection index evaluation strategy, and screen the initial detection plan corresponding to the highest detection evaluation value as the target detection plan of the imaging detection type; Based on the target area detection ranges of the user abnormal areas of the user, generate the sub-imaging detection plans of the user in the imaging detection type through the target detection plan of the imaging detection type, and use the sub-imaging detection plans of all imaging detection types as the imaging detection plan of the user.

7. A generation system for an image detection solution, characterized in that, The system includes: An acquisition module, configured to acquire the preliminary examination information of the user and the medical staff's analysis information on the user's condition, and based on the preliminary examination information of the user, identify the user's physical sign data and the user's condition question information; An identification module, configured to identify the user's abnormal area and the user's abnormal information based on the medical staff's analysis information on the user's condition, and identify the imaging detection parameter ranges corresponding to the imaging detection types suitable for the user through the imaging adaptation network based on the user's physical sign data and the user's condition question information; A generation module, configured to generate a target inspection area of the user based on the user's abnormal area and the user's abnormal information, and generate an imaging detection scheme for the user based on the imaging detection parameter ranges corresponding to each imaging detection type adapted to the user and the target inspection area of the user.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When this computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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