WO2021146941A1

WO2021146941A1 - Disease location acquisition method, apparatus, device and computer readable storage medium

Info

Publication number: WO2021146941A1
Application number: PCT/CN2020/073580
Authority: WO
Inventors: 白桦; 颜永阳; 王雨楠; 杨立民
Original assignee: 京东方科技集团股份有限公司
Priority date: 2020-01-21
Filing date: 2020-01-21
Publication date: 2021-07-29
Also published as: CN113728398A

Abstract

A human disease location acquisition method, an apparatus, a device and a computer readable storage medium. Said method comprises: acquiring element features of disease data by means of the disease data (S101); and acquiring, on the basis of the element features of the disease data, a three-dimensional location of the disease by using a plurality of disease location models, the plurality of disease location models respectively corresponding to different three-dimensional locations of a body (S102). By means of said method, the display of a disease on a 3D model can be implemented, so that disease information is displayed to a user more accurately, improving the interactive display effect of a disease and an abnormal indicator, and improving the efficiency and user experience.

Description

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

Technical field

This application relates to the field of computer human-computer interaction technology. Specifically, this application relates to a method, device, equipment, computer-readable storage medium, and electronic equipment for obtaining the location of human diseases.

Background technique

Health has always been an important issue that people pay attention to. With the development of computer technology and communication technology, human diseases and examination abnormalities are mostly displayed in the form of two-dimensional (2D) in the display interface. However, due to the dimensional limitation of 2D, it is impossible to display three-dimensional (3D) complex interspersed structures and structures, and therefore cannot quickly locate lesions and abnormalities, which makes it impossible to display accurate medical diagrams to users, causing users to misunderstand.

Summary of the invention

The present disclosure is made in view of the above-mentioned problems. The present disclosure provides a method, device, equipment, computer-readable storage medium, and electronic equipment for acquiring the location of human diseases.

According to one aspect of the present disclosure, there is provided a method for acquiring a disease location, including: acquiring element characteristics of the disease data through disease data; and acquiring the disease using multiple disease location models based on the element characteristics of the disease data The three-dimensional position of the disease, wherein the multiple disease position models respectively correspond to different three-dimensional positions of the body.

According to an example of the present disclosure, the multiple disease location models correspond to multiple human organs, and each model includes at least one tag corresponding to it, and the multiple disease location models are used based on the element characteristics of the disease data. Obtaining the three-dimensional location of the disease includes: corresponding element features of the disease data to at least one label in the multiple disease location models to obtain the three-dimensional location of the disease.

According to an example of the present disclosure, the method further includes: obtaining 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 through training data training, The training data includes feature data and identification tags, the feature data includes multiple disease information, the identification tags include three-dimensional positions corresponding to the multiple disease information, and the multiple disease information includes multiple disease information. Disease data.

According to an example of the present disclosure, the method further includes: when the element feature of the disease data cannot correspond to at least one of the multiple disease location models, obtaining 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 identification tags, the feature data includes disease classification information that is 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.

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.

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 to the disease classification information. The method further includes: matching element characteristics 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.

According to an example of the present disclosure, the disease data is obtained from one or more of health assessment, physical examination report, health data, and peripheral input.

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

According to one aspect of the present disclosure, there is provided a device for acquiring a disease location, including: an element feature acquiring unit for acquiring element features of the disease data through disease data; and a three-dimensional location acquiring unit for acquiring disease data based on the disease data. Using multiple disease location models to obtain the three-dimensional location of the disease, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.

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 tag corresponding to it, and the three-dimensional location acquisition unit corresponds to the element characteristics of the disease data to all the human organs. At least one label in the multiple disease location models to obtain the three-dimensional location of the disease.

According to an example of the present disclosure, the device further includes: a three-dimensional position obtaining unit obtains the corrected three-dimensional position of the disease through a first neural network based on the disease data, wherein the first neural network is trained through training data Obtained, the training data includes feature data and identification tags, the feature data includes multiple disease information, the identification tags include three-dimensional positions corresponding to the multiple disease information, and the multiple disease information includes multiple Disease data for different diseases.

According to an example of the present disclosure, the device further includes: when the element feature of the disease data cannot correspond to at least one of the multiple disease position models, the three-dimensional position acquisition unit is based on the disease data through the second nerve The network obtains the three-dimensional position of the disease, wherein the second neural network is obtained by training with training data, the training data includes feature data and identification tags, and the feature data includes diseases that are different from the disease classification information Classification information, and the identification label includes a three-dimensional position corresponding to the disease classification information included in the characteristic data.

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 to it, and the three-dimensional position acquisition unit matches the element characteristics 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.

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

According to one aspect of the present disclosure, there is provided a disease location acquisition device, including: a processor; and a memory, in which computer-readable instructions are stored, wherein the disease location is executed when the computer-readable instructions are executed by the processor An acquiring method, the method comprising: acquiring element characteristics of the disease data through disease data; and acquiring the three-dimensional position of the disease using multiple disease location models based on the element characteristics of the disease data, wherein the multiple diseases The position models respectively correspond to different three-dimensional positions of the body.

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

According to one aspect of the present disclosure, there is provided an electronic device including 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, and the three-dimensional human body model uses the above-mentioned disease location acquisition method. The disease classification information is displayed at the obtained three-dimensional position, and the organ corresponding to the obtained three-dimensional position is highlighted.

In the above aspects of the present disclosure, a method for acquiring disease location is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional locations, and then maps the acquired disease data to the disease location model to obtain the disease location model. Three-dimensional position, so as to realize the 3D display of the disease on the 3D model, to more accurately show the disease information to the user, improve the interactive display effect of the disease and abnormal indicators, and improve the efficiency and user experience.

Description of the drawings

Through a more detailed description of the embodiments of the present disclosure in conjunction with the accompanying drawings, the above and other objectives, features, and advantages of the present disclosure will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification, and are used to explain the present disclosure together with the embodiments of the present disclosure, and do not constitute a limitation to the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

Fig. 1 shows a flowchart of a method for acquiring a disease location according to an embodiment of the present disclosure;

2 is a schematic diagram outlining an application scenario of a training method of a neural network model according to an embodiment of the present disclosure;

Fig. 3 shows a flowchart of a method for acquiring a disease location according to another embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of an example of acquiring a disease location according to an embodiment of the present disclosure;

Fig. 5 shows a schematic diagram of a display interface for displaying human body information according to an embodiment of the present disclosure;

Fig. 6 shows a schematic diagram of a display interface for disease display according to an embodiment of the present disclosure;

FIG. 7 shows a block diagram of a device for acquiring a disease location according to an embodiment of the present disclosure;

Fig. 8 shows a block diagram of a disease location acquiring device according to an embodiment of the present disclosure;

FIG. 9 shows a schematic structural diagram of an electronic device according to an embodiment of the present disclosure; and

FIG. 10 shows a schematic diagram of a storage medium according to an embodiment of the present disclosure.

Detailed ways

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work shall fall within the protection scope of the present disclosure.

The "first", "second" and similar words used in the present disclosure do not indicate any order, quantity, or importance, but are only used to distinguish different components. Similarly, "including" or "including" and other similar words mean that the element or item appearing before the word covers the element or item listed after the word and their equivalents, but does not exclude other elements or items. Similar words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "Down", "Left", "Right", etc. are only used to indicate the relative position relationship. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

In this application, a flowchart is used to illustrate the steps of the method according to the embodiment of the application. It should be understood that the preceding or following steps are not necessarily performed in precise order. Instead, the various steps can be processed in reverse order or simultaneously. At the same time, you can also add other operations to these processes, or remove a step or several steps from these processes.

The International Classification of Diseases (ICD10) is a unified international classification for various diseases in order to analyze the differences in the health status of the populations of the world and the cause of death. It is very important for clinical application and management to assign the correct ICD10 code to each patient according to the diagnosis (that is, to add the ICD10 code to the patient's medical record). However, assigning the correct ICD10 code to the patient when visiting a doctor only finds the 2D position of the corresponding disease or disease for the visiting patient, but cannot accurately locate the 3D position of the disease, and cannot show the user accurate medical icons. Cause user misunderstanding. In addition, when assigning codes, medical coders need to consult the doctor’s diagnosis using text phrases and sentences as well as other information in the electronic medical record, and then manually assign appropriate ICD10 codes according to the coding instructions. This is easy to happen in this process. A variety of errors have caused errors in locating the location of the disease. For example, doctors often use abbreviations and synonyms when writing diagnosis descriptions, which can lead to confusion and inaccuracy when coders match ICD10 codes with these abbreviations and synonyms.

In addition, ICD10 codes are organized in a hierarchical structure, where the upper code represents a wide range of disease categories, and the lower code represents more specific diseases. Therefore, when the coder matches the diagnosis description to an overly broad code instead of a more specific code, a wrong code will also occur, which makes it more difficult to locate the disease location.

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 acquired disease data to the disease location model, thereby realizing the disease on the 3D model 3D display to more accurately display disease information to users and improve user experience.

The embodiments and examples of the present disclosure will be described in detail below with reference to the accompanying drawings.

At least one embodiment of the present disclosure provides a method for acquiring a disease location, a device for acquiring a disease location, a device for acquiring a disease location, and a computer-readable storage medium. The following is a non-limiting description of the disease location acquisition provided according to at least one embodiment of the present disclosure through several examples and embodiments. As described below, these specific examples and embodiments are different if they do not conflict with each other. The features can be combined with each other to obtain new examples and embodiments, and these new examples and embodiments also fall within the protection scope of the present disclosure.

Hereinafter, a method for acquiring a disease location according to an embodiment of the present disclosure will be described with reference to FIGS. 1-4. First, a method for acquiring a disease location according to an embodiment of the present disclosure will be described with reference to FIG. 1. This method can be automatically completed by a computer or the like. For example, the method can be applied to display the three-dimensional position of human diseases and so on. For example, the method for acquiring the disease location can be implemented in software, hardware, firmware or any combination thereof, loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, and a network server.

For example, the method for obtaining the location of the disease is applicable to a computing device that includes any electronic device with computing functions, such as a mobile phone, a laptop, a tablet, a desktop computer, a web server, etc., which can load and execute the disease The location acquisition method is not limited in the embodiment of the present disclosure. For example, the computing device may include a central processing unit (CPU) or a graphics processing unit (Graphics Processing Unit, GPU) and other forms of processing units, storage units, etc. that have data processing capabilities and/or instruction execution capabilities, 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 method for acquiring the disease location provided in the embodiments of the present disclosure is implemented by running code or instructions. For example, the computing device may also include a display component, such as a liquid crystal display (LCD), an organic light emitting diode (OLED) display, and a quantum dot light emitting diode (Quantum Dot Light Emitting). Diode, QLED) display screens, projection components, VR head-mounted display devices (for example, VR helmets, VR glasses), etc., which are not limited in the embodiments of the present disclosure. For example, the display part can display the object to be displayed.

As shown in Fig. 1, the method for acquiring the location of the disease includes the following steps S101 to S102.

In step S101, the element characteristics of the disease data are acquired through the disease data.

In step S102, based on the element characteristics of the disease data, multiple disease location models are used to obtain the three-dimensional location of the disease, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.

For step S101, for example, the disease data may be data related to the user's current disease, which contains a language description of the disease, which can be obtained from the user's personal health information. For example, the user's disease data can be obtained from analyzing one or more of health assessment, physical examination report, health data, and peripheral input. For example, the disease data may be data related to the current disease in the main complaint of a given medical record (or health data), current medical history, past history, and diagnosis results given by a doctor. In the embodiments of the present disclosure, the disease data can be handwritten data, and the data required in the disease data can be obtained through OCR or manual reading; it can also be electronic disease data, which can be exported through the electronic disease data management platform data.

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 can be ICD10 or other suitable disease classification databases, which is not limited here.

For example, the disease classification information may correspond to different disease names, and each disease classification information contains at least one name tag corresponding to it. In this case, the element characteristics of the disease data may be matched to at least one of the disease classification information. The name tag obtains the disease name corresponding to the disease data. For example, diabetes in the disease classification information corresponds to label 1, then when the element characteristics of the disease data correspond to diabetes, the disease data is matched to label 1 in the disease classification information, so that the disease name corresponding to the disease data is diabetes.

For step S101, for example, the element characteristics of the disease data can be represented by a vector representation or a word vector 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. To get the name of the disease.

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

In addition, because the correct disease classification information code or label is assigned to the patient when the patient visits a doctor, only the corresponding disease or 2D position of the patient can be found, but the 3D position of the disease cannot be accurately located. To this end, based on the disease classification information, multiple disease location models can be obtained, the multiple disease location models respectively corresponding to different three-dimensional locations of the body. For example, a disease location model can be obtained based on ICD10. For example, 13904 diseases in the ICD10 can be integrated and analyzed, and the ICD10 can be divided into multiple disease location models based on different human organs or tissues, so as to map the diseases to the 3D model. For example, the ICD 10 can be divided into multiple disease location models through a classifier (such as clustering, support vector machine (SVM), K-nearest neighbor algorithm) or neural network, where different disease location models correspond to different human organs or tissues.

For example, multiple disease location models correspond to multiple human organs, and each model includes 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 may also include sub-labels. For example, the "heart" tag may contain subtags "left atrium", "left ventricle", "right atrium", "right ventricle" and so on.

For example, in step S102, based on the element features of the disease data, using multiple disease location models to obtain the three-dimensional location of the disease may include: corresponding the element features of the disease data to those in the multiple disease location models. At least one tag to obtain the three-dimensional location of the disease.

For example, each tag can correspond to a code, and each subtag can also correspond to a code. For example, in the disease location model corresponding to ICD10, the code for labeling myasthenia gravis is G80.001, the code for labeling the esophagus is I95, and the code for esophageal varices (a subtag of the esophagus) is I95.5. For example, if the disease data is "the test tube cavity is not significantly narrowed, the gastroscope can pass, and the lower esophageal varices can be seen", the disease data is matched to the disease location model according to the element feature "lower esophageal vein" in the disease data The code of I95.5, so that the three-dimensional position of the disease can be obtained as "esophagus, esophageal vein".

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

After obtaining the three-dimensional position of the disease, the disease can be displayed on the organ position corresponding to the three-dimensional human body model in the display interface of the electronic device to realize 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.

Further, for the 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 characteristics of the disease data cannot correspond to at least one of the multiple disease location models, based on the disease data, a neural network can be directly used to obtain the three-dimensional location of the disease.

Neural network is a large-scale, multi-parameter optimization tool. Relying on a large amount of training data, deep neural networks can learn hidden features that are difficult to summarize in the data, thereby completing a number of complex tasks, such as face detection, image semantic segmentation, text summary extraction, object detection, motion tracking, natural language translation Wait. Obtaining the vector representation of the text through the word vector refers to expressing each word in the text as a single vector, and generating a vector representation of the text through a high degree of summary and abstraction. Techniques based on neural network models can be used in, for example, medical disease location processing, so as to automatically process disease data.

An application scenario of a training method of a neural network model according to an embodiment of the present disclosure is schematically described with reference to FIG. 2.

As shown in FIG. 2, a neural network model 20 according to an embodiment of the present disclosure is used to receive an input 10, and the input 10 performs feature extraction processing, and based on the extracted features, an output 30 is generated. In the embodiment of the present disclosure, the input 10 may be, for example, an image, video, or natural language text waiting to be processed. 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 input.

In an embodiment of the present disclosure, the neural network model 20 may be trained according to the training method shown in FIG. 3.

Fig. 3 is a flowchart of a method for acquiring a disease location according to another embodiment of the present disclosure. As shown in FIG. 3, the method for acquiring the location of the disease includes the following steps S201 to S203.

In step S201, the element characteristics of the disease data are acquired through the disease data.

In step S202, based on the element characteristics of the disease data, multiple disease location models are used to obtain the three-dimensional location of the disease, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.

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

Since the steps S201-S202 are the same as the steps described above with reference to the steps S101-S102 described in FIG. 1, they will not be repeated here.

Next, in step S203, the corrected three-dimensional position of the disease may be obtained through the 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 tags, the feature data includes multiple disease information, and the identification tags include three-dimensional positions corresponding to the multiple disease information, The plurality of disease information includes disease data of a plurality of different diseases.

For example, the first neural network may be Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Back Propagation (BP), Linear Neural Network, etc. Do restrictions.

For example, multiple disease information may be obtained from analyzing one or more of health assessment, physical examination report, health data, and peripheral input, and the three-dimensional location corresponding to the multiple disease information may be more detailed location information manually labeled. By training the first neural network with multiple disease information marked with more detailed position information, the three-dimensional position corresponding to the disease data can be corrected, so that the obtained three-dimensional position is more accurate.

Alternatively, the training data may include feature data and an identification label, the feature data including a plurality of disease information and disease classification information, and the identification label includes a three-dimensional position corresponding to the plurality of disease information and corresponding to the disease classification information. The three-dimensional location information. It should be realized that in addition to the disease classification information and its corresponding three-dimensional position in the training data, multiple disease information and their corresponding three-dimensional positions are newly added, thereby increasing the types and scope of disease positions. Therefore, when the disease location model corresponding to the disease data is inaccurate, the first neural network trained with more training data can be used to correct the three-dimensional location of the disease data.

In addition, when the element characteristics of the disease data cannot correspond to at least one of the disease location models, the first neural network can be directly used to obtain the three-dimensional location of the disease based on the disease data. Since the trained neural network has a predictive function, when the disease data cannot correspond to at least one of the disease location models, the three-dimensional position of the disease can be obtained through the neural network with the predictive function.

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.

In the above aspects of the present disclosure, a method for acquiring disease location is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional locations, and then maps the acquired disease data to the disease location model to obtain the disease location model. 3D position, and then use neural network to analyze the disease data to obtain the corrected 3D position of the disease, so as to realize the 3D display of the disease on the 3D model, to more accurately show the disease information to the user, and to improve the interaction between the disease and abnormal indicators Display effect, improve efficiency and user experience.

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

For example, the element characteristics of some disease data may not correspond to the existing disease location model. At this time, the second neural network can be trained through training data composed of other disease classification information or disease location models, and the second neural network can be used. The network obtains the three-dimensional position of the disease.

For example, the second neural network is obtained by training with training data, the training data includes feature data and identification tags, the feature data includes disease classification information different from the disease classification information, and the identification tags include the same as the feature data. The three-dimensional position corresponding to the disease classification information included in.

For example, the second neural network may also be Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Back Propagation (BP), Linear Neural Network, etc., here No restrictions.

For example, the training data uses disease classification information different from the disease classification information, thereby increasing the type and range of disease locations. Therefore, when the disease data cannot correspond 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.

In the above aspects of the present disclosure, a method for acquiring disease location is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional locations, and then maps the acquired disease data to the disease location model to obtain the disease location model. The three-dimensional position, when the disease data cannot correspond to at least one of the multiple disease position models, based on the disease data, the three-dimensional position of the disease is directly obtained through the second neural network to obtain the three-dimensional position of the disease, thereby Realize the 3D display of the disease on the 3D model to more accurately show the disease information to the user, improve the interactive display effect of the disease and abnormal indicators, and improve the efficiency and user experience.

After the disease location acquisition method according to the embodiment of the present disclosure is described above through FIGS. 1-3, the following describes an example of the disease location acquisition according to the embodiment of the present disclosure based on FIG. 4.

Fig. 4 shows a schematic diagram of an example of acquiring the location of a disease according to an embodiment of the present disclosure. As shown in Figure 4, first, get disease data, which contains the language description of the disease. The disease data may be obtained from one or more of the health assessment 51, the physical examination report 52, the health data 53, and the peripheral input 54. Next, the element characteristics of the disease data are obtained from the disease data, and then the element characteristics of the disease data are mapped to the disease classification information 55 to obtain the disease name corresponding to the disease data. Since the multiple disease location models 56 are obtained through the disease classification information 55, the element characteristics of the disease data can be mapped to the multiple disease location models 56 to obtain the three-dimensional location of the disease, thereby achieving 3D display. In addition, since the disease classification information contains various descriptions of the disease, based on the element characteristics of the disease data, the disease location acquisition method can also be used to obtain the disease or problem corresponding to the disease data. System/site 58, health index 59 and other information.

The disease information display method provided in this application generally runs through electronic equipment to realize information interaction with the user. After turning on the electronic equipment to enter the display interface of disease information display (for example, the digital human body APP), the initial display on the display interface is usually A complete three-dimensional human body model is generally presented as a three-dimensional human body image that is upright and in a natural stretched state. In addition, highlight the diseased organ in the place where the disease is detected so that the patient can understand it.

The display interface for displaying human body information according to an embodiment of the present disclosure will be described below with reference to FIGS. 5-6. FIG. 5 is a schematic diagram of a display interface for displaying human body information according to an embodiment of the disclosure. FIG. 6 is a schematic diagram of a display interface for displaying diseases according to an embodiment of the disclosure.

As shown in Figure 5, on the display interface displaying the three-dimensional human body model, at least one curve surrounding the three-dimensional human body model is displayed, and several human body information labels are set on the curve. In some feasible implementations, as shown in Figure 5 , The curve includes at least one of the following: elliptical arc and spiral; the elliptical arc surrounds the position of the chest cavity of the three-dimensional human model; the spiral extends from the foot of the three-dimensional human model to the head of the three-dimensional human model; curve and display The interface is relatively static. The curve can also be another form of geometric curve that can surround the three-dimensional human body model. It can be one or two parallel displayed at a certain interval, or when the curve is a spiral line, it can appear as a double helix structure. Of course, the quantity It can even be 3, 4, etc., which can be set according to actual needs.

Furthermore, the human body information tags correspond to different parts of the three-dimensional human body model. Different parts of the three-dimensional human body model can be determined according to multiple classifications. In some feasible implementations, the human body information label includes at least one of the following: human organ category information, Human body system category information or human body parameter information; the distance 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 according to human system categories, including circulatory system, digestive system, respiratory system, reproductive system, immune system, etc., or according to Partial classification, 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 set 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 distance between adjacent human body information labels on the curve is equal. As it gets farther and farther from the middle position, the human body information label gradually becomes smaller.

Of course, the human body information labels can also be set to different states. In some feasible implementation manners, the curve includes a preset prominent area and a preset non-highlighted 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 labels are set on the curve, specifically including: if the human body information label moves to the preset highlight area, the human body information label is enlarged and displayed, and Set the human body information label to the activated state; if the human body information label moves to the preset non-protruding area, the human body information label is reduced to be displayed, and the human body information label is set to the inactive state.

The human body information label can move on the curve, and at a specific position of the curve, for example, on the curve segment close to the central axis of the three-dimensional human body model, the human body information label moves to this preset highlight area, then the human body information label is activated , The appearance is shown in an enlarged state, can be selected, and the next operation is performed accordingly, the other areas are the preset non-protruding areas, and the human body information tags in the preset non-protruding areas are in the inactive state, so compare them accordingly. Small size display. During the movement of the human body information label, the process of zooming in and out is also displayed on the display interface, which can form a vivid three-dimensional dynamic process and improve the user's immersion in human-computer interaction. Of course, in addition to the shape change by zooming in or zooming out, the characteristics of changing the brightness and color of the human body information label can also be added.

After the location of the disease is detected by the method described in the present disclosure, as shown in FIG. 6, the corresponding position of the three-dimensional human body model is highlighted, so as to realize the 3D display of the disease on the 3D model, so as to show the user more accurately Disease information to enhance user experience.

Above, the method for acquiring the location of a disease according to an embodiment of the present invention has been described with reference to the accompanying drawings. Hereinafter, a disease location acquiring device according to an embodiment of the present disclosure will be described.

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

The element feature acquiring unit 1001 is configured to acquire the element feature of the disease data through the disease data.

For example, the disease data may be data related to the user's current disease, which may be obtained from the user's personal health information. For example, the user's disease data can be obtained from analyzing one or more of health assessment, physical examination report, health data, and peripheral input.

The three-dimensional position acquiring unit 1002 is configured to acquire the three-dimensional position of the disease using multiple disease location models based on the element characteristics of the disease data, wherein the multiple disease location models respectively correspond to different three-dimensional positions of the body.

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 can be ICD10 or other suitable disease classification databases, which is not limited here. For example, the disease classification information may correspond to different disease names, and each disease classification information contains at least one name tag corresponding to it. In this case, the element characteristics of the disease data may be matched to at least one of the disease classification information. The name tag obtains the disease name corresponding to the disease data.

For example, a disease location model can be obtained based on ICD10. For example, the diseases in the ICD10 can be integrated and analyzed, and the ICD10 can be divided into multiple disease location models based on different human organs or tissues, so as to map the diseases to the 3D model.

For example, the three-dimensional position obtaining 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.

After obtaining the three-dimensional position of the disease, it can be displayed on the corresponding organ model to realize 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.

Further, the three-dimensional position acquiring unit 1002 can use the neural network to analyze the disease data to obtain the corrected three-dimensional position of the disease.

For example, the three-dimensional position obtaining unit 1002 may obtain the corrected three-dimensional position of the disease through the 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 tags, the feature data includes multiple disease information, and the identification tags include three-dimensional positions corresponding to the multiple disease information, The plurality of disease information includes disease data of a plurality of different diseases.

Alternatively, when the disease data cannot correspond to at least one of the multiple disease position models, the three-dimensional position obtaining unit 1002 may directly obtain the three-dimensional position of the disease through the first neural network.

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

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 that is different from the disease classification information, and the identification labels include diseases that are different from those included in the feature data. The three-dimensional position corresponding to the classification information, and the plurality of disease information includes disease data of a plurality of different diseases.

In the above aspects of the present disclosure, a method for acquiring disease location is proposed. Specifically, the present disclosure creates a disease location model based on different three-dimensional locations, and then maps the acquired disease data to the disease location model to obtain the disease location model. Three-dimensional position. The three-dimensional position acquiring unit 1002 can then use the neural network to analyze the disease data to obtain the corrected three-dimensional position of the disease. When the disease data cannot correspond to at least one of the multiple disease location models, the three-dimensional location obtaining unit 1002 may also directly obtain the three-dimensional location of the disease through the first neural network or the second neural network based on the disease data. , So as to realize the 3D display of the disease on the 3D model, to more accurately display the disease information to the user, improve the interactive display effect of the disease and abnormal indicators, and improve the efficiency and user experience.

Hereinafter, a disease location acquisition device according to an embodiment of the present disclosure will be described with reference to FIG. 8. FIG. 8 is a schematic diagram of a disease location acquiring device 2000 according to an embodiment of the present disclosure. Since the disease location acquisition device of this embodiment has the same details as the method described above with reference to FIG. 1, for the sake of simplicity, detailed description of the same content is omitted here.

As shown in FIG. 8, the disease location acquisition device 2000 includes a processor 210, a memory 220, and one or more computer program modules 221.

For example, the processor 210 and the memory 220 are connected through 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, instructions in one or more computer program modules 221 may be executed by the processor 210. For example, the bus system 230 may be a commonly used serial or parallel communication bus, etc., which is not limited in the embodiments of the present disclosure.

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

The memory 220 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include random access memory (RAM) and/or cache memory (cache), for example. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, etc., for example. 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) and/or other desired functions in the embodiments of the present disclosure, For example, how to obtain the location of the disease. The computer-readable storage medium can also store various application programs and various data, such as element characteristics of disease data, disease location models, and various data used and/or generated by the application programs.

It should be noted that, for the sake of clarity and conciseness, the embodiment of the present disclosure does not provide all the components of the disease location acquisition device 2000. In order to realize the necessary functions of the disease location acquisition device 2000, those skilled in the art can provide and set other unshown component units according to specific needs, which are not limited in the embodiments of the present disclosure.

Regarding the technical effects of the disease location acquiring device 1000 and the disease location acquiring device 2000 in different embodiments, reference may be made to the technical effects of the disease location acquiring method provided in the embodiments of the present disclosure, which will not be repeated here.

The disease location acquiring device 1000 and the disease location acquiring device 2000 can be used in various appropriate electronic devices.

FIG. 9 is a schematic structural diagram of an electronic device provided by at least one embodiment of the present disclosure. Terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (e.g. Mobile terminals such as car navigation terminals) and fixed terminals such as digital TVs, desktop computers, etc. The electronic device shown in FIG. 9 is only an example, and should not bring any limitation to the function and scope of use of the embodiments of the present disclosure.

For example, the electronic device includes the disease location acquiring device 1000 provided by any embodiment of the present disclosure and a display interface (for example, the output device 307 shown in FIG. 9); 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 position acquisition method, and highlights the organ corresponding to the obtained three-dimensional position.

For example, as shown in FIG. 9, in some examples, the electronic device 300 includes a processing device (such as a central processing unit, a graphics processor, etc.) 301, which can be based on a program stored in a read-only memory (ROM) 302 or from a storage device. The device 308 loads a program in a random access memory (RAM) 303 to perform various appropriate actions and processing. In the RAM 303, various programs and data required for the operation of the computer system are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected by the bus 304 here. An input/output (I/O) interface 305 is also connected to the bus 304.

For example, the following components can be connected to the I/O interface 305: including input devices 306 such as touch screens, touch pads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; including input devices such as liquid crystal displays (LCD), speakers, vibration The output device 307 of the device, etc.: includes a storage device 308 such as a magnetic tape, a hard disk, etc.; and a communication device 309 including a network interface card such as a LAN card, a modem, and the like. The communication device 309 may allow the electronic device 300 to perform wireless or wired communication with other devices to exchange data, and perform communication processing via a network such as the Internet. The driver 310 is also connected to the I/O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that the computer program read from it can be installed into the storage device 309 as needed. Although FIG. 9 shows the electronic device 300 including various devices, it should be understood that it is not required to implement or include all of the illustrated devices. More or fewer devices may be implemented alternatively or included.

For example, the electronic device 300 may further include a peripheral interface (not shown in the figure) and the like. The peripheral interface can be various types of interfaces, such as a USB interface, a lightning interface, and the like. The communication device 309 can 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 telephone network, a wireless local area network (LAN), and/or a metropolitan area network ( MAN). Wireless communication can use any of a variety of communication standards, protocols and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA) , Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi (e.g. based on IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n standards), voice transmission based on Internet protocol (VoIP), Wi-MAX, protocols used for e-mail, instant messaging and/or short message service (SMS), or any other suitable communication protocol.

For example, the electronic device can be any device such as a mobile phone, a tablet computer, a notebook computer, an e-book, a game console, a television, a digital photo frame, a navigator, etc., or can be any combination of electronic devices and hardware. This is not limited.

For example, according to an embodiment of the present disclosure, the process described above with reference to the flowchart may 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 may 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.

It should be noted that the above-mentioned computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the 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 a combination of any of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable removable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device. In the embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as a part of a carrier wave, and a computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium. The computer-readable signal medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device . The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

In some embodiments, the client and server can communicate with any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and can communicate with digital data in any form or medium. Communication (e.g., communication network) interconnects. Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (for example, the Internet), and end-to-end networks (for example, ad hoc end-to-end networks), as well as any currently known or future research and development network of.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist alone without being assembled into the electronic device.

The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet protocol addresses; A node evaluation request for an Internet Protocol address, 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 the edge node in the content distribution network.

Alternatively, the aforementioned computer-readable medium carries one or more programs, and when the aforementioned 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; Among at least two Internet Protocol addresses, an Internet Protocol address is selected; the selected Internet Protocol address is returned; the received Internet Protocol address indicates an edge node in the content distribution network.

The computer program code used to perform the operations of the present disclosure can be written in one or more programming languages or a combination thereof. The above-mentioned programming languages include but are not limited to object-oriented programming languages such as Java, Smalltalk, C++, and Including conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to pass Internet connection).

The functions described above in this document 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 can be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logical device (CPLD) and so on.

In various embodiments of the present disclosure, a machine-readable medium may be a tangible medium, which may contain or store a program for use by the instruction execution system, apparatus, or device or in combination with the instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The 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. More specific examples of machine-readable storage media would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

At least one embodiment of the present disclosure also provides a storage medium. FIG. 10 is a schematic diagram of a storage medium provided by at least one embodiment of the present disclosure. For example, as shown in FIG. 10, the storage medium 400 non-transitory stores computer-readable instructions 401, and when the non-transitory computer-readable instructions are executed by a computer (including a processor), any one of the embodiments of the present disclosure can be executed. How to get the location of the disease.

For example, the storage medium may be any combination of one or more computer-readable storage media. For example, one computer-readable storage medium contains computer-readable program code for determining the number of display faces of the sub-model, and another computer-readable storage medium The medium contains computer-readable program code that determines the location of the disease. For example, when the program code is read by a computer, the computer can 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.

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, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), Portable compact disk read-only memory (CD-ROM), flash memory, or any combination of the foregoing storage media may also be other suitable storage media.

The following points need to be explained:

(1) The drawings of the embodiments of the present disclosure only refer to the structures related to the embodiments of the present disclosure, and other structures can refer to the usual design.

(2) In the case of no conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

The above are only exemplary implementations of the present disclosure, and are not used to limit the protection scope of the present disclosure, which is determined by the appended claims.

Claims

A method for obtaining the location of a disease, including:

Obtain the element characteristics of the disease data through the disease data; and

Based on the element characteristics of the disease data, multiple disease location models are used to obtain the three-dimensional location of the disease, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.
The method according to claim 1, wherein the multiple disease location models correspond to multiple human organs, and each model contains at least one label corresponding to it,

The obtaining the three-dimensional position of the disease by using multiple disease position models based on the element characteristics of the disease data includes:

The element feature of the disease data is corresponding to at least one label in the multiple disease location models to obtain the three-dimensional location of the disease.
The method according to claim 2, further comprising:

Based on the disease data, the corrected three-dimensional position of the disease is obtained through the first neural network,

Wherein, the first neural network is obtained by training with training data, the training data includes feature data and identification tags, the feature data includes multiple disease information, and the identification tags include information corresponding to the multiple disease information. The three-dimensional position of the disease information includes disease data of a plurality of different diseases.
The method according to claim 2, further comprising:

When the element characteristics of the disease data cannot correspond to at least one of the multiple disease location models, based on the disease data, obtain the three-dimensional location 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 identification labels, the feature data includes disease classification information different from the disease classification information, and the identification labels include The three-dimensional position corresponding to the disease classification information included in the feature data.
The method according to any one of claims 1 to 4, wherein the multiple disease location models are obtained by dividing the disease classification information based on different three-dimensional locations of the body.
The method according to claim 5, wherein the disease classification information corresponds to different disease names, and each disease classification information contains at least one name tag corresponding to the disease classification information, and the method further comprises:

Matching the element characteristics of the disease data to at least one name label of the disease classification information to obtain the disease name corresponding to the disease data.
The method of claim 6, wherein:

The disease data is obtained from one or more of health assessment, physical examination report, health data and peripheral input.
The method of claim 6, wherein:

After obtaining the three-dimensional position of the disease, highlight the three-dimensional position on the three-dimensional human body structure.
A device for acquiring disease location, including:

An element feature acquisition unit for acquiring the element feature of the disease data through the disease data; and

The three-dimensional position acquiring unit is configured to acquire the three-dimensional position of the disease using multiple disease location models based on the element characteristics of the disease data, wherein the multiple disease location models respectively correspond to different three-dimensional positions of the body.
The device according to claim 9, wherein the multiple disease location models correspond to multiple human organs, and each model contains at least one tag corresponding to it, and the three-dimensional location acquisition unit integrates elements of the disease data The feature corresponds to at least one label in the multiple disease location models to obtain the three-dimensional location of the disease.
The device according to claim 10, further comprising:

The three-dimensional position obtaining unit obtains the corrected three-dimensional position of the disease through the 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 tags, the feature data includes multiple disease information, and the identification tags include information corresponding to the multiple disease information. The three-dimensional position of the disease information includes disease data of a plurality of different diseases.
The device according to claim 10, further comprising:

When the element feature of the disease data cannot correspond to at least one of the multiple disease location models, the three-dimensional location acquiring unit obtains the three-dimensional location 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 identification labels, the feature data includes disease classification information different from the disease classification information, and the identification labels include and The three-dimensional position corresponding to the disease classification information included in the feature data.
The device according to any one of claims 9-12, wherein the multiple disease location models are obtained by dividing the disease classification information based on different three-dimensional locations of the body.
The device according to claim 10, wherein the disease classification information corresponds to different disease names, and each disease classification information contains at least one name tag corresponding to the disease classification information, and the three-dimensional position acquisition unit combines the disease data with The element feature is matched to at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.
The device according to claim 14, wherein:

After obtaining the three-dimensional position of the disease, the three-dimensional position obtaining unit highlights the three-dimensional position on the three-dimensional human body structure.
A disease location acquisition device, including:

Processor; and

Memory, in which computer-readable instructions are stored,

Wherein, when the computer-readable instruction is executed by the processor, the method for acquiring the disease location is executed, and the method includes:

Obtain the element characteristics of the disease data through the disease data; and

Based on the element characteristics of the disease data, multiple disease location models are used to obtain the three-dimensional location of the disease, wherein the multiple disease location models respectively correspond to different three-dimensional locations of the body.
A computer-readable storage medium for storing a computer-readable program, the program causing a computer to execute the method for acquiring the disease location according to any one of claims 1-8.
An electronic device, comprising: the disease location acquiring device according to any one of claims 9-15 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 a three-dimensional position obtained by using the disease position acquisition method according to any one of claims 1-9, and Highlight the organ corresponding to the obtained three-dimensional position.