Disease location acquisition method, device, equipment and computer-readable storage medium

Abstract

A method, device, equipment and computer-readable storage medium for obtaining the location of a human disease. The method comprises: obtaining element features of the disease data through disease data (S101); and obtaining the three-dimensional location of the disease using multiple disease location models based on the element features of the disease data, wherein the multiple disease location models correspond to different three-dimensional locations of the body (S102). The above method can realize the display of the disease on a 3D model, so as to more accurately display disease information to users, improve the interactive display effect of diseases and abnormal indicators, and improve efficiency and user experience.

Description

Technical Field

[0001] The present application relates to the technical field of computer human-computer interaction. Specifically, the present application relates to a method, device, equipment, computer-readable storage medium and electronic equipment for obtaining the location of a human disease. Background Art

[0002] Health has always been an important topic of concern to people. With the development of computer technology and communication technology, human diseases and examination abnormalities are mostly displayed in two-dimensional (2D) form in the display interface. However, due to the dimensional limitation of 2D, it is impossible to display the complex interlaced structures and structures of three-dimensional (3D), so it is impossible to quickly locate the lesions and abnormalities, making it impossible to show accurate medical diagrams to users, causing user misunderstanding. Summary of the invention

[0003] In view of the above problems, the present disclosure is proposed. The present disclosure provides a method, device, equipment, computer-readable storage medium and electronic device for obtaining the location of a human disease.

[0004] According to one aspect of the present disclosure, a disease location acquisition method is provided, comprising: acquiring elemental features of the disease data through disease data; and based on the elemental features of the disease data, acquiring the three-dimensional location of the disease using multiple disease location models, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.

[0005] According to an example of the present disclosure, the multiple disease location models correspond to multiple human organs, and each model contains at least one label corresponding thereto, and the method of obtaining the three-dimensional position of the disease using the multiple disease location models based on the elemental features of the disease data includes: corresponding the elemental features of the disease data to at least one label in the multiple disease location models to obtain the three-dimensional position of the disease.

[0006] According to an example of the present disclosure, the method further includes: based on the disease data, obtaining a corrected three-dimensional position of the disease through a first neural network, wherein the first neural network is obtained by training with training data, the training data includes feature data and identification labels, the feature data includes multiple disease information, the identification labels include three-dimensional positions corresponding to the multiple disease information, and the multiple disease information includes disease data of multiple different diseases.

[0007] According to an example of the present disclosure, the method also includes: when an element feature of the disease data cannot correspond to at least one of the multiple disease location models, based on the disease data, obtaining the three-dimensional position of the disease through a second neural network, wherein the second neural network is obtained by training with training data, the training data includes feature data and an identification label, the feature data includes disease classification information different from the disease classification information, and the identification label includes the three-dimensional position corresponding to the disease classification information included in the feature data.

[0008] According to an example of the present disclosure, the multiple disease location models are obtained by dividing the disease classification information based on different three-dimensional locations of the body.

[0009] According to an example of the present disclosure, the disease classification information corresponds to different disease names, and each disease classification information contains at least one name tag corresponding thereto, and the method further includes: matching element features of the disease data to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.

[0010] According to an example of the present disclosure, the disease data is obtained from one or more of health assessments, physical examination reports, health data, and peripheral device inputs.

[0011] According to an example of the present disclosure, after the three-dimensional position of the disease is obtained, the three-dimensional position is highlighted on the three-dimensional human body structure.

[0012] According to one aspect of the present disclosure, a disease location acquisition device is provided, including: an element feature acquisition unit, used to acquire the element features of the disease data through the disease data; and a three-dimensional position acquisition unit, used to acquire the three-dimensional position of the disease based on the element features of the disease data using multiple disease location models, wherein the multiple disease location models correspond to different three-dimensional positions of the body respectively.

[0013] According to an example of the present disclosure, the multiple disease location models correspond to multiple human organs, and each model contains at least one label corresponding thereto, and the three-dimensional position acquisition unit corresponds the element features of the disease data to at least one label in the multiple disease location models to obtain the three-dimensional position of the disease.

[0014] According to an example of the present disclosure, the device also includes: a three-dimensional position acquisition unit obtains a corrected three-dimensional position of the disease through a first neural network based on the disease data, wherein the first neural network is obtained by training with training data, the training data includes feature data and identification labels, the feature data includes multiple disease information, the identification labels include three-dimensional positions corresponding to the multiple disease information, and the multiple disease information includes disease data of multiple different diseases.

[0015] According to an example of the present disclosure, the device also includes: when the element feature of the disease data cannot correspond to at least one of the multiple disease location models, the three-dimensional position acquisition unit obtains the three-dimensional position of the disease through a second neural network based on the disease data, wherein the second neural network is obtained by training with training data, the training data includes feature data and an identification label, the feature data includes disease classification information different from the disease classification information, and the identification label includes a three-dimensional position corresponding to the disease classification information included in the feature data.

[0016] According to an example of the present disclosure, the multiple disease location models are obtained by dividing the disease classification information based on different three-dimensional locations of the body.

[0017] According to an example of the present disclosure, the disease classification information corresponds to different disease names, and each disease classification information contains at least one name tag corresponding thereto, and the three-dimensional position acquisition unit matches the element features of the disease data to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.

[0018] According to an example of the present disclosure, after obtaining the three-dimensional position of the disease, the three-dimensional position acquisition unit highlights the three-dimensional position on the three-dimensional human body structure.

[0019] According to one aspect of the present disclosure, a disease location acquisition device is provided, comprising: a processor; and a memory storing computer-readable instructions, wherein a disease location acquisition method is executed when the computer-readable instructions are executed by the processor, the method comprising: acquiring elemental features of the disease data through the disease data; and based on the elemental features of the disease data, acquiring the three-dimensional location of the disease using multiple disease location models, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.

[0020] According to one aspect of the present disclosure, there is provided a computer-readable storage medium for storing a computer-readable program, wherein the program causes a computer to execute the disease location acquisition method as described above.

[0021] According to one aspect of the present disclosure, there is provided an electronic device comprising the above-mentioned disease location acquisition device and a display interface; wherein the display interface is configured to display a three-dimensional human body model, the three-dimensional human body model displays disease classification information at the three-dimensional position obtained by the above-mentioned disease location acquisition method, and highlights the organ corresponding to the obtained three-dimensional position.

[0022] In the above aspects of the present disclosure, a disease location acquisition method is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional positions, and then obtains the three-dimensional position of the disease by mapping the obtained disease data into the disease location model, thereby realizing a 3D display of the disease on the 3D model, so as to more accurately display disease information to users, improve the interactive display effect of diseases and abnormal indicators, and improve efficiency and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other purposes, features and advantages of the present disclosure will become more apparent by describing the embodiments of the present disclosure in more detail in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0024] Figure 1 A flow chart of a disease location acquisition method according to an embodiment of the present disclosure is shown;

[0025] Figure 2 is a schematic diagram summarizing an application scenario of a training method for a neural network model according to an embodiment of the present disclosure;

[0026] Figure 3 A flow chart showing a method for acquiring a disease location according to another embodiment of the present disclosure is shown;

[0027] Figure 4 A schematic diagram showing an example of disease location acquisition according to an embodiment of the present disclosure;

[0028] Figure 5 A schematic diagram of a display interface for displaying human body information according to an embodiment of the present disclosure is shown;

[0029] Figure 6 A schematic diagram of a display interface for disease presentation according to an embodiment of the present disclosure is shown;

[0030] Figure 7 A block diagram of a disease location acquisition device according to an embodiment of the present disclosure is shown;

[0031] Figure 8 A block diagram of a disease location acquisition device according to an embodiment of the present disclosure is shown;

[0032] Fig. 9 A schematic diagram showing the structure of an electronic device according to an embodiment of the present disclosure; and

[0033] Fig.10 A schematic diagram of a storage medium according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0035] The words "first", "second" and similar terms used in the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "include" or "comprises" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0036] Flowcharts are used in the present application to illustrate the steps of the method according to the embodiments of the present application. It should be understood that the preceding or following steps are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or a certain step or several steps may be removed from these processes.

[0037] The International Classification of Diseases (ICD10) is an internationally accepted unified classification of various diseases for the purpose of analyzing the differences in the health status of the population of countries around the world and the causes of death. Assigning the correct ICD10 code to each patient according to the diagnosis (i.e., adding the ICD10 code to the patient's medical record) is very important for clinical application and management. However, assigning the correct ICD10 code to the patient when he / she is seeing a doctor only helps the patient find the corresponding disease or the 2D location of the disease, but cannot accurately locate the 3D location of the disease, and cannot show the user an accurate medical diagram, which can easily cause user misunderstanding. In addition, when assigning codes, medical coders need to refer to the doctor's diagnosis described in text phrases and sentences and other information in the electronic medical record, and then manually assign the appropriate ICD10 code according to the coding guidance. In this process, many errors are prone to occur, which leads to errors in locating the disease location. For example, doctors often use abbreviations and synonyms when writing diagnosis descriptions, which can cause confusion and inaccuracy when coders match ICD10 codes with these abbreviations and synonyms.

[0038] In addition, ICD10 codes are organized in a hierarchical structure, where upper codes represent broad disease categories and lower codes represent more specific diseases. Therefore, miscoding can occur when coders match a diagnosis description to an overly broad code instead of a more specific code, making it more difficult to locate the disease.

[0039] The present disclosure provides a disease location acquisition method, which creates a disease location model based on different three-dimensional locations, and then obtains the three-dimensional location of the disease by mapping the obtained disease data into the disease location model, thereby realizing 3D display of the disease on the 3D model, so as to more accurately display disease information to users and improve user experience.

[0040] The embodiments and examples of the present disclosure are described in detail below with reference to the accompanying drawings.

[0041] At least one embodiment of the present disclosure provides a disease location acquisition method, a disease location acquisition device, a disease location acquisition equipment, and a computer-readable storage medium. The disease location acquisition provided according to at least one embodiment of the present disclosure is described in a non-limiting manner through several examples and embodiments. As described below, in the absence of mutual conflict, different features in these specific examples and embodiments can be combined with each other to obtain new examples and embodiments, and these new examples and embodiments also belong to the scope of protection of the present disclosure.

[0042] Refer to the following Figure 1-4 The disease location acquisition method according to the embodiment of the present disclosure is described. First, referring to Figure 1The disease location acquisition method according to the embodiment of the present disclosure is described. The method can be automatically completed by a computer or the like. For example, the method can be applied to display the three-dimensional location of a human disease or the like. For example, the disease location acquisition method can be implemented in the form of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a network server, or the like.

[0043] For example, the disease location acquisition method is applicable to a computing device, which includes any electronic device with computing functions, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, a network server, etc., which can load and execute the disease location acquisition method, and the embodiments of the present disclosure are not limited to this. For example, the computing device may include a central processing unit (CPU) or a graphics processing unit (GPU) and other processing units with data processing capabilities and / or instruction execution capabilities, storage units, etc., and the computing device is also installed with an operating system, an application programming interface (for example, OpenGL (Open Graphics Library), Metal, etc.), etc., and the disease location acquisition method provided by the embodiments of the present disclosure is implemented by running code or instructions. For example, the computing device may further include a display component, such as a liquid crystal display (LCD), an organic light emitting diode (OLED) display, a quantum dot light emitting diode (QLED) display, a projection component, a VR head mounted display device (such as a VR helmet, VR glasses), etc., and the embodiments of the present disclosure are not limited thereto. For example, the display component may display an object to be displayed.

[0044] like Figure 1 As shown, the disease location acquisition method includes the following steps S101-S102.

[0045] In step S101, element features of disease data are acquired through disease data.

[0046] In step S102, based on the element features of the disease data, a plurality of disease location models are used to obtain the three-dimensional location of the disease, wherein the plurality of disease location models correspond to different three-dimensional locations of the body, respectively.

[0047] For step S101, for example, the disease data may be data related to the user's current disease, which includes a language description of the disease, which may be obtained from the user's personal health information. For example, the user's disease data is obtained from parsing one or more of a health assessment, a physical examination report, health data, and an external device input. For example, the disease data may be data related to the current disease in a given medical record (or health data), such as the chief complaint, current medical history, past medical history, and the diagnosis result given by the doctor. In an embodiment of the present disclosure, the disease data may be handwritten data, and the required data in the disease data may be obtained by OCR or manual reading; it may also be electronic disease data, and the required data may be exported through an electronic disease data management platform.

[0048] For example, multiple disease location models are obtained by dividing disease classification information based on different three-dimensional locations of the body. For example, the disease classification information may be ICD10 or other suitable disease classification libraries, which are not limited here.

[0049] For example, the disease classification information may correspond to different disease names, and each disease classification information contains at least one name tag corresponding thereto. In this case, the element feature of the disease data may be matched to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data. For example, diabetes in the disease classification information corresponds to tag 1, and then when the element feature of the disease data corresponds to diabetes, the disease data is matched to tag 1 in the disease classification information, thereby obtaining the disease name corresponding to the disease data as diabetes.

[0050] For step S101, for example, the element features of the disease data can be represented by vectors or word vectors of each element of the disease data. For example, a neural network can be used to obtain the probability of matching each disease in the disease classification information to the disease data. If the probability is greater than a predetermined threshold, the disease in the disease classification information corresponding to the probability is matched to the disease data, thereby obtaining the name of the disease.

[0051] It should be recognized that the above method of obtaining the disease name based on disease data is not limited to this, and other effective methods can also be used to obtain the disease name, which will not be repeated here.

[0052] In addition, since the correct disease classification information code or label is assigned to the patient when he / she visits the doctor, it only finds the corresponding disease or 2D position for the patient, but cannot accurately locate the 3D position of the disease. To this end, multiple disease location models can be obtained based on the disease classification information, and the multiple disease location models correspond to different three-dimensional positions of the body. For example, a disease location model can be obtained based on ICD10. For example, the 13,904 diseases in ICD10 can be integrated and analyzed, and ICD10 can be divided into multiple disease location models based on different human organs or tissues, so as to correspond the disease to the 3D model. For example, ICD10 can be divided into multiple disease location models by a classifier (such as clustering, support vector machine (SVM), K nearest neighbor algorithm) or a neural network, where different disease location models correspond to different human organs or tissues.

[0053] For example, multiple disease location models correspond to multiple human organs, and each model contains at least one label corresponding to it. For example, the disease classification information can be divided into multiple disease location models according to organs such as heart, liver, spleen, lung, and kidney, and each disease location model corresponds to labels such as heart, liver, spleen, lung, and kidney. The label of each disease location model can also contain sub-labels. For example, the "heart" label can contain sub-labels such as "left atrium", "left ventricle", "right atrium", and "right ventricle".

[0054] For example, in step S102, obtaining the three-dimensional position of the disease based on the elemental features of the disease data using multiple disease location models may include: corresponding the elemental features of the disease data to at least one label in the multiple disease location models to obtain the three-dimensional position of the disease.

[0055] For example, each label can correspond to a code, and each sub-label can also correspond to a code. For example, in the disease location model corresponding to ICD10, the label myasthenia gravis is coded as G80.001, the label esophagus is coded as I95, and the label esophageal varices (sub-label of esophagus) is coded as I95.5. For example, when the disease data is "there is no obvious stenosis in the test tube cavity, the gastroscope can pass, and esophageal varices can be seen at the lower end of the esophagus", according to the element feature "lower end esophageal vein" in the disease data, the disease data is matched to the code I95.5 of the disease location model, so that the three-dimensional location of the disease can be obtained as "esophagus, esophageal vein".

[0056] It should be appreciated that the above process of matching the disease data to the coding of the disease location model can be automatically completed by a computer. For example, the technical effect of matching the disease data to the coding of the disease location model can also be achieved by a neural network or other methods, which will not be repeated here.

[0057] After obtaining the three-dimensional position of the disease, the disease can be displayed at the organ position corresponding to the three-dimensional human body model in the display interface of the electronic device to achieve human-computer interaction. For example, the three-dimensional position of the disease can be highlighted on the three-dimensional human body structure, or the three-dimensional position of the disease can be displayed on the display interface of the mobile device through the engine.

[0058] In the above aspects of the present disclosure, a disease location acquisition method is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional positions, and then obtains the three-dimensional position of the disease by mapping the obtained disease data into the disease location model, thereby realizing a 3D display of the disease on the 3D model, so as to more accurately display disease information to users, improve the interactive display effect of diseases and abnormal indicators, and improve efficiency and user experience.

[0059] Furthermore, for the above-mentioned disease data corresponding to the disease model, the neural network model can be used to analyze the disease data to obtain the corrected three-dimensional position of the disease. In addition, when the element features of the disease data cannot be corresponded to at least one of the multiple disease position models, the three-dimensional position of the disease can be directly obtained based on the disease data using the neural network.

[0060] Neural network is a large-scale, multi-parameter optimization tool. Relying on a large amount of training data, deep neural network can learn hidden features that are difficult to summarize in the data, so as to complete many complex tasks, such as face detection, image semantic segmentation, text summary extraction, object detection, motion tracking, natural language translation, etc. Obtaining the vector representation of text through word vector means representing each word in the text as a single vector, and generating the vector representation of the text through high-level summarization and abstraction. Technology based on neural network models can be used in medical disease location processing, so as to automatically process disease data.

[0061] Reference Figure 2 The application scenario of the training method of the neural network model according to the embodiment of the present disclosure is schematically described.

[0062] like Figure 2 As shown, the neural network model 20 according to the embodiment of the present disclosure is used to receive the input 10, and the input 10 performs feature extraction processing, and generates an output 30 based on the extracted features. In the embodiment of the present disclosure, the input 10 can be, for example, an object to be processed such as an image, a video, or a natural language text. The neural network model 20 performs image semantic segmentation, object detection, motion tracking, natural language translation, and other processing on the input 10 to generate the output 30. The neural network model 20 can be embedded in a terminal device or a server to process the input.

[0063] In one embodiment of the present disclosure, the neural network model 20 can be based on Figure 3 The training method shown is used for training.

[0064] like Figure 3 FIG. 2 is a flow chart of a method for acquiring a disease location according to another embodiment of the present disclosure. Figure 3 As shown, the disease location acquisition method includes the following steps S201 to S203.

[0065] In step S201, element features of the disease data are acquired through the disease data.

[0066] In step S202, based on the element features of the disease data, a plurality of disease location models are used to obtain the three-dimensional location of the disease, wherein the plurality of disease location models correspond to different three-dimensional locations of the body, respectively.

[0067] In step S203, based on the disease data, a corrected three-dimensional position of the disease is obtained through a first neural network.

[0068] Since steps S201-S202 are similar to the above reference Figure 1 The steps S101-S102 are the same as those described above and will not be repeated here.

[0069] Next, in step S203, the corrected three-dimensional position of the disease can be obtained through a first neural network based on the disease data.

[0070] For example, the first neural network is obtained by training with training data, the training data includes feature data and identification labels, the feature data includes multiple disease information, the identification labels include three-dimensional positions corresponding to the multiple disease information, and the multiple disease information includes disease data of multiple different diseases.

[0071] For example, the first neural network can be a convolutional neural network (CNN), a recurrent neural network (RNN), a back propagation neural network (BP), a linear neural network, etc., without limitation here.

[0072] For example, multiple disease information may be obtained from analyzing one or more of health assessments, physical examination reports, health data, and peripheral inputs, and the three-dimensional positions corresponding to the multiple disease information may be more detailed position information that is manually annotated. By training the first neural network using multiple disease information annotated with more detailed position information, the three-dimensional position corresponding to the disease data may be corrected, so that the obtained three-dimensional position is more accurate.

[0073] Alternatively, the training data may include feature data and identification tags, the feature data including multiple disease information and disease classification information, and the identification tags including three-dimensional positions corresponding to the multiple disease information and three-dimensional position information corresponding to the disease classification information. It should be recognized that in addition to the disease classification information and its corresponding three-dimensional positions, multiple disease information and its corresponding three-dimensional positions are newly added to the training data, thereby increasing the types and ranges of disease positions. Therefore, in the case where the disease position model corresponding to the disease data is inaccurate, the three-dimensional position of the disease data can be corrected using the first neural network trained with more training data.

[0074] In addition, when the element features of the disease data cannot be matched to at least one of the disease location models, the three-dimensional position of the disease can be directly obtained by using the first neural network based on the disease data. Since the trained neural network has a prediction function, when the disease data cannot be matched to at least one of the disease location models, the three-dimensional position of the disease can be obtained by using a neural network with a prediction function.

[0075] Similarly, after obtaining the three-dimensional position of the disease, it can be displayed on the corresponding organ model to achieve human-computer interaction. For example, the three-dimensional position of the disease can be highlighted on the three-dimensional human body structure, or the three-dimensional position of the disease can be displayed on the display interface of the mobile device through the engine.

[0076] In the above aspects of the present disclosure, a disease location acquisition method is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional positions, and then obtains the three-dimensional position of the disease by mapping the obtained disease data into the disease location model. The disease data is then analyzed using a neural network to obtain a corrected three-dimensional position of the disease, thereby achieving a 3D display of the disease on the 3D model, so as to more accurately display disease information to users, improve the interactive display effect of diseases and abnormal indicators, and improve efficiency and user experience.

[0077] Alternatively, when the element features of the disease data cannot be mapped to at least one of the multiple disease location models, the three-dimensional location of the disease can be directly obtained by using a second neural network trained with training data different from the disease classification information.

[0078] For example, the element features of some disease data may not correspond to the existing disease location model. In this case, the second neural network can be trained by training data composed of other disease classification information or disease location models, and the three-dimensional location of the disease can be obtained using the second neural network.

[0079] For example, the second neural network is obtained by training with training data, the training data including feature data and identification labels, the feature data including disease classification information different from the disease classification information, the identification labels including a three-dimensional position corresponding to the disease classification information included in the feature data.

[0080] For example, the second neural network can also be a convolutional neural network (CNN), a recurrent neural network (RNN), a back propagation neural network (BP), a linear neural network, etc., without limitation here.

[0081] For example, disease classification information different from disease classification information is used in the training data, thereby increasing the types and ranges of disease locations. Therefore, when the disease data cannot be matched to at least one of the multiple disease location models, the three-dimensional location of the disease can be directly obtained through the second neural network.

[0082] Similarly, after obtaining the three-dimensional position of the disease, it can be displayed on the corresponding organ model to achieve human-computer interaction. For example, the three-dimensional position of the disease can be highlighted on the three-dimensional human body structure, or the three-dimensional position of the disease can be displayed on the display interface of the mobile device through the engine.

[0083] In the above aspects of the present disclosure, a disease location acquisition method is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional positions, and then obtains the three-dimensional position of the disease by mapping the obtained disease data into the disease location model. When the disease data cannot correspond to at least one of the multiple disease location models, the three-dimensional position of the disease is directly obtained through a second neural network based on the disease data to obtain the three-dimensional position of the disease, thereby realizing a 3D display of the disease on the 3D model, so as to more accurately display the disease information to the user, improve the interactive display effect of the disease and abnormal indicators, and improve efficiency and user experience.

[0084] In the above Figure 1-3 After describing the disease location acquisition method according to the embodiment of the present disclosure, the following is based on Figure 4 An example of disease location acquisition according to an embodiment of the present disclosure is described.

[0085] Figure 4 FIG. 2 is a schematic diagram showing an example of obtaining a disease location according to an embodiment of the present disclosure. Figure 4As shown, first, disease data is obtained, and the disease data contains a language description of the disease. The disease data can be obtained from one or more of a health assessment 51, a physical examination report 52, health data 53, and a peripheral input 54. Next, the elemental features of the disease data are obtained through the disease data, and then the elemental features of the disease data are mapped to the disease classification information 55 to obtain the disease name corresponding to the disease data. Since multiple disease location models 56 are obtained through the disease classification information 55, the elemental features of the disease data can be mapped to multiple disease location models 56 to obtain the three-dimensional position of the disease, thereby realizing a 3D display. In addition, since the disease classification information contains various descriptions of the disease, based on the elemental features of the disease data, the disease location acquisition method can also be used to obtain the disease or problem 57 corresponding to the disease data, the system / part 58 described in the corresponding abnormal information, the health index 59 and other information.

[0086] The disease information display method provided in this application is generally operated through an electronic device to achieve information interaction with the user. After the electronic device is turned on and the display interface for disease information display (e.g., a digital human body APP) is entered, the initial display on the display interface is usually a complete three-dimensional human body model, generally presented as a three-dimensional human body image standing upright and in a naturally stretched state. In addition, the diseased organ is highlighted where the disease is detected to facilitate the patient's understanding.

[0087] Refer to the following Figure 5-6 A display interface for displaying human body information according to an embodiment of the present disclosure is described. Figure 5 A schematic diagram of a display interface for displaying human body information according to an embodiment of the present disclosure. Figure 6 A schematic diagram of a display interface for disease presentation according to an embodiment of the present disclosure.

[0088] like Figure 5 As shown, on the display interface showing the three-dimensional human body model, at least one curve surrounding the three-dimensional human body model is displayed, and a plurality of human body information labels are arranged on the curve. In some feasible implementation manners, such as Figure 5 As shown, the curve includes at least one of the following: an elliptical arc and a spiral line; the elliptical arc surrounds the chest position of the three-dimensional human body model; the spiral line spirally extends from the foot of the three-dimensional human body model to the head of the three-dimensional human body model; the curve is relatively static with respect to the display interface. The curve can also be other forms of geometric curves that can surround the three-dimensional human body model, specifically one or two curves displayed in parallel at a certain interval, or when the curve is a spiral line, it can be presented as a double spiral structure. Of course, the number can even be 3, 4, etc., which can be set according to actual needs.

[0089] Furthermore, the human body information labels correspond to different parts of the three-dimensional human body model. The different parts of the three-dimensional human body model can be determined according to multiple classifications. In some feasible implementations, the human body information labels include at least one of the following: human organ category information, human system category information or human parameter information; the spacing between adjacent human body information labels on the curve is equal. For example, human organ category information is classified according to organs, including heart, liver, spleen, lung, kidney, etc., and classified according to human system categories, including circulatory system, digestive system, respiratory system, reproductive system, immune system, etc. It can also be classified according to local parts, including head, chest, upper and lower limbs, etc. It even includes other human body parameter information related to human health, such as health history data, trauma data, etc. These human body information labels are arranged along the curve. In order to form a three-dimensional effect, the human body information labels on the curve are of different sizes, but the spacing between adjacent human body information labels on the curve is equal. For example, the human body information label in the middle position is larger, and as it moves farther away from the middle position, the human body information label gradually becomes smaller.

[0090] Of course, the human body information tag can also be set to different states. In some feasible implementations, the curve includes a preset highlight area and a preset non-highlight area. On the display interface displaying the three-dimensional human body model, at least one curve surrounding the human body model is displayed, and a number of human body information tags are set on the curve, specifically including: if the human body information tag moves to the preset highlight area, the human body information tag is enlarged and displayed, and the human body information tag is set to an activated state; if the human body information tag moves to the preset non-highlight area, the human body information tag is reduced and displayed, and the human body information tag is set to an inactive state.

[0091] The human body information tag can be moved on the curve, and at a specific position of the curve, such as on a curve segment close to the central axis of the three-dimensional human body model, the human body information tag moves to this preset prominent area, then the human body information tag is activated, and the appearance is enlarged, and can be selected, and the next operation is performed accordingly. Other areas are preset non-prominent areas, and the human body information tags in the preset non-prominent areas are inactive, and are displayed in a smaller size accordingly. During the movement of the human body information tag, the process of enlarging and reducing is also displayed on the display interface, which can form a vivid three-dimensional dynamic process and enhance the user's immersion in human-computer interaction. Of course, in addition to the changes in appearance through enlargement or reduction, the characteristics of the brightness and color of the human body information tag in a changing state can also be added.

[0092] After the location of the disease is detected by the methods described in the present disclosure, Figure 6 As shown, the corresponding position of the three-dimensional human body model is highlighted, thereby realizing a 3D display of the disease on the 3D model, so as to more accurately display the disease information to the user and improve the user experience.

[0093] The disease location acquisition method according to the embodiment of the present invention is described above with reference to the accompanying drawings. Hereinafter, the disease location acquisition device according to the embodiment of the present disclosure will be described.

[0094] Figure 7 FIG. 2 is a functional block diagram of a disease location acquisition device according to an embodiment of the present disclosure. Figure 7 As shown, the disease location acquisition device 1000 according to the embodiment of the present disclosure includes an element feature acquisition unit 1001 and a three-dimensional location acquisition unit 1002. The above modules can respectively execute the above reference Figures 1 to 3 The various steps of the disease location acquisition method according to the embodiment of the present disclosure are described. Those skilled in the art understand that these unit modules can be implemented in various ways by hardware alone, software alone, or by a combination thereof, and the present disclosure is not limited to any of them. For example, these units can be implemented by a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), a field programmable gate array (FPGA), or other forms of processing units with data processing capabilities and / or instruction execution capabilities and corresponding computer instructions.

[0095] The element feature acquisition unit 1001 is used to acquire the element features of the disease data through the disease data.

[0096] For example, disease data may be data related to the user's current disease, which may be obtained from the user's personal health information, such as by parsing one or more of health assessments, physical examination reports, health data, and peripheral inputs to obtain the user's disease data.

[0097] The three-dimensional position acquisition unit 1002 is used to acquire the three-dimensional position of the disease based on the element features of the disease data using multiple disease position models, wherein the multiple disease position models correspond to different three-dimensional positions of the body respectively.

[0098] For example, multiple disease location models are obtained by dividing disease classification information based on different three-dimensional positions of the body. For example, the disease classification information can be ICD10, or other suitable disease classification libraries, which are not limited here. For example, the disease classification information can correspond to different disease names, and each disease classification information contains at least one name tag corresponding to it. At this time, the element feature of the disease data can be matched to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.

[0099] For example, a disease location model may be obtained based on ICD 10. For example, the diseases in ICD 10 may be integrated and analyzed, and ICD 10 may be divided into a plurality of disease location models based on different human organs or tissues, so that the diseases may be mapped to the 3D model.

[0100] For example, the three-dimensional position acquisition unit 1002 may correspond the element feature of the disease data to at least one label in the multiple disease position models to obtain the three-dimensional position of the disease.

[0101] It should be appreciated that the above process of matching the disease data to the coding of the disease location model can be automatically completed by a computer. For example, the technical effect of matching the disease data to the coding of the disease location model can also be achieved by a neural network or other methods, which will not be repeated here.

[0102] After obtaining the three-dimensional position of the disease, it can be displayed on the corresponding organ model to achieve human-computer interaction. For example, the three-dimensional position of the disease can be highlighted on the three-dimensional human body structure, or the three-dimensional position of the disease can be displayed on the display interface of the mobile device through the engine.

[0103] Furthermore, the three-dimensional position acquisition unit 1002 may further utilize a neural network to analyze the disease data to obtain a corrected three-dimensional position of the disease.

[0104] For example, the three-dimensional position acquisition unit 1002 can obtain the corrected three-dimensional position of the disease through a first neural network based on the disease data. For example, the first neural network is obtained by training with training data, the training data includes feature data and identification labels, the feature data includes multiple disease information, the identification labels include three-dimensional positions corresponding to the multiple disease information, and the multiple disease information includes disease data of multiple different diseases.

[0105] Alternatively, when the disease data cannot correspond to at least one of the multiple disease location models, the three-dimensional location acquisition unit 1002 can directly obtain the three-dimensional location of the disease through the first neural network.

[0106] Alternatively, the three-dimensional position acquisition unit 1002 may obtain the three-dimensional position of the disease through a second neural network based on the disease data.

[0107] For example, the second neural network is obtained by training with training data, the training data includes feature data and identification labels, the feature data includes disease classification information different from the disease classification information, the identification label includes a three-dimensional position corresponding to the disease classification information included in the feature data, and the multiple disease information includes disease data of multiple different diseases.

[0108] In the above aspects of the present disclosure, a method for acquiring a disease position is proposed. Specifically, the present disclosure creates a disease position model based on different three-dimensional positions, and then obtains the three-dimensional position of the disease by mapping the obtained disease data into the disease position model. The three-dimensional position acquisition unit 1002 can then use a neural network to analyze the disease data to obtain a corrected three-dimensional position of the disease. When the disease data cannot correspond to at least one of the multiple disease position models, the three-dimensional position acquisition unit 1002 can also directly obtain the three-dimensional position of the disease based on the disease data through a first neural network or a second neural network, thereby realizing a 3D display of the disease on a 3D model, so as to more accurately display disease information to users, improve the interactive display effect of diseases and abnormal indicators, and improve efficiency and user experience.

[0109] The following will refer to Figure 8 A disease location acquisition device according to an embodiment of the present disclosure is described. Figure 8 2000 is a schematic diagram of a disease location acquisition device according to an embodiment of the present disclosure. Figure 1 The details of the described methods are the same, so for the sake of simplicity, a detailed description of the same contents is omitted here.

[0110] like Figure 8 As shown, the disease location acquisition device 2000 includes a processor 210 , a memory 220 , and one or more computer program modules 221 .

[0111] For example, the processor 210 is connected to the memory 220 via a bus system 230. For example, one or more computer program modules 221 are stored in the memory 220. For example, one or more computer program modules 221 include instructions for executing the disease location acquisition method provided by any embodiment of the present disclosure. For example, the instructions in one or more computer program modules 221 can be executed by the processor 210. For example, the bus system 230 can be a commonly used serial or parallel communication bus, etc., and the embodiments of the present disclosure are not limited to this.

[0112] For example, the processor 210 can be a central processing unit (CPU), a digital signal processor (DSP), a graphics processing unit (GPU), or other forms of processing units with data processing capabilities and / or instruction execution capabilities. It can be a general-purpose processor or a special-purpose processor, and can control other components in the disease location acquisition device 2000 to perform desired functions.

[0113] The memory 220 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on a computer-readable storage medium, and the processor 210 may run the program instructions to implement the functions (implemented by the processor 210) in the embodiment of the present disclosure and / or other desired functions, such as a disease location acquisition method, etc. Various applications and various data may also be stored in the computer-readable storage medium, such as element features of disease data, disease location models, and various data used and / or generated by applications, etc.

[0114] It should be noted that, for the sake of clarity and brevity, the embodiment of the present disclosure does not provide all the components of the disease location acquisition device 2000. To achieve the necessary functions of the disease location acquisition device 2000, those skilled in the art may provide and set other components not shown according to specific needs, and the embodiment of the present disclosure does not limit this.

[0115] The technical effects of the disease location acquisition apparatus 1000 and the disease location acquisition device 2000 in different embodiments may refer to the technical effects of the disease location acquisition method provided in the embodiments of the present disclosure, which will not be described in detail here.

[0116] The disease location acquisition apparatus 1000 and the disease location acquisition device 2000 can be used in various appropriate electronic devices.

[0117] Fig. 9 A schematic diagram of the structure of an electronic device provided for at least one embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0118] For example, the electronic device includes the disease location acquisition device 1000 provided in any embodiment of the present disclosure and a display interface (for example, Fig. 9The output device 307 shown); wherein the display interface is configured to display a three-dimensional human body model, which displays disease classification information at the three-dimensional position obtained by the above-mentioned disease position acquisition method, and highlights the organ corresponding to the obtained three-dimensional position.

[0119] For example, Fig. 9 As shown, in some examples, the electronic device 300 includes a processing device (e.g., a central processing unit, a graphics processor, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In RAM 303, various programs and data required for the operation of the computer system are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0120] For example, the following components may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309 including a network interface card such as a LAN card, a modem, etc. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data, performing communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive 310 as needed, so that the computer programs read therefrom are installed into the storage device 309 as needed. Although Fig. 9 The electronic device 300 including various devices is shown, but it should be understood that it is not required to implement or include all the devices shown. More or fewer devices may be implemented or included instead.

[0121] For example, the electronic device 300 may further include a peripheral interface (not shown in the figure), etc. The peripheral interface may be various types of interfaces, such as a USB interface, a lightning interface, etc. The communication device 309 may communicate with a network and other devices through wireless communication, such as the Internet, an intranet and / or a wireless network such as a cellular phone network, a wireless local area network (LAN), and / or a metropolitan area network (MAN). Wireless communication may use any of a variety of communication standards, protocols, and technologies, including but not limited to a global system for mobile communications (GSM), an enhanced data GSM environment (EDGE), a wideband code division multiple access (W-CDMA), a code division multiple access (CDMA), a time division multiple access (TDMA), Bluetooth, Wi-Fi (e.g., based on IEEE 802.11a, IEEE 802.11b, IEEE802.11g, and / or IEEE 802.11n standards), a voice transmission over Internet protocol (VoIP), Wi-MAX, protocols for email, instant messaging, and / or short message service (SMS), or any other suitable communication protocol.

[0122] For example, the electronic device may be any device such as a mobile phone, a tablet computer, a laptop computer, an e-book, a game console, a television, a digital photo frame, a navigator, or any combination of electronic devices and hardware, which is not limited in the embodiments of the present disclosure.

[0123] For example, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned disease location acquisition function defined in the method of the embodiment of the present disclosure is executed.

[0124] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0125] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0126] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0127] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, and the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0128] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; returns the selected Internet Protocol address; and the received Internet Protocol address indicates an edge node in a content distribution network.

[0129] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0130] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0131] In various embodiments of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] At least one embodiment of the present disclosure further provides a storage medium. Fig.10 A schematic diagram of a storage medium provided by at least one embodiment of the present disclosure. Fig.10 As shown, the storage medium 400 non-temporarily stores computer-readable instructions 401. When the non-temporary computer-readable instructions are executed by a computer (including a processor), the disease location acquisition method provided by any embodiment of the present disclosure can be executed.

[0133] For example, the storage medium may be any combination of one or more computer-readable storage media, such as one computer-readable storage medium containing a computer-readable program code for determining the number of display surfaces of the submodel, and another computer-readable storage medium containing a computer-readable program code for determining the disease location. For example, when the program code is read by a computer, the computer may execute the program code stored in the computer storage medium, and execute, for example, the disease location acquisition method provided in any embodiment of the present disclosure.

[0134] For example, the storage medium may include a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), flash memory, or any combination of the above storage media, or other applicable storage media.

[0135] There are a few points to note:

[0136] (1) The drawings of the embodiments of the present disclosure only relate to the structures related to the embodiments of the present disclosure, and other structures may refer to the general design.

[0137] (2) In the absence of conflict, the embodiments of the present disclosure and the features therein may be combined with each other to obtain new embodiments.

[0138] The above description is merely an exemplary embodiment of the present disclosure and is not intended to limit the protection scope of the present disclosure. The protection scope of the present disclosure is determined by the appended claims.

Claims

1. A method for obtaining a disease location, comprising: Obtaining element features of the disease data through the disease data; In a case where the element feature of the disease data can correspond to at least one of a plurality of disease location models, the three-dimensional location of the disease is obtained using at least one disease location model, and the obtained three-dimensional location is corrected using a neural network to obtain a corrected three-dimensional location of the disease, wherein the plurality of disease location models are obtained based on different three-dimensional locations of the body according to disease classification information and correspond to a plurality of human organs, respectively, and each disease location model includes at least one label corresponding thereto; as well as In the case where the element features of the disease data cannot be mapped to at least one of the multiple disease location models, a three-dimensional location of the disease is obtained based on the disease data using a neural network.

2. The disease location acquisition method according to claim 1, wherein: The neural network for correcting the three-dimensional position of the disease is a first neural network, Wherein, the first neural network is obtained by training with first training data, the first training data includes first feature data and a first identification label, the first feature data includes the disease classification information and a plurality of disease information other than the disease classification information, the first identification label includes a three-dimensional position corresponding to the disease classification information and the plurality of disease information, and the plurality of disease information includes disease data of a plurality of different diseases.

3. The disease location acquisition method according to claim 1, wherein: The disease classification information belongs to a disease classification library, and the neural network used to obtain the three-dimensional position of the disease is a second neural network. Wherein, the second neural network is obtained by training with second training data, the second training data includes second feature data and second identification labels, the second feature data includes other disease classification information that does not belong to the disease classification library, and the second identification label includes a three-dimensional position corresponding to the disease classification information included in the second feature data.

4. The disease location acquisition method according to claim 1, wherein: The disease classification information corresponds to different disease names, and each disease classification information includes at least one name tag corresponding thereto, and the method further includes: The element features of the disease data are matched to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.

5. The disease location acquisition method according to claim 1, wherein: The disease data is obtained from one or more of health assessments, physical examination reports, health data and peripheral inputs.

6. The disease location acquisition method according to claim 1, wherein: After the three-dimensional position of the disease is obtained, the three-dimensional position is highlighted on the three-dimensional human body structure.

7. A disease location acquisition device, comprising: An element feature acquisition unit, used to acquire element features of the disease data through the disease data; as well as A three-dimensional position acquisition unit is used to obtain the three-dimensional position of the disease using at least one disease location model when the element characteristics of the disease data can correspond to at least one of a plurality of disease location models, and to correct the obtained three-dimensional position using a neural network to obtain a corrected three-dimensional position of the disease, wherein the plurality of disease location models are obtained based on different three-dimensional positions of the body according to disease classification information, and correspond to a plurality of human organs, respectively, and each disease location model includes at least one label corresponding thereto; and when the element characteristics of the disease data cannot correspond to at least one of the plurality of disease location models, the three-dimensional position of the disease is obtained based on the disease data using a neural network.

8. The disease location acquisition device according to claim 7, wherein: The neural network for correcting the three-dimensional position of the disease is a first neural network, Wherein, the first neural network is obtained by training with first training data, the first training data includes first feature data and a first identification label, the first feature data includes the disease classification information and multiple disease information other than the disease classification information, the first identification label includes a three-dimensional position corresponding to the disease classification information and the multiple disease information, and the multiple disease classification information includes disease data of multiple different diseases.

9. The disease location acquisition device according to claim 7, wherein: The disease classification information belongs to a disease classification library, and the neural network used to obtain the three-dimensional position of the disease is a second neural network. Wherein, the second neural network is obtained by training with second training data, the second training data includes second feature data and second identification labels, the second feature data includes other disease classification information that does not belong to the disease classification library, and the second identification label includes a three-dimensional position corresponding to the disease classification information included in the second feature data.

10. The disease location acquisition device according to claim 7, wherein: The disease classification information corresponds to different disease names, and each disease classification information contains at least one name tag corresponding thereto. The three-dimensional position acquisition unit matches the element features of the disease data to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.

11. The disease location acquisition device according to claim 7, wherein: After obtaining the three-dimensional position of the disease, the three-dimensional position acquisition unit highlights the three-dimensional position on the three-dimensional human body structure.

12. A disease location acquisition device, comprising: processor; as well as a memory storing computer readable instructions, Wherein, when the computer readable instructions are executed by the processor, a disease location acquisition method is executed, the method comprising: Obtaining element features of the disease data through the disease data; and In a case where the element feature of the disease data can correspond to at least one of a plurality of disease location models, the three-dimensional location of the disease is acquired using the at least one disease location model, and the acquired three-dimensional location is corrected using a neural network to obtain a corrected three-dimensional location of the disease, wherein the plurality of disease location models are obtained based on different three-dimensional locations of the body according to disease classification information and correspond to a plurality of human organs, respectively, and each disease location model includes at least one label corresponding thereto; and In the case where the element features of the disease data cannot be mapped to at least one of the multiple disease location models, a three-dimensional location of the disease is obtained based on the disease data using a neural network.

13. A computer-readable storage medium for storing a computer-readable program, wherein the program enables a computer to execute the disease location acquisition method according to any one of claims 1 to 6.

14. An electronic device comprising: The disease location acquisition device and display interface according to any one of claims 7 to 11; Wherein, the display interface is configured to display a three-dimensional human body model, which displays disease classification information at the three-dimensional position obtained by the disease position acquisition method described in any one of claims 1-6, and highlights the organ corresponding to the obtained three-dimensional position.

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