CN113728398A

CN113728398A - Disease position acquisition method, device, equipment and computer-readable storage medium

Info

Publication number: CN113728398A
Application number: CN202080000061.1A
Authority: CN
Inventors: 白桦; 颜永阳; 王雨楠; 杨立民
Original assignee: BOE Technology Group Co Ltd
Current assignee: BOE Technology Group Co Ltd
Priority date: 2020-01-21
Filing date: 2020-01-21
Publication date: 2021-11-30
Also published as: WO2021146941A1

Abstract

A human disease position acquisition method, a human disease position acquisition device, human disease position acquisition equipment and a computer readable storage medium are provided. The method comprises the following steps: acquiring element characteristics of disease data through the disease data (S101); and acquiring a three-dimensional position of the disease using a plurality of disease position models based on the element features of the disease data, wherein the plurality of disease position models respectively correspond to different three-dimensional positions of the body (S102). By the method, the display of the disease on the 3D model can be realized, so that the disease information can be displayed to the user more accurately, the interactive display effect of the disease and the abnormal index is improved, and the efficiency and the user experience are improved.

Description

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

Technical Field

The application relates to the technical field of computer human-computer interaction, in particular to a method, a device, equipment, a computer readable storage medium and electronic equipment for acquiring a human body disease position.

Background

Health is always an important issue of people's attention, and with the development of computer technology and communication technology, human diseases and abnormal examinations are often displayed in a two-dimensional (2D) form in a display interface. However, due to the dimension limitation of 2D, three-dimensional (3D) complex interpenetration structures and structures cannot be displayed, so that diseased regions and abnormalities cannot be quickly located, an accurate medical diagram cannot be displayed to a user, and misunderstanding of the user is caused.

Disclosure of Invention

The present disclosure has been made in view of the above problems. The disclosure provides a human disease position acquisition method, a human disease position acquisition device, a human disease position acquisition equipment, a computer readable storage medium and electronic equipment.

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

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

According to an example of the present disclosure, the method further comprises: 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 by training data, the training data comprises characteristic data and identification labels, the characteristic data comprises a plurality of disease information, the identification labels comprise three-dimensional positions corresponding to the plurality of disease information, and the plurality of disease information comprises disease data of a plurality of different diseases.

According to an example of the present disclosure, the method further comprises: when the element features of the disease data cannot correspond to at least one of the plurality of disease location models, obtaining a three-dimensional location of the disease through a second neural network based on the disease data, wherein the second neural network is obtained through training of training data, the training data includes feature data and an identification tag, the feature data includes disease classification information different from the disease classification information, and the identification tag includes a three-dimensional location corresponding to the disease classification information included in the feature data.

According to an example of the present disclosure, the plurality of disease location models are partitioned based on different three-dimensional locations of the body by disease classification information.

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, the method further comprising: matching the element features of the disease data to at least one name label of the disease classification information to obtain a disease name corresponding to the disease data.

According to one example of the present disclosure, the disease data is obtained from one or more of a health assessment, a physical examination report, health data, and a 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 a three-dimensional human body structure.

According to an aspect of the present disclosure, there is provided a disease position acquiring apparatus including: the element characteristic acquisition unit is used for acquiring element characteristics of the disease data through the disease data; and a three-dimensional position acquisition unit configured to acquire a three-dimensional position of the disease using a plurality of disease position models based on an element feature of the disease data, wherein the plurality of disease position models respectively correspond to different three-dimensional positions of a body.

According to an example of the present disclosure, the plurality of disease location models correspond to a plurality of human organs, and each model includes at least one label corresponding thereto, and the three-dimensional location obtaining unit corresponds the element feature of the disease data to the at least one label in the plurality of disease location models to obtain the three-dimensional location of the disease.

According to an example of the present disclosure, the apparatus further comprises: the three-dimensional position acquisition unit acquires a corrected three-dimensional position of the disease through a first neural network based on the disease data, wherein the first neural network is acquired through training of training data, the training data includes feature data and identification tags, the feature data includes a plurality of pieces of disease information, the identification tags include three-dimensional positions corresponding to the plurality of pieces of disease information, and the plurality of pieces of disease information include pieces of disease data of a plurality of different diseases.

According to an example of the present disclosure, the apparatus further comprises: when the element feature of disease data cannot correspond to at least one of the plurality of disease location models, the three-dimensional location acquisition unit acquires the three-dimensional location of the disease through a second neural network based on the disease data, wherein the second neural network is obtained through training by training data, the training data includes feature data including disease classification information different from the disease classification information, and an identification tag including a three-dimensional location corresponding to the disease classification information included in the feature data.

According to an example of the present disclosure, the disease classification information corresponds to different disease names, and each disease classification information includes at least one name tag corresponding thereto, and the three-dimensional position obtaining unit matches the element feature of the disease data to the 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 three-dimensional position acquisition unit highlights a three-dimensional position of the disease on a three-dimensional human body structure after acquiring the three-dimensional position.

According to an aspect of the present disclosure, there is provided a disease position acquiring apparatus including: a processor; and a memory having computer readable instructions stored therein, wherein the computer readable instructions, when executed by the processor, perform a disease location acquisition method, the method comprising: acquiring element characteristics of disease data through the disease data; and acquiring a three-dimensional position of the disease using a plurality of disease position models based on the element features of the disease data, wherein the plurality of disease position models respectively correspond to different three-dimensional positions of the body.

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

According to one aspect of the present disclosure, an electronic device is provided, which includes the disease position acquiring apparatus and a display interface; 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 disease position obtaining method, and highlights an organ corresponding to the obtained three-dimensional position.

In the above aspect of the present disclosure, a disease position obtaining method is provided, and specifically, the present disclosure creates a disease position model based on different three-dimensional positions, and then obtains a three-dimensional position of a disease by mapping obtained disease data to the disease position model, thereby realizing a 3D display of the disease on the 3D model, so as to more accurately display disease information to a user, improve an interactive display effect of the disease and an abnormal index, and improve efficiency and user experience.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.

Fig. 1 shows a flow chart of a disease location acquisition method according to an embodiment of the present disclosure;

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

fig. 3 shows a flow chart of a disease location acquisition method according to another embodiment of the present disclosure;

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

FIG. 5 shows a display interface schematic of human body information presentation according to an embodiment of the disclosure;

FIG. 6 shows a display interface schematic of a disease presentation in accordance with an embodiment of the present disclosure;

fig. 7 shows a block diagram of a disease location acquisition device according to an embodiment of the present disclosure;

fig. 8 shows a block diagram of a disease location acquisition 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 disclosure.

Detailed Description

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without any inventive step, are intended to be within the scope of the present disclosure.

The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. Likewise, the word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.

Flow charts are used herein to illustrate steps of methods according to embodiments of the present application. It should be understood that the preceding and following steps are not necessarily performed in the exact order in which they are performed. Rather, various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or steps may be removed from the processes.

The international classification of diseases (ICD10) is an international universal, unified classification made to face various diseases for the purpose of analyzing differences in health status and cause of death in the population of countries around the world. Assigning the correct ICD10 code to each patient visit (i.e., adding the ICD10 code to the patient's medical record) based on the diagnosis is very important for clinical application and management. However, the correct ICD10 code is assigned to a patient during the patient visit, which only finds the corresponding disease or 2D position of the disease for the patient to be visited, but cannot be precisely located at the 3D position of the disease, so that the user cannot be presented with an accurate medical diagram, which is easily misunderstood by the user. In addition, when assigning codes, medical coding personnel need to consult diagnoses described by doctors using text phrases and sentences and other information in electronic medical records, and then manually assign the appropriate ICD10 codes according to coding guidance, and various errors are easy to occur in the process, so that errors also occur in locating the positions of diseases. For example, physicians often use abbreviations and synonyms when writing diagnostic descriptions, which can lead to confusion and inaccuracy for the encoding personnel when matching ICD10 codes to these abbreviations and synonyms.

Further, the ICD10 codes are organized in a hierarchical structure, with upper layer codes representing a wide range of disease categories and lower layer codes representing more specific diseases. Thus, where the encoding personnel matches the diagnostic description to an overly broad code, rather than a more specific code, a mis-code may also occur, making locating the disease location more difficult.

The disclosure provides a disease position obtaining method, which creates a disease position model based on different three-dimensional positions, and then obtains the three-dimensional position of a disease by mapping obtained disease data to the disease position model, so that 3D display of the disease on a 3D model is realized, disease information is displayed to a user more accurately, and user experience is improved.

Embodiments of the present disclosure and examples thereof are described in detail below with reference to the accompanying drawings.

At least one embodiment of the present disclosure provides a disease position acquisition method, a disease position acquisition apparatus, a disease position acquisition device, and a computer-readable storage medium. The following non-limiting illustration of disease location acquisition provided according to at least one embodiment of the present disclosure is provided by several examples and embodiments, and as described below, different features of these specific examples and embodiments may be combined with each other without conflicting with each other to obtain new examples and embodiments, which also fall within the scope of the present disclosure.

A disease location acquisition method according to an embodiment of the present disclosure is described below with reference to fig. 1 to 4. First, a disease location acquisition method according to an embodiment of the present disclosure is described with reference to fig. 1. The method can be automatically completed by a computer and the like. For example, the method may be applied to display three-dimensional positions of human diseases, and the like. For example, the disease location obtaining method may be implemented in 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 notebook computer, a desktop computer, a network server, and the like.

For example, the disease location obtaining method is applicable to a computing device, and the computing device includes any electronic device with a computing function, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, a web server, and the like, and the disease location obtaining method can be loaded and executed, which is not limited in this respect by the embodiments of the present disclosure. For example, the computing device may include other forms of Processing units, storage units, and the like having data Processing capability and/or instruction execution capability, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), and an operating system, an application programming interface (e.g., opengl (open Graphics library), Metal, and the like) are also installed on the computing device, and the disease location obtaining method provided by the embodiment of the present disclosure is implemented by running codes 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 (e.g., VR helmet, VR glasses), and so on, which are not limited in this regard. For example, the display section may display an object to be displayed.

As shown in fig. 1, the disease position acquisition method includes the following steps S101 to S102.

In step S101, the element features of the disease data are acquired from the disease data.

In step S102, a three-dimensional position of the disease is acquired using a plurality of disease position models, which respectively correspond to different three-dimensional positions of the body, based on the element features of the disease data.

For step S101, for example, the disease data may be data related to the user 'S current disease, which contains a linguistic 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 the 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 chief complaints, the present medical history, the past medical history of a given medical record (or health data), and the diagnosis results given by a doctor. In the embodiment of the disclosure, the disease data may be handwritten data, and the data required in the disease data is acquired by an OCR or manual reading method; but also electronic disease data, and the required data can be derived through a management platform of the electronic disease data.

For example, a plurality of disease location models are partitioned based on different three-dimensional locations of the body by disease classification information. For example, the disease classification information may be ICD10, or other suitable disease classification library, which is not limited herein.

For example, the disease classification information may correspond to different disease names, and each disease classification information includes at least one name tag corresponding thereto, at which time, the element features of the disease data may be matched to the at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data. For example, if diabetes in the disease classification information corresponds to the label 1, the disease data is matched to the label 1 in the disease classification information if the element features of the disease data correspond to diabetes, so that the disease name corresponding to the disease data is obtained as diabetes.

For step S101, for example, the element features of the disease data may be represented by a vector representation or a word vector of each element of the disease data. For example, a probability of matching each disease in the disease classification information to the disease data may be obtained by using a neural network, and 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, so as to obtain the name of the disease.

It should be appreciated that the above method for obtaining the disease name according to the disease data is not limited thereto, and other effective methods may be used to obtain the disease name, which are not described herein again.

Furthermore, since the patient is assigned the correct disease classification information code or label at the time of the visit, the patient is only found the corresponding disease or 2D location for the visit, but cannot be precisely located to the 3D location of his disease. For this purpose, a plurality of disease location models, each corresponding to a different three-dimensional location of the body, may be acquired based on the disease classification information. For example, a disease location model may be derived based on ICD 10. For example, 13904 cases of diseases in ICD10 may be analyzed integrally, and ICD10 may be divided into a plurality of disease location models based on different human organs or tissues, thereby corresponding diseases to the 3D model. For example, ICD10 may be partitioned into multiple disease location models by classifiers (such as clustering, Support Vector Machines (SVMs), K-nearest neighbor algorithms) or neural networks, where different disease location models correspond to different human organs or tissues.

For example, the plurality of disease location models correspond to a plurality of human organs, and each model includes at least one label corresponding thereto. For example, the disease classification information may be divided into a plurality of disease location models for each organ such as heart, liver, spleen, lung, and kidney, and each disease location model may correspond to a label for the heart, liver, spleen, lung, and kidney. The label of each disease location model may also contain sub-labels. For example, a "heart" tag may contain the sub-tags "left atrium," "left ventricle," "right atrium," "right ventricle," and the like.

For example, the obtaining of the three-dimensional position of the disease using the plurality of disease position models based on the element features of the disease data in step S102 may include: corresponding the elemental features of the disease data to at least one tag in the plurality of disease location models to obtain a three-dimensional location of the disease.

For example, each tag may correspond to a code, and each sub-tag may also correspond to a code. For example, in the disease location model corresponding to ICD10, the code for the tag myasthenia gravis is G80.001, the code for the tag esophagus is I95, and the code for esophageal varices (sub-tags of esophagus) is I95.5. For example, in the case of disease data "no obvious stenosis of the lumen of the test tube, gastroscope can pass through, and varices of the lower esophageal veins" can be seen ", the disease data is matched to the code I95.5 of the disease position model according to the element feature" lower esophageal veins "in the disease data, so that the three-dimensional position of the disease can be obtained as" esophagus, esophageal veins ".

It should be appreciated that the above-described process of matching disease data to codes of a disease location model may be automated by a computer. For example, the technical effect of matching the disease data to the codes of the disease location model can also be achieved by a neural network or other methods, which are not described herein again.

After obtaining the three-dimensional position of the disease, the disease may be displayed on an organ position corresponding to the three-dimensional model of the human body in a display interface of the electronic device to enable human-computer interaction. For example, the three-dimensional location of the disease may be highlighted on the three-dimensional body structure or displayed on a display interface of the mobile device by the engine.

Further, for the disease data corresponding to the disease model, the neural network model may be reused to analyze the disease data to obtain a corrected three-dimensional position of the disease. Further, when the element feature of the disease data cannot correspond to at least one of the plurality of disease location models, the three-dimensional location of the disease can be obtained directly using a neural network based on the disease data.

Neural networks are a tool for large-scale, multi-parameter optimization. Depending on a large amount of training data, the deep neural network can learn hidden features which are difficult to summarize in the data, so that multiple complex tasks such as face detection, image semantic segmentation, text abstract extraction, object detection, motion tracking, natural language translation and the like are completed. Obtaining a vector representation of text by a word vector means representing each word in the text as a single vector, and generating a vector representation of the text by performing a high degree of summarization and abstraction. Neural network model based techniques may be used, for example, in medical disease location processing, whereby disease data is automatically processed.

An application scenario of the training method of the neural network model according to the 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 configured to receive an input 10, and the input 10 performs a feature extraction process to generate an output 30 based on the extracted features. In an embodiment of the present disclosure, the input 10 may be, for example, an image, a video, a natural language text, or a subject awaiting processing. The neural network model 20 performs image semantic segmentation, object detection, motion tracking, natural language translation, etc. on the input 10 to generate the output 30. The neural network model 20 may be embedded in a terminal device or a server to process the input.

In one 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 illustrating a disease location acquisition method according to another embodiment of the present disclosure. As shown in fig. 3, the disease position acquisition method includes the following steps S201 to S203.

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

In step S202, a three-dimensional position of the disease is acquired using a plurality of disease position models, which respectively correspond to different three-dimensional positions of the body, based on the element features of the disease data.

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

Since steps S201 to S202 are the same as the steps S101 to S102 described above with reference to fig. 1, detailed description thereof is omitted.

Next, in step S203, a 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 data including feature data including a plurality of disease information and identification tags including three-dimensional positions corresponding to the plurality of disease information, the plurality of disease information including disease data of a plurality of different diseases.

For example, the first Neural Network may be a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a Back learning Neural Network (BP), a linear Neural Network, or the like, which is not limited herein.

For example, the plurality of disease information may be derived from parsing one or more of the health assessment, the physical examination report, the health data, and the peripheral input, and the three-dimensional location corresponding to the plurality of disease information may be manually labeled more detailed location information. By training the first neural network using the plurality of disease information labeled 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 including a plurality of pieces of disease information and disease classification information, and an identification tag including three-dimensional positions corresponding to the plurality of pieces of disease information and three-dimensional position information corresponding to the disease classification information. It should be appreciated that a plurality of disease information and their corresponding three-dimensional positions are newly added in the training data in addition to the disease classification information and their corresponding three-dimensional positions, thereby increasing the kinds and ranges of the 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.

Further, when the element feature of the disease data cannot correspond to at least one of the disease location models, the three-dimensional location of the disease may be obtained directly using the above-described first neural network based on the disease data. Since the trained neural network has a prediction function, when the disease data cannot be associated with at least one of the disease location models, the three-dimensional location of the disease can be obtained by the neural network having the prediction function.

Likewise, after obtaining the three-dimensional location of the disease, a display may be made on the corresponding organ model to enable human-machine interaction. For example, the three-dimensional location of the disease may be highlighted on the three-dimensional body structure or displayed on a display interface of the mobile device by the engine.

In the above aspect of the present disclosure, a disease position obtaining method is provided, and specifically, the present disclosure creates a disease position model based on different three-dimensional positions, obtains a three-dimensional position of a disease by mapping obtained disease data into the disease position model, and analyzes the disease data by using a neural network to obtain a corrected three-dimensional position of the disease, thereby implementing a 3D display of the disease on a 3D model, so as to more accurately display disease information to a user, improve an interactive display effect of the disease and an abnormal index, and improve efficiency and user experience.

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

For example, the element features of some disease data may not be mapped into the existing disease location model, and then a second neural network may be trained by training data composed of other disease classification information or disease location model, and the three-dimensional location of the disease is obtained by using the second neural network.

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

For example, the second Neural Network may also be a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a Back learning Neural Network (BP), a linear Neural Network, and the like, which is not limited herein.

For example, the training data uses disease classification information different from the disease classification information, thereby increasing the types and ranges of disease locations. Thus, when the disease data cannot correspond to at least one of the plurality of disease location models, the three-dimensional location of the disease can be obtained directly through the second neural network.

In the above aspect of the present disclosure, a disease position obtaining method is provided, and in particular, the present disclosure creates a disease position model based on different three-dimensional positions, and then obtains a three-dimensional position of a disease by mapping obtained disease data into the disease position model, and when the disease data cannot correspond to at least one of the plurality of disease position 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, so that a 3D display of the disease on a 3D model is realized, so as to more accurately display disease information to a user, improve an interactive display effect of the disease and abnormal indexes, and improve efficiency and user experience.

After the disease position acquisition method according to the embodiment of the present disclosure is described above by fig. 1 to 3, a disease position acquisition example according to the embodiment of the present disclosure is described below based on fig. 4.

Fig. 4 shows a schematic diagram of a disease location acquisition example according to an embodiment of the present disclosure. As shown in fig. 4, first, disease data is acquired, which contains a linguistic description of the disease. The disease data may be obtained from one or more of health assessment 51, physical examination report 52, health data 53, and peripheral input 54. Next, the element features of the disease data are obtained through the disease data, and then the element features of the disease data are mapped to the disease classification information 55 to obtain the disease name corresponding to the disease data. Since the plurality of disease location models 56 are obtained through the disease classification information 55, the element features of the disease data may be mapped to the plurality of disease location models 56 to obtain the three-dimensional location of the disease, thereby implementing the 3D presentation. In addition, since the disease classification information includes various descriptions of diseases, information such as a disease or a problem 57 corresponding to the disease data, a system/part 58 described in corresponding abnormality information, and a health index 59 can be obtained by the disease location acquisition method based on the element features of the disease data.

The disease information display method provided by the application generally runs through electronic equipment to realize information interaction with a user, and after the electronic equipment is opened to enter a display interface (for example, a digital human body APP) for disease information display, a generally complete three-dimensional human body model is initially displayed on the display interface and is generally presented as an upright three-dimensional human body image in a natural extension state. In addition, the diseased organ is highlighted where the disease is detected for the patient to understand.

A display interface for human body information presentation according to an embodiment of the present disclosure is described below with reference to fig. 5 to 6. Fig. 5 is a schematic view of a display interface for displaying human body information according to an embodiment of the present disclosure. Fig. 6 is a display interface schematic diagram of a disease display according to an embodiment of the disclosure.

As shown in fig. 5, at least one curve surrounding the three-dimensional human body model is displayed on the display interface where the three-dimensional human body model is displayed, and a plurality of human body information labels are arranged on the curve, and in some possible embodiments, as shown in fig. 5, the curve includes at least one of the following: elliptical arc lines, spiral lines; the elliptic arc line surrounds the position of the chest cavity of the three-dimensional human body model; the spiral line extends from the foot part of the three-dimensional human body model to the head part of the three-dimensional human body model in a spiral way; the curve is stationary relative to the display interface. The curve can also be a geometric curve in other forms which can surround the three-dimensional human body model, specifically, the curve can be one, and also can be 2 curves which are displayed in parallel at a certain interval, or when the curve is a spiral line, the curve can be in a double-spiral structure, of course, the number can be even 3 or 4 curves, and the curve is specifically arranged according to actual needs.

Furthermore, the body information labels correspond to different parts of the three-dimensional body model, which may be determined according to a plurality of classifications, in some possible embodiments, the body information labels include at least one of: human organ type information, human system type information, or human parameter information; the distances between adjacent human body information labels on the curve are equal. For example, the human organ type information is classified by organs including heart, liver, spleen, lung, kidney, etc., by human system types including circulatory system, digestive system, respiratory system, reproductive system, immune system, etc., and also by parts including head, chest, upper and lower limbs, etc. Even further including other human parameter information related to human health, such as health history data, trauma data, etc. The human body information labels are arranged along the curve, so that the human body information labels on the curve are different in size for forming a three-dimensional effect, but the distance between the adjacent human body information labels on the curve is equal, for example, the human body information label at the middle position is larger, and the human body information label gradually becomes smaller along with the distance from the middle position.

Of course, the human body information tag can also be set to different states, and in some possible embodiments, the curve includes a preset highlight region and a preset non-highlight region. On the display interface that shows three-dimensional human model, show at least one curve around human model, be provided with a plurality of human information labels on the curve, specifically include: if the human body information label moves to the preset highlight area, amplifying and displaying the human body information label, and setting the human body information label to be in an activated state; and if the human body information label moves to the preset non-highlighted area, reducing and displaying the human body information label, and setting the human body information label in a non-activated state.

The human body information label can move on the curve, and on a specific position of the curve, for example, on a curve section close to the central axis of the three-dimensional human body model, the human body information label moves to the preset highlighting area, the human body information label is in an activated state, is in an amplified state in shape, can be selected and can be used for executing next operation, other areas are preset non-highlighting areas, the human body information label in the preset non-highlighting area is in an inactivated state, and is correspondingly displayed in a small size. Of course, in addition to the change in the shape by the enlargement or reduction, the characteristic of changing the brightness and color of the human body information tag in a changed state may be added.

After the position of the disease is detected by the method disclosed by the disclosure, as shown in fig. 6, the position corresponding to the three-dimensional human body model is highlighted, so that 3D display of the disease on the 3D model is realized, the disease information is displayed to the user more accurately, and the user experience is improved.

In the above, the disease location acquisition method according to the embodiment of the present invention is described with reference to the drawings. Hereinafter, a disease position acquisition apparatus according to an embodiment of the present disclosure will be described.

Fig. 7 is a functional block diagram illustrating a disease position acquisition apparatus according to an embodiment of the present disclosure. As shown in fig. 7, a disease position acquisition apparatus 1000 according to an embodiment of the present disclosure includes an element feature acquisition unit 1001 and a three-dimensional position acquisition unit 1002. The above-described modules may respectively perform the steps of the disease location acquisition method according to the embodiment of the present disclosure as described above with reference to fig. 1 to 3. Those skilled in the art understand that: these unit modules may be implemented in various ways by hardware alone, by software alone, or by a combination thereof, and the present disclosure is not limited to any one of them. These units may be implemented, for example, by a Central Processing Unit (CPU), image processor (GPU), Tensor Processor (TPU), Field Programmable Gate Array (FPGA) or other form of processing unit having data processing and/or instruction execution capabilities and corresponding computer instructions.

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

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

The three-dimensional position acquisition unit 1002 is configured to acquire a three-dimensional position of the disease using a plurality of disease position models, which respectively correspond to different three-dimensional positions of the body, based on the element features of the disease data.

For example, the plurality of disease location models are divided based on different three-dimensional locations of the body by disease classification information. For example, the disease classification information may be ICD10, or other suitable disease classification library, which is not limited herein. For example, the disease classification information may correspond to different disease names, and each disease classification information includes at least one name tag corresponding thereto, at which time, the element features of the disease data may be matched to the at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.

For example, a disease location model may be derived based on ICD 10. For example, the disease in ICD10 may be analyzed in an integrated manner, and ICD10 may be divided into a plurality of disease location models based on different human organs or tissues, so as to map the disease onto a 3D model.

For example, the three-dimensional position obtaining unit 1002 may correspond the element feature of the disease data to at least one tag in the plurality of disease position models to obtain the three-dimensional position of the disease.

After the three-dimensional position of the disease is obtained, a display can be made on the corresponding organ model to enable human-machine interaction. For example, the three-dimensional location of the disease may be highlighted on the three-dimensional body structure or displayed on a display interface of the mobile device by the engine.

Further, the three-dimensional position obtaining unit 1002 may analyze the disease data by using a neural network to obtain a corrected three-dimensional position of the disease.

For example, the three-dimensional position acquisition unit 1002 may obtain a 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 data including feature data including a plurality of disease information and identification tags including three-dimensional positions corresponding to the plurality of disease information, the plurality of disease information including disease data of a plurality of different diseases.

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

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 data including feature data including disease classification information different from the disease classification information and identification tags including three-dimensional positions corresponding to the disease classification information included in the feature data, the plurality of disease information including disease data of a plurality of different diseases.

In the above aspect of the present disclosure, a disease position acquisition method is proposed, and specifically, the present disclosure creates a disease position model based on different three-dimensional positions and then obtains a three-dimensional position of a disease by mapping obtained disease data into the disease position model. The three-dimensional position acquisition unit 1002 may analyze the disease data using a neural network to obtain a corrected three-dimensional position of the disease. When the disease data cannot correspond to at least one of the disease position models, the three-dimensional position obtaining unit 1002 may further directly obtain the three-dimensional position of the disease through the first neural network or the second neural network based on the disease data, thereby implementing 3D display of the disease on the 3D model, so as to more accurately display disease information to the user, improve an interactive display effect of the disease and an abnormal index, and improve efficiency and user experience.

A disease position acquisition apparatus according to an embodiment of the present disclosure will be described below with reference to fig. 8. Fig. 8 is a schematic diagram of a disease location acquisition device 2000 according to an embodiment of the present disclosure. Since the disease position acquiring apparatus of the present embodiment is the same as the details of the method described hereinabove with reference to fig. 1, a detailed description of the same is omitted here for the sake of simplicity.

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 by a bus system 230. For example, one or more computer program modules 221 are stored in memory 220. For example, the one or more computer program modules 221 include instructions for performing a 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 processor 210. For example, the bus system 230 may be a conventional serial, parallel communication bus, etc., and embodiments of the present disclosure are not limited in this respect.

For example, the processor 210 may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Graphics Processor (GPU), or other form of processing unit having data processing capabilities and/or instruction execution capabilities, may be a general purpose processor or a special purpose processor, and may control other components in the disease location acquisition device 2000 to perform desired functions.

Memory 220 may include one or more computer program products that 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), cache memory (cache), and/or the like. 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 executed by processor 210 to implement the functions of the disclosed embodiments (implemented by processor 210) and/or other desired functions, such as a disease location acquisition method, etc. Various applications and various data, such as elemental characteristics of disease data, disease location models, and various data used and/or generated by the applications, etc., may also be stored in the computer-readable storage medium.

It should be noted that, for clarity and conciseness of illustration, not all the constituent elements of the disease location acquiring apparatus 2000 are given in the embodiments of the present disclosure. To achieve the necessary functions of the disease location acquiring device 2000, those skilled in the art may provide and arrange other components not shown according to specific needs, and the embodiment of the present disclosure is not limited thereto.

For technical effects of the disease location acquiring apparatus 1000 and the disease location acquiring device 2000 in different embodiments, reference may be made to technical effects of the disease location acquiring method provided in the embodiments of the present disclosure, and details are not described here.

The disease position acquisition apparatus 1000 and the disease position acquisition device 2000 may be used for various appropriate electronic devices.

Fig. 9 is a schematic structural diagram of an electronic device according to at least one embodiment of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

For example, the electronic device includes a disease position acquiring apparatus 1000 and a display interface (e.g., the output apparatus 307 shown in fig. 9) provided in any embodiment of the present disclosure; 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 disease position obtaining method, and highlights an organ corresponding to the obtained three-dimensional position.

For example, as shown in fig. 9, in some examples, an electronic device 300 includes a processing apparatus (e.g., central processing unit, graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data necessary for the operation of the computer system are also stored. The processing device 301, the ROM302, and the RAM303 are connected thereto via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

For example, the following components may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 307 including devices such as Liquid Crystal Displays (LCDs), speakers, vibrators, and the like: storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309 including a network interface card such as a LAN card, modem, or the like. The communication means 309 may allow the electronic apparatus 300 to perform wireless or wired communication with other apparatuses 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. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 310 as necessary, so that a computer program read out therefrom is mounted into the storage device 309 as necessary. While fig. 9 illustrates an electronic device 300 that includes various means, it is to be understood that not all illustrated means are required to be implemented or included. More or fewer devices may be alternatively implemented 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 may be various types of interfaces, such as a USB interface, a lightning (lighting) interface, and the like. The communication device 309 may communicate with networks such as the internet, intranets, and/or wireless networks such as cellular telephone networks, wireless Local Area Networks (LANs), and/or Metropolitan Area Networks (MANs) and other devices via wireless communication. The wireless communication may use any of a number 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 over internet protocol (VoIP), Wi-MAX, protocols for email, instant messaging, and/or Short Message Service (SMS), or any other suitable communication protocol.

For example, the electronic device may be any device such as a mobile phone, a tablet computer, a notebook computer, an electronic book, a game machine, a television, a digital photo frame, and a navigator, and may also be any combination of electronic devices and hardware, which is not limited in this respect in the embodiments of the disclosure.

For example, the processes described above with reference to the flowcharts may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 308, or installed from the ROM 302. The above-described disease location acquisition function defined in the method of the embodiments of the present disclosure is performed when the computer program is executed by the processing device 301.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; the acquired internet protocol address indicates an edge node in the content distribution network.

Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The functions described herein above 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 a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

In various embodiments of the disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The 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, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

At least one embodiment of the present disclosure also provides a storage medium. Fig. 10 is a schematic diagram of a storage medium according to at least one embodiment of the present disclosure. For example, as shown in fig. 10, the storage medium 400 stores non-transitory computer readable instructions 401, which can perform the disease location acquisition method provided by any embodiment of the present disclosure when the non-transitory computer readable instructions are executed by a computer (including a processor).

For example, the storage medium can be any combination of one or more computer-readable storage media, such as one containing computer-readable program code that determines the number of display surfaces of the sub-model and another containing computer-readable program code that determines the location of the disease. For example, when the program code is read by a computer, the computer may execute the program code stored in the computer storage medium to perform a disease location acquisition method such as that provided by any of the embodiments 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, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a flash memory, or any combination of the above, as well as other suitable storage media.

The following points need to be explained:

(1) the drawings of the embodiments of the disclosure only relate to the structures related to the embodiments of the disclosure, and other structures can refer to the common design.

(2) Without conflict, embodiments of the present disclosure and features of the embodiments may be combined with each other to arrive at new embodiments.

The above description is intended to be exemplary of the present disclosure, and not to limit the scope of the present disclosure, which is defined by the claims appended hereto.

Claims

A disease location acquisition method, comprising:

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

acquiring a three-dimensional position of the disease using a plurality of disease position models based on the elemental characteristics of the disease data, wherein the plurality of disease position models respectively correspond to different three-dimensional positions of a body.
The method of claim 1, wherein the plurality of disease location models correspond to a plurality of human organs and each model contains at least one tag corresponding thereto,

the obtaining the three-dimensional position of the disease using a plurality of disease position models based on the element features of the disease data comprises:

corresponding the elemental features of the disease data to at least one tag in the plurality of disease location models to obtain a three-dimensional location of the disease.
The method of claim 2, further comprising:

obtaining, based on the disease data, a corrected three-dimensional position of the disease through a first neural network,

the first neural network is obtained through training of training data, the training data comprises feature data and identification labels, the feature data comprises a plurality of pieces of disease information, the identification labels comprise three-dimensional positions corresponding to the plurality of pieces of disease information, and the plurality of pieces of disease information comprise disease data of a plurality of different diseases.
The method of claim 2, further comprising:

obtaining a three-dimensional location of the disease through a second neural network based on the disease data when an elemental feature of the disease data fails to correspond to at least one of the plurality of disease location models,

wherein the second neural network is obtained by training with training data, the training data including feature data including disease classification information different from the disease classification information and an identification tag including a three-dimensional position corresponding to the disease classification information included in the feature data.
The method of any one of claims 1-4, wherein the plurality of disease location models are segmented based on different three-dimensional locations of the body by disease classification information.
The method of claim 5, wherein the disease classification information corresponds to different disease names, and each disease classification information contains at least one name tag corresponding thereto, the method further comprising:

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

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

after obtaining the three-dimensional location of the disease, highlighting the three-dimensional location on the three-dimensional body structure.
A disease location acquisition device comprising:

the element characteristic acquisition unit is used for acquiring element characteristics of the disease data through the disease data; and

a three-dimensional position acquisition unit configured to acquire a three-dimensional position of the disease using a plurality of disease position models based on an element feature of the disease data, wherein the plurality of disease position models respectively correspond to different three-dimensional positions of a body.
The apparatus according to claim 9, wherein the plurality of disease location models correspond to a plurality of human organs, and each model contains at least one label corresponding thereto, and the three-dimensional location obtaining unit corresponds an element feature of the disease data to at least one label in the plurality of disease location models to obtain a three-dimensional location of the disease.
The apparatus of claim 10, further comprising:

a three-dimensional position acquisition unit acquires a corrected three-dimensional position of the disease through a first neural network based on the disease data,

the first neural network is obtained through training of training data, the training data comprises feature data and identification labels, the feature data comprises a plurality of pieces of disease information, the identification labels comprise three-dimensional positions corresponding to the plurality of pieces of disease information, and the plurality of pieces of disease information comprise disease data of a plurality of different diseases.
The apparatus of claim 10, further comprising:

the three-dimensional position obtaining unit obtains a three-dimensional position of a disease through a second neural network based on disease data when an element feature of the disease data fails to correspond to at least one of the plurality of disease position models,

wherein the second neural network is obtained by training with training data, the training data including feature data including disease classification information different from the disease classification information and an identification tag including a three-dimensional position corresponding to the disease classification information included in the feature data.
The apparatus of any one of claims 9-12, wherein the plurality of disease location models are partitioned based on different three-dimensional locations of the body by disease classification information.
The apparatus 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 thereto, and the three-dimensional position obtaining unit matches the element feature of the disease data to the at least one name tag of the disease classification information to obtain the disease name corresponding to the disease data.
The apparatus of claim 14, wherein,

the three-dimensional position acquisition unit highlights a three-dimensional position of the disease on a three-dimensional human body structure after acquiring the three-dimensional position.
A disease position acquisition apparatus comprising:

a processor; and

a memory having stored therein computer-readable instructions,

wherein the computer readable instructions, when executed by the processor, perform a disease location acquisition method, the method comprising:

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

acquiring a three-dimensional position of the disease using a plurality of disease position models based on the elemental characteristics of the disease data, wherein the plurality of disease position models respectively correspond to different three-dimensional positions of a body.
A computer-readable storage medium storing a computer-readable program for causing a computer to execute the disease location acquisition method according to any one of claims 1 to 8.
An electronic device, comprising: the disease location acquisition device and display interface of any one of claims 9-15;

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 obtaining method according to any one of claims 1 to 9, and highlights an organ corresponding to the obtained three-dimensional position.