Fertility auxiliary judgment method and system for hysteromyoma patient

Abstract

The invention provides a fertility auxiliary judgment method and system for a hysteromyoma patient, and the method comprises the steps: obtaining a sample detection result and a scanning graph of the hysteromyoma of the patient, and recognizing the sample detection data of each detection type of the hysteromyoma based on the sample detection result; identifying three-dimensional structure data and position distribution information of the hysteromyoma on the basis of the scanning graph of the hysteromyoma, and identifying a detection evaluation value of each detection type through a sample evaluation strategy on the basis of the sample detection data of each detection type; based on the three-dimensional structure data and the position distribution information of the hysteromyoma, identifying the fertility influence degree of the hysteromyoma on the fertility influence type of the patient, and based on the detection evaluation value of each detection type and the fertility influence degree of the fertility influence type, identifying the fertility probability value of the patient through a fertility influence evaluation model; and taking the fertility probability value of the patient as a fertility auxiliary judgment result of the patient. By adopting the scheme, the evaluation accuracy of the fertility can be improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and intelligent medical technology, and in particular to a method and system for assisting in determining the fertility of patients with uterine fibroids. Background Art

[0002] When a user has uterine fibroids, it often affects the user's fertility and fertility level. Therefore, when medical staff find that a user has uterine fibroids, they often need to evaluate and analyze the impact of the uterine fibroids on the user's fertility. Therefore, how to assist medical staff in evaluating the user's fertility is the current research focus.

[0003] The traditional method of fertility assessment is for medical staff to conduct independent analysis and combine it with the user's test results to directly determine the user's fertility. However, this method can only analyze approximate fertility information and is affected by the work experience and work level of medical staff, resulting in low accuracy in fertility assessment. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and system for assisting in determining the fertility of patients with uterine fibroids, aiming to solve the problem of low accuracy in fertility assessment in the prior art, as only approximate fertility information can be analyzed and the system is affected by the work experience and work level of medical staff.

[0005] To achieve the above object, the present invention provides a method for assisting in determining the fertility of patients with uterine fibroids, the method comprising:

[0006] Obtaining a sample test result of a patient's uterine fibroids and a scan of the uterine fibroids, and identifying sample test data of each test type of the uterine fibroids based on the sample test result;

[0007] Based on the scanned image of the uterine fibroid, the three-dimensional structural data of the uterine fibroid and the position distribution information of the uterine fibroid in the uterus are identified, and based on the sample detection data of each detection type, the detection evaluation value of each detection type is identified through a sample evaluation strategy;

[0008] Based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type are identified; based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, the fertility probability value of the patient is identified through a fertility impact assessment model;

[0009] The patient's fertility probability value is used as an auxiliary judgment result of the patient's fertility.

[0010] Optionally, the identifying the sample detection data of each detection type of the uterine fibroid based on the sample detection result includes:

[0011] Splitting the sample test results into initial test data of each test type, and for each test type, extracting target test data related to fertility from the initial test data of the test type;

[0012] The target detection data is used as sample detection data of the detection type.

[0013] Optionally, the identifying the three-dimensional structural data of the uterine fibroid and the position distribution information of the uterine fibroid in the uterus based on the scanned image of the uterine fibroid includes:

[0014] Based on the scanned image of the uterine fibroid, a three-dimensional structural model of the patient's uterus is constructed, and based on the three-dimensional structural model of the uterus, three-dimensional structural edge data of the uterine fibroid and position range information of the uterine fibroid in the uterus are identified by an edge recognition algorithm;

[0015] Based on the three-dimensional data of the structural edge of the uterine fibroid, the three-dimensional structural data of the uterine fibroid is identified, and based on the position range information of the uterine fibroid in the uterus, the position distribution information of the uterine fibroid is identified.

[0016] Optionally, the identifying the detection evaluation value of each detection type through a sample evaluation strategy based on the sample detection data of each detection type includes:

[0017] Based on the sample test data of each test type, identifying the test data range to which the sample test data of each test type belongs through the sample evaluation strategy of each test type;

[0018] Based on the detection data range described by the sample detectable data of each detection type, the evaluation value corresponding to each detection data range is queried in the detection database, and the evaluation value of the detection data range corresponding to each detection type is used as the detection evaluation value of each detection type.

[0019] Optionally, the identifying the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus includes:

[0020] Based on the position distribution information of the uterine fibroids in the uterus, identifying the uterine region where the uterine fibroids are located, and the position distribution characteristics of the uterine fibroids in the uterine region;

[0021] In the fertility impact database, query each fertility impact type corresponding to each uterine region and the position distribution feature range corresponding to each fertility impact type, and query the position distribution feature range to which the position distribution feature of the uterine fibroid in the uterine region belongs, to determine the fertility impact type of the uterine fibroid on the patient;

[0022] Based on the three-dimensional structural data of the uterine fibroids, the structural shape of the uterine fibroids and the structural range of the uterine fibroids are identified, and based on the structural shape of the uterine fibroids and the structural range of the uterine fibroids, the fertility impact degree of the remaining impact type corresponding to the uterine fibroids is identified through the fertility impact identification network corresponding to the fertility impact type.

[0023] Optionally, the detecting evaluation value based on each of the detecting types and the degree of fertility impact of the uterine fibroids on the fertility of the patient is used to identify the fertility probability value of the patient through a fertility impact assessment model, including:

[0024] Based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, identifying the patient's fertility impact evaluation value through a fertility impact evaluation model;

[0025] Obtaining a weight value of the fertility impact type corresponding to the patient, and based on the weight value of the fertility impact type, performing weighted calculation on the fertility impact evaluation value of the fertility impact type to obtain a target fertility impact evaluation value of the patient;

[0026] Based on the target fertility impact evaluation value, the fertility impact database is queried to obtain the fertility probability value of the patient.

[0027] In addition, to achieve the above-mentioned purpose, the present invention also provides a fertility auxiliary judgment system for patients with uterine fibroids, the fertility auxiliary judgment system for patients with uterine fibroids comprising:

[0028] An acquisition module, used to acquire a sample test result of a patient's uterine fibroids and a scan of the uterine fibroids, and identify sample test data of each test type of the uterine fibroids based on the sample test result;

[0029] an identification module, for identifying the three-dimensional structural data of the uterine fibroid and the position distribution information of the uterine fibroid in the uterus based on the scanned image of the uterine fibroid, and identifying the detection evaluation value of each detection type through a sample evaluation strategy based on the sample detection data of each detection type;

[0030] An evaluation module, for identifying the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, and identifying the fertility probability value of the patient through a fertility impact evaluation model based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type;

[0031] The determination module is used to use the patient's fertility probability value as the patient's fertility auxiliary judgment result.

[0032] Optionally, the acquisition module is specifically used to:

[0033] Splitting the sample test results into initial test data of each test type, and for each test type, extracting target test data related to fertility from the initial test data of the test type;

[0034] The target detection data is used as sample detection data of the detection type.

[0035] Optionally, the identification module is specifically used to:

[0036] Based on the scanned image of the uterine fibroid, a three-dimensional structural model of the patient's uterus is constructed, and based on the three-dimensional structural model of the uterus, three-dimensional structural edge data of the uterine fibroid and position range information of the uterine fibroid in the uterus are identified by an edge recognition algorithm;

[0037] Based on the three-dimensional data of the structural edge of the uterine fibroid, the three-dimensional structural data of the uterine fibroid is identified, and based on the position range information of the uterine fibroid in the uterus, the position distribution information of the uterine fibroid is identified.

[0038] Optionally, the identification module is specifically used to:

[0039] Based on the sample test data of each test type, identifying the test data range to which the sample test data of each test type belongs through the sample evaluation strategy of each test type;

[0040] Based on the detection data range described by the sample detectable data of each detection type, the evaluation value corresponding to each detection data range is queried in the detection database, and the evaluation value of the detection data range corresponding to each detection type is used as the detection evaluation value of each detection type.

[0041] Optionally, the evaluation module is specifically used to:

[0042] Based on the position distribution information of the uterine fibroids in the uterus, identifying the uterine region where the uterine fibroids are located, and the position distribution characteristics of the uterine fibroids in the uterine region;

[0043] In the fertility impact database, query each fertility impact type corresponding to each uterine region and the position distribution feature range corresponding to each fertility impact type, and query the position distribution feature range to which the position distribution feature of the uterine fibroid in the uterine region belongs, to determine the fertility impact type of the uterine fibroid on the patient;

[0044] Based on the three-dimensional structural data of the uterine fibroids, the structural shape of the uterine fibroids and the structural range of the uterine fibroids are identified, and based on the structural shape of the uterine fibroids and the structural range of the uterine fibroids, the fertility impact degree of the remaining impact type corresponding to the uterine fibroids is identified through the fertility impact identification network corresponding to the fertility impact type.

[0045] Optionally, the evaluation module is specifically used to:

[0046] Based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, identifying the patient's fertility impact evaluation value through a fertility impact evaluation model;

[0047] Obtaining a weight value of the fertility impact type corresponding to the patient, and based on the weight value of the fertility impact type, performing weighted calculation on the fertility impact evaluation value of the fertility impact type to obtain a target fertility impact evaluation value of the patient;

[0048] Based on the target fertility impact evaluation value, the fertility impact database is queried to obtain the fertility probability value of the patient.

[0049] In a third aspect, the present application provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0051] 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 any one of the methods in the first aspect are implemented.

[0052] The present invention provides a method and system for auxiliary determination of fertility of patients with uterine fibroids. The method comprises: obtaining sample detection results of the patient's uterine fibroids and a scanned image of the uterine fibroids, and identifying sample detection data of each detection type of the uterine fibroids based on the sample detection results; identifying three-dimensional structural data of the uterine fibroids and position distribution information of the uterine fibroids in the uterus based on the scanned image of the uterine fibroids, and identifying detection evaluation values ​​of each detection type through a sample evaluation strategy based on the sample detection data of each detection type; identifying the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, and identifying the fertility probability value of the patient through a fertility impact assessment model based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type; and using the fertility probability value of the patient as the auxiliary determination result of the fertility of the patient. This scheme combines the sample test results of the patient's uterine fibroids and the scanned images of the uterine fibroids to comprehensively analyze the uterine fibroids from the pathological perspective of the uterine fibroids and the physical perspective of the uterine fibroids, and identifies the sample test data of each test type of uterine fibroids and the degree of fertility impact of the fertility impact type. Then, from the above-identified data information, the patient's fertility probability value is identified through the trained fertility impact assessment model, thereby obtaining the patient's fertility auxiliary judgment result. This method avoids the inefficiency, limitations, and subjective experience of manual analysis. In addition, when analyzing the probability of fertility, the precise quantitative means driven by the constructed fertility impact assessment model improves the scientificity and reliability of the fertility assessment of patients with uterine fibroids, provides clinicians with decision support based on personalized data, and helps to formulate individualized diagnosis and treatment plans. Thereby comprehensively improving the accuracy of fertility assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 is a flow chart of a method for assisting in determining fertility of a patient with uterine fibroids provided by an embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of the structure of the auxiliary fertility determination system for patients with uterine fibroids provided by an embodiment of the present invention;

[0056] Figure 3 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The fertility auxiliary judgment method for patients with uterine fibroids provided by the embodiment of the present invention is applied to the fertility auxiliary judgment system for patients with uterine fibroids. Unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by technicians in the technical field of this application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned drawings and any variations thereof are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0060] The fertility auxiliary judgment method for uterine fibroid patients provided in the embodiment of the present application can be applied to the fertility auxiliary judgment application environment of uterine fibroid patients. Among them, the method can be applied to a terminal, a server, or a system including a terminal and a server, and is realized through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, etc. Among them, the terminal combines the sample test results of the patient's uterine fibroids and the scanned image of the uterine fibroids, thereby comprehensively analyzing the uterine fibroids from the pathological perspective of the uterine fibroids and the physical perspective of the uterine fibroids, identifying the sample test data of each detection type of the uterine fibroids, and the degree of fertility impact of the fertility impact type. Then, from the above-identified data information, the fertility probability value of the patient is identified through the trained fertility impact assessment model, thereby obtaining the patient's fertility auxiliary judgment result. This method avoids the inefficiency, limitations, and subjective experience of manual analysis, and when analyzing the fertility probability, the precise quantitative means driven by the constructed fertility impact assessment model improves the scientificity and reliability of the fertility assessment of patients with uterine fibroids, provides clinicians with decision support based on personalized data, and helps to formulate individualized diagnosis and treatment plans. This improves the accuracy of fertility assessment.

[0061] In one embodiment, Figure 1 As shown, a method for assisting in determining the fertility of patients with uterine fibroids is provided, and the method is applied to a terminal as an example for explanation, and includes the following steps:

[0062] Step S101, obtaining the sample test results of the patient's uterine fibroids and the scanned image of the uterine fibroids, and identifying the sample test data of each test type of the uterine fibroids based on the sample test results.

[0063] In this embodiment, the terminal obtains the test results of the uterine fibroid sample of the patient by receiving the sample test data transmitted by each detection instrument. Then, the terminal performs a development scan on the patient's uterine area through magnetic resonance imaging (MRI) scanning technology to obtain a scan of the uterine area. Then, based on the sample test results, the terminal identifies the sample test data of each test type of uterine fibroid. Among them, each test type includes but is not limited to a pathological test type, a cancer cell test type, and a cancer cell distribution test type.

[0064] Step S102, based on the scan image of the uterine fibroids, identify the three-dimensional structure data of the uterine fibroids and the location distribution information of the uterine fibroids in the uterus, and based on the sample detection data of each detection type, identify the detection evaluation value of each detection type through a sample evaluation strategy.

[0065] In this embodiment, the terminal identifies the three-dimensional structure data of the uterine fibroids and the location distribution information of the uterine fibroids in the uterus based on the scanned image of the uterine fibroids, and identifies the detection evaluation value of each detection type through the sample evaluation strategy based on the sample detection data of each detection type. Among them, the detection evaluation value of each detection type is the evaluation value of the impact of the detection type on fertility. The specific identification process will be described in detail later.

[0066] Step S103, based on the three-dimensional structural data of uterine fibroids and the location distribution information of uterine fibroids in the uterus, identify the type of fertility impact of uterine fibroids on the patient and the degree of fertility impact of the fertility impact type, and based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, identify the patient's fertility probability value through a fertility impact assessment model.

[0067] In this embodiment, the terminal identifies the type of fertility impact of uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, and identifies the patient's fertility probability value through a fertility impact assessment model based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type. In particular, when the uterine position of the uterine fibroid is different, the type of fertility impact of the uterine fibroid on the patient is different, and the three-dimensional structural data of the uterine fibroids is used to identify the degree of impact of the uterine fibroids on the patient's fertility impact type, and the specific identification process will be described in detail later.

[0068] Step S104, using the patient's fertility probability value as the patient's fertility auxiliary judgment result.

[0069] In this embodiment, the terminal uses the patient's fertility probability value as the patient's fertility auxiliary judgment result.

[0070] Based on the above scheme, by combining the sample test results of the patient's uterine fibroids and the scanned images of the uterine fibroids, a comprehensive analysis of the uterine fibroids is performed from the pathological perspective of the uterine fibroids and the physical perspective of the uterine fibroids, and the sample test data of each test type of the uterine fibroids and the degree of fertility impact of the fertility impact type are identified. Then, from the above-identified data information, the patient's fertility probability value is identified through the trained fertility impact assessment model, thereby obtaining the patient's fertility auxiliary judgment result. This method avoids the inefficiency, limitations, and subjective experience of manual analysis. Moreover, when analyzing the fertility probability, the precise quantitative means driven by the constructed fertility impact assessment model are used to improve the scientificity and reliability of the fertility assessment of patients with uterine fibroids, and provide clinicians with decision support based on personalized data, which is helpful for formulating individualized diagnosis and treatment plans. Thereby comprehensively improving the accuracy of fertility assessment.

[0071] Optionally, based on the sample test results, sample test data of each test type of uterine fibroids is identified, including: splitting the sample test results into initial test data of each test type, and for each test type, extracting target test data related to fertility from the initial test data of the test type; and using the target test data as sample test data of the test type.

[0072] In this embodiment, the terminal splits the sample test results into initial test data of each test type, and extracts target test data related to fertility from the initial test data of each test type. The terminal presets a test data range related to fertility, and then the terminal directly filters the test data belonging to the test data range in the initial test data of each test type to obtain the target test data. Finally, the terminal uses the target test data as the sample test data of the test type.

[0073] Based on the above solution, by splitting the sample test results and screening the target test data related to fertility, the accuracy of data analysis for each test type is improved.

[0074] Optionally, based on a scan image of the uterine fibroids, the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus are identified, including: based on the scan image of the uterine fibroids, a three-dimensional structural model of the patient's uterus is constructed, and based on the three-dimensional structural model of the uterus, through an edge recognition algorithm, the three-dimensional data of the structural edges of the uterine fibroids and the position range information of the uterine fibroids in the uterus are identified; based on the three-dimensional data of the structural edges of the uterine fibroids, the three-dimensional structural data of the uterine fibroids are identified, and based on the position range information of the uterine fibroids in the uterus, the position distribution information of the uterine fibroids is identified.

[0075] In this embodiment, the terminal constructs a three-dimensional structural model of the patient's uterus based on the scanned image of the uterine fibroids, and identifies the three-dimensional structural edge data of the uterine fibroids and the position range information of the uterine fibroids in the uterus through an edge recognition algorithm based on the three-dimensional structural model of the uterus. The edge recognition algorithm is an image edge recognition algorithm, which is used to identify the range surrounded by the three-dimensional edge information of the uterine fibroids in the three-dimensional structural model of the uterus.

[0076] Then, the terminal identifies the three-dimensional structural data of the uterine fibroids based on the three-dimensional structural edge data of the uterine fibroids, and identifies the position distribution information of the uterine fibroids based on the position range information of the uterine fibroids in the uterus.

[0077] Based on the above scheme, after edge recognition is performed through an edge recognition algorithm, the three-dimensional structural data of the uterine fibroids and the location distribution information of the uterine fibroids are analyzed, thereby improving the accuracy of the structural analysis of the uterine fibroids.

[0078] Optionally, based on the sample detection data of each detection type, a detection evaluation value of each detection type is identified through a sample evaluation strategy, including: based on the sample detection data of each detection type, identifying the detection data range to which the sample detection data of each detection type belongs through the sample evaluation strategy of each detection type; based on the detection data range of the sample detectable data of each detection type, querying the evaluation value corresponding to each detection data range in the detection database, and using the evaluation value of the detection data range corresponding to each detection type as the detection evaluation value of each detection type.

[0079] In this embodiment, the terminal identifies the detection data range to which the sample detection data of each detection type belongs based on the sample detection data of each detection type and through the sample evaluation strategy of each detection type, wherein the sample evaluation strategy includes the detection data range corresponding to each evaluation value.

[0080] Then, based on the detection data range of the sample detectable data of each detection type, the terminal queries the evaluation value corresponding to each detection data range in the detection database, and uses the evaluation value of the detection data range corresponding to each detection type as the detection evaluation value of each detection type.

[0081] Based on the above scheme, sample evaluation is carried out through the sample evaluation strategy of each detection type, which improves the comprehensiveness and accuracy of the evaluation of each detection type.

[0082] Optionally, based on the three-dimensional structural data of uterine fibroids and the position distribution information of uterine fibroids in the uterus, the type of fertility impact of uterine fibroids on the patient and the degree of fertility impact of the fertility impact type are identified, including: based on the position distribution information of uterine fibroids in the uterus, identifying the uterine region where the uterine fibroids are located and the position distribution characteristics of the uterine fibroids in the uterine region; in the fertility impact database, querying each fertility impact type corresponding to each uterine region and the position distribution characteristic range corresponding to each fertility impact type, and querying the position distribution characteristic range to which the position distribution characteristics of the uterine fibroids in the uterine region belong, to determine the type of fertility impact of uterine fibroids on the patient; based on the three-dimensional structural data of uterine fibroids, identifying the structural shape of the uterine fibroids and the structural range of the uterine fibroids, and based on the structural shape of the uterine fibroids and the structural range of the uterine fibroids, identifying the degree of fertility impact of the remaining impact types corresponding to the uterine fibroids through a fertility impact identification network corresponding to the fertility impact type.

[0083] In this embodiment, the terminal identifies the uterine region where the uterine fibroids are located and the location distribution characteristics of the uterine fibroids in the uterine region based on the location distribution information of the uterine fibroids in the uterus. The location distribution characteristics are the distribution modes of the uterine fibroids in the uterine region, for example, attached to the surface of the uterine wall, embedded in the surface of the uterine wall, inside the uterine wall, and attached to the wall of the fallopian tube (which may cause blockage of the egg and prevent normal ovulation).

[0084] Then, the terminal queries the fertility impact database for each fertility impact type corresponding to each uterine area, as well as the position distribution characteristic range corresponding to each fertility impact type, and queries the position distribution characteristic range to which the position distribution characteristics of uterine fibroids in the uterine area belong, to determine the type of fertility impact of uterine fibroids on the patient.

[0085] Then, the terminal identifies the structural shape and structural range of the uterine fibroids based on the three-dimensional structural data of the uterine fibroids, and identifies the degree of fertility impact of the fertility impact type corresponding to the uterine fibroids through the fertility impact identification network corresponding to the fertility impact type based on the structural shape and structural range of the uterine fibroids. The fertility impact identification network is a neural network based on reinforcement learning. The neural network can identify the degree of fertility impact based on different structural shapes and structural ranges.

[0086] Based on the above technical solution, by analyzing the uterine region where the uterine fibroids are located and their location distribution characteristics, and then querying the three-dimensional structural data of the uterine fibroids, the fertility impact type corresponding to the uterine fibroids is identified through the fertility impact identification network, thereby improving the accuracy of uterine fibroid identification.

[0087] Optionally, based on the test evaluation values ​​of each test type and the degree of fertility impact of the uterine fibroids on the patient's fertility type, the patient's fertility probability value is identified through a fertility impact assessment model, including: based on the test evaluation values ​​of each test type and the degree of fertility impact of the uterine fibroids on the patient's fertility type, the patient's fertility impact evaluation value is identified through a fertility impact assessment model; the weight value of the fertility impact type corresponding to the patient is obtained, and based on the weight value of the fertility impact type, the fertility impact evaluation value of the fertility impact type is weightedly calculated to obtain the patient's target fertility impact evaluation value; based on the target fertility impact evaluation value, the fertility impact database is queried to obtain the patient's fertility probability value.

[0088] In this embodiment, the terminal identifies the patient's fertility impact evaluation value through a fertility impact evaluation model based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type. The fertility impact evaluation model is a decision tree model. The decision tree model can identify the patient's fertility impact evaluation value based on the detection evaluation values ​​of different detection types and the degree of fertility impact of the fertility impact type.

[0089] Then, the terminal obtains the weight value of the fertility impact type corresponding to the patient, and based on the weight value of the fertility impact type, performs weighted calculation on the fertility impact evaluation value of the fertility impact type to obtain the patient's target fertility impact evaluation value. Finally, based on the target fertility impact evaluation value, the terminal queries the fertility impact database to obtain the patient's fertility probability value. The fertility impact database stores the corresponding relationship between different residual impact evaluation values ​​and fertility probability values.

[0090] Based on the above scheme, the patient's fertility probability value is identified through the trained fertility impact assessment model, thereby obtaining the patient's fertility auxiliary judgment result. This method avoids the inefficiency, limitations, and subjective experience of manual analysis. In addition, when analyzing the fertility probability, the precise quantitative means driven by the constructed fertility impact assessment model are used to improve the scientificity and reliability of the fertility assessment of patients with uterine fibroids, providing clinicians with decision support based on personalized data, which helps to formulate individualized diagnosis and treatment plans.

[0091] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0092] Based on the same inventive concept, the embodiment of the present application also provides a fertility auxiliary judgment system for uterine fibroid patients for implementing the above-mentioned fertility auxiliary judgment method for uterine fibroid patients. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of the fertility auxiliary judgment system for uterine fibroid patients provided below can refer to the limitations of the fertility auxiliary judgment method for uterine fibroid patients above, and will not be repeated here.

[0093] Further references Figure 2 , as a response to the above Figure 1 The present application provides an embodiment of a fertility auxiliary judgment system 200 for patients with uterine fibroids. The fertility auxiliary judgment system for patients with uterine fibroids includes an acquisition module 210, an identification module 220, an evaluation module 230, and a determination module 240, wherein:

[0094] An acquisition module 210 is used to acquire a sample test result of a patient's uterine fibroids and a scan of the uterine fibroids, and identify sample test data of each test type of the uterine fibroids based on the sample test result;

[0095] An identification module 220 is used to identify the three-dimensional structure data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus based on the scanned image of the uterine fibroids, and to identify the detection evaluation value of each detection type through a sample evaluation strategy based on the sample detection data of each detection type;

[0096] An evaluation module 230 is used to identify the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, and to identify the fertility probability value of the patient through a fertility impact evaluation model based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type;

[0097] The determination module 240 is used to use the patient's fertility probability value as the patient's fertility auxiliary judgment result.

[0098] Optionally, the acquisition module 210 is specifically configured to:

[0099] Splitting the sample test results into initial test data of each test type, and for each test type, extracting target test data related to fertility from the initial test data of the test type;

[0100] The target detection data is used as sample detection data of the detection type.

[0101] Optionally, the identification module 220 is specifically configured to:

[0102] Based on the scanned image of the uterine fibroid, a three-dimensional structural model of the patient's uterus is constructed, and based on the three-dimensional structural model of the uterus, three-dimensional structural edge data of the uterine fibroid and position range information of the uterine fibroid in the uterus are identified by an edge recognition algorithm;

[0103] Based on the three-dimensional data of the structural edge of the uterine fibroid, the three-dimensional structural data of the uterine fibroid is identified, and based on the position range information of the uterine fibroid in the uterus, the position distribution information of the uterine fibroid is identified.

[0104] Optionally, the identification module 220 is specifically configured to:

[0105] Based on the sample test data of each test type, identifying the test data range to which the sample test data of each test type belongs through the sample evaluation strategy of each test type;

[0106] Based on the detection data range described by the sample detectable data of each detection type, the evaluation value corresponding to each detection data range is queried in the detection database, and the evaluation value of the detection data range corresponding to each detection type is used as the detection evaluation value of each detection type.

[0107] Optionally, the evaluation module 230 is specifically configured to:

[0108] Based on the position distribution information of the uterine fibroids in the uterus, identifying the uterine region where the uterine fibroids are located, and the position distribution characteristics of the uterine fibroids in the uterine region;

[0109] In the fertility impact database, query each fertility impact type corresponding to each uterine region and the position distribution feature range corresponding to each fertility impact type, and query the position distribution feature range to which the position distribution feature of the uterine fibroid in the uterine region belongs, to determine the fertility impact type of the uterine fibroid on the patient;

[0110] Based on the three-dimensional structural data of the uterine fibroids, the structural shape of the uterine fibroids and the structural range of the uterine fibroids are identified, and based on the structural shape of the uterine fibroids and the structural range of the uterine fibroids, the fertility impact degree of the remaining impact type corresponding to the uterine fibroids is identified through the fertility impact identification network corresponding to the fertility impact type.

[0111] Optionally, the evaluation module 230 is specifically configured to:

[0112] Based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, identifying the patient's fertility impact evaluation value through a fertility impact evaluation model;

[0113] Obtaining a weight value of the fertility impact type corresponding to the patient, and based on the weight value of the fertility impact type, performing weighted calculation on the fertility impact evaluation value of the fertility impact type to obtain a target fertility impact evaluation value of the patient;

[0114] Based on the target fertility impact evaluation value, the fertility impact database is queried to obtain the fertility probability value of the patient.

[0115] Each module in the above-mentioned fertility auxiliary judgment system for patients with uterine fibroids can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0116] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As 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 achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for assisting in determining the fertility of a patient with uterine fibroids is implemented. 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 a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0117] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the methods in the first aspect are implemented.

[0119] 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 any one of the methods in the first aspect are implemented.

[0120] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0121] 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 used 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.

[0122] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0123] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0124] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, 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 attached claims.

Claims

1. A method for assisting in determining the fertility of patients with uterine fibroids, characterized in that: The method comprises: Obtaining a sample test result of a patient's uterine fibroids and a scan of the uterine fibroids, and identifying sample test data of each test type of the uterine fibroids based on the sample test result; Based on the scanned image of the uterine fibroid, the three-dimensional structural data of the uterine fibroid and the position distribution information of the uterine fibroid in the uterus are identified, and based on the sample detection data of each detection type, the detection evaluation value of each detection type is identified through a sample evaluation strategy; Based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type are identified; based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, the fertility probability value of the patient is identified through a fertility impact assessment model; The patient's fertility probability value is used as an auxiliary judgment result of the patient's fertility.

2. The method according to claim 1, characterized in that: The method of identifying the sample detection data of each detection type of the uterine fibroid based on the sample detection result includes: Splitting the sample test results into initial test data of each test type, and for each test type, extracting target test data related to fertility from the initial test data of the test type; The target detection data is used as sample detection data of the detection type.

3. The method according to claim 1, characterized in that: The method of identifying the three-dimensional structure data of the uterine fibroid and the position distribution information of the uterine fibroid in the uterus based on the scanned image of the uterine fibroid includes: Based on the scanned image of the uterine fibroid, a three-dimensional structural model of the patient's uterus is constructed, and based on the three-dimensional structural model of the uterus, three-dimensional structural edge data of the uterine fibroid and position range information of the uterine fibroid in the uterus are identified by an edge recognition algorithm; Based on the three-dimensional data of the structural edge of the uterine fibroid, the three-dimensional structural data of the uterine fibroid is identified, and based on the position range information of the uterine fibroid in the uterus, the position distribution information of the uterine fibroid is identified.

4. The method according to claim 1, characterized in that The method of identifying the detection evaluation value of each detection type based on the sample detection data of each detection type through a sample evaluation strategy includes: Based on the sample test data of each test type, identifying the test data range to which the sample test data of each test type belongs through the sample evaluation strategy of each test type; Based on the detection data range described by the sample detectable data of each detection type, the evaluation value corresponding to each detection data range is queried in the detection database, and the evaluation value of the detection data range corresponding to each detection type is used as the detection evaluation value of each detection type.

5. The method according to claim 4, characterized in that The method of identifying the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus includes: Based on the position distribution information of the uterine fibroids in the uterus, identifying the uterine region where the uterine fibroids are located, and the position distribution characteristics of the uterine fibroids in the uterine region; In the fertility impact database, query each fertility impact type corresponding to each uterine region and the position distribution feature range corresponding to each fertility impact type, and query the position distribution feature range to which the position distribution feature of the uterine fibroid in the uterine region belongs, to determine the fertility impact type of the uterine fibroid on the patient; Based on the three-dimensional structural data of the uterine fibroids, the structural shape of the uterine fibroids and the structural range of the uterine fibroids are identified, and based on the structural shape of the uterine fibroids and the structural range of the uterine fibroids, the fertility impact degree of the remaining impact type corresponding to the uterine fibroids is identified through the fertility impact identification network corresponding to the fertility impact type.

6. The method according to claim 5, characterized in that The method of identifying the patient's fertility probability value based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility type through a fertility impact assessment model includes: Based on the detection evaluation values ​​of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type, identifying the patient's fertility impact evaluation value through a fertility impact evaluation model; Obtaining a weight value of the fertility impact type corresponding to the patient, and based on the weight value of the fertility impact type, performing weighted calculation on the fertility impact evaluation value of the fertility impact type to obtain a target fertility impact evaluation value of the patient; Based on the target fertility impact evaluation value, the fertility impact database is queried to obtain the fertility probability value of the patient.

7. A fertility auxiliary judgment system for patients with uterine fibroids, characterized in that: The system comprises: An acquisition module, used to acquire a sample test result of a patient's uterine fibroids and a scan of the uterine fibroids, and identify sample test data of each test type of the uterine fibroids based on the sample test result; an identification module, for identifying the three-dimensional structural data of the uterine fibroid and the position distribution information of the uterine fibroid in the uterus based on the scanned image of the uterine fibroid, and identifying the detection evaluation value of each detection type through a sample evaluation strategy based on the sample detection data of each detection type; An evaluation module, for identifying the type of fertility impact of the uterine fibroids on the patient and the degree of fertility impact of the fertility impact type based on the three-dimensional structural data of the uterine fibroids and the position distribution information of the uterine fibroids in the uterus, and identifying the fertility probability value of the patient through a fertility impact evaluation model based on the detection evaluation value of each detection type and the degree of fertility impact of the uterine fibroids on the patient's fertility impact type; The determination module is used to use the patient's fertility probability value as the patient's fertility auxiliary judgment result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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