KR20210094964A - Method for providing disease information of subject and apparatus using same - Google Patents

Abstract

Disclosed is a method for providing disease information that provides disease information of a subject using a pre-learned artificial neural network. The method for providing the disease information may comprise: a step of obtaining subject information; a step of determining disease information of the subject based on the subject information through the pre-learned artificial neural network; and a step of providing the disease information to an external entity.

Description

Method for providing disease information of a subject and device using the same

The present invention relates to a method for diagnosing a disease in a subject and an apparatus using the same.

A method of diagnosing a disease of a subject using a computerized tomography (CT) image or a magnetic resonance imaging (MRI) image requires relatively large equipment for imaging. Therefore, a system based on an artificial neural network trained to diagnose a disease of a subject based on such a medical image is subject to restrictions of place and time.

The demand for a diagnosis system using an artificial neural network for diagnosing the condition of a subject more quickly and accurately in a rapidly changing environment is increasing.

An object of the present invention is to provide a means for diagnosing a disease of a subject based on subject information that can be obtained relatively easily with respect to the subject other than a medical image. Another object of the present invention is to provide a means for performing a diagnosis on a subject based on a combination of various types of subject information that can be variably obtained according to circumstances.

The characteristic configuration of the present invention for achieving the object of the present invention as described above and for realizing the characteristic effects of the present invention to be described later is as follows.

The present invention comprises the steps of obtaining subject information; determining disease information of the subject based on the subject information through the pre-learned artificial neural network; and providing the disease information to an external entity.

The present invention provides a means for diagnosing a disease of a subject based on various information obtainable from the subject in addition to a medical image, thereby adaptive to various situations and has the effect of performing high-accuracy diagnosis. Through this, even when the subject is in a moving situation such as an ambulance, it is possible to provide a subject diagnosis means suitable for the corresponding situation. It will be readily understood by those skilled in the art that the application examples herein are not limited to emergency situations.

According to the present invention, there is a potential effect of innovating the workflow in the medical field by ultimately improving the diagnosis accuracy of the medical staff.

And, in the present invention, the biometric information acquisition means conventionally used in hospitals can be utilized as it is, so it goes without saying that the method of the present invention is not dependent on a specific type of signal or platform.

The accompanying drawings for use in the description of the embodiments of the present invention are only a part of the embodiments of the present invention, and for those of ordinary skill in the art to which the present invention pertains, based on these drawings, without efforts to reach a separate invention Other drawings may be obtained.
1 is a diagram illustrating a method for providing disease information according to an embodiment.
2A to 2B are diagrams illustrating an example of an artificial neural network used in a method for providing disease information according to an embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The following detailed description of the present invention refers to the accompanying drawings, which show by way of illustration a specific embodiment in which the present invention may be practiced, in order to clarify the objects, technical solutions and advantages of the present invention. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention.

The term "image" or "image data" as used throughout the present description and claims refers to multidimensional data composed of discrete image elements (e.g., pixels in a two-dimensional image, voxels in a three-dimensional image). refers to For example, "imaging" means X-ray imaging, (cone-beam) computed tomography, magnetic resonance imaging (MRI), ultrasound or any other medical imaging known in the art. It may be a medical image of a subject, ie, a subject, collected by the system. Also, the image may be provided in a non-medical context, for example, a remote sensing system, an electron microscopy, and the like.

Throughout the description and claims of the present invention, 'image' refers to a viewable image (eg, displayed on a video screen) or an image (eg, a file corresponding to a pixel output of a CT, MRI detector, etc.) A term that refers to digital representations.

And throughout the detailed description and claims of the present invention, 'learning' or 'learning' is a term referring to performing machine learning through computing according to a procedure, and a mental action such as human educational activity. It will be understood by those of ordinary skill in the art that this is not intended to be a reference.

And throughout this description and claims, the word 'comprise' and variations thereof are not intended to exclude other technical features, additions, components or steps. In addition, 'one' or 'an' is used to mean more than one, and 'another' is limited to at least a second or more.

Other objects, advantages and characteristics of the present invention will appear to a person skilled in the art, in part from this description, and in part from practice of the present invention. The following illustrations and drawings are provided by way of illustration and are not intended to limit the invention. Therefore, the details disclosed herein with respect to a specific structure or function are not to be construed in a limiting sense, but merely representative should be interpreted as basic data.

Moreover, the invention encompasses all possible combinations of the embodiments indicated herein. It should be understood that various embodiments of the present invention are different but need not be mutually exclusive. For example, certain shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in relation to one embodiment. In addition, it should be understood that the position or arrangement of individual components in each disclosed embodiment may be changed without departing from the spirit and scope of the present invention. Accordingly, the detailed description set forth below is not intended to be taken in a limiting sense, and the scope of the invention, if properly described, is limited only by the appended claims, along with all scope equivalents to those claimed. Like reference numerals in the drawings refer to the same or similar functions throughout the various aspects.

Unless otherwise indicated herein or otherwise clearly contradicted by context, items referred to in the singular encompass the plural unless the context requires otherwise. In addition, in describing the present invention, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present invention, the detailed description thereof will be omitted.

As used herein, the term "subject" may mean any object (eg, a patient) to be diagnosed with disease information.

Hereinafter, in order to enable those skilled in the art to easily practice the present invention, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically illustrating a method for providing disease information performed by a computing device according to the present invention.

Computing devices according to one embodiment are specifically typical computer hardware (eg, computer processors, memory, storage, input and output devices, devices that may include other components of conventional computing devices, such as routers, switches, etc.) Electronic communication devices, electronic information storage systems such as network-attached storage (NAS) and storage area networks (SANs)) and computer software (i.e., that enable the computing device to function in a particular way) instructions) to achieve the desired system performance.

The communication unit of such a computing device may transmit/receive a request and a response to/from another computing device that interworks. As an example, such a request and a response may be made by the same transmission control protocol (TCP) session, but limited thereto. However, it may be transmitted and received as, for example, a user datagram protocol (UDP) datagram. In addition, in a broad sense, the communication unit may include a keyboard, a mouse, other external input devices, printers, displays, and other external output devices for receiving commands or instructions.

In addition, the processor of the computing device includes a micro processing unit (MPU), a central processing unit (CPU), a graphics processing unit (GPU) or a tensor processing unit (TPU), a cache memory, a data bus, and the like. It may include hardware configuration. In addition, it may further include an operating system, a software configuration of an application for performing a specific purpose.

A person skilled in the art will understand that the communication unit or processor included in the computing device may be implemented by interworking between the communication unit and the processor.

The computing device may diagnose a disease of the subject through a pre-learned artificial neural network stored in a built-in memory or an interlocked database. A pre-trained artificial neural network may be generated based on a TensorFlow-based deep learning algorithm. Pre-trained artificial neural networks include, for example, Deep Neural Network (DNN), Convolutional Neural Network (CNN), Deep Convolution Neural Network (DCNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief (DBN). Network), SSD (Single Shot Detector), YOLO (You Only Look Once), etc. may be used as will be understood by those skilled in the art. The types of artificial neural networks that can be used in the present invention are not limited to the presented examples, and can be learned to extract a feature vector from input data based on the labeled learning data, and to diagnose the subject's disease based on this. It will be understood by those skilled in the art that an artificial neural network may be included.

According to an embodiment, the computing device may acquire subject information of the subject to be diagnosed in step 110 .

The subject information may include demographic information, symptom information, vital sign information, blood test result information, electrocardiogram information, EEG information, cerebral blood flow information, and basic image information (eg, X-ray) of the subject. . For example, the cerebral blood flow information may be measured by a device measuring cerebral blood flow in the form of a helmet, but the cerebral blood flow measuring device is not limited to this example.

Demographic information is information that reflects the human characteristics of the subject obtained through the preparation of questionnaires, etc., and may include, for example, age, sex, height, weight, obesity, etc., but is not limited thereto. It will be understood by those of ordinary skill in the art that it may include any information that reflects the characteristics of the subject.

The symptom information may include information about any externally visible symptoms, such as vomiting, rash, and the like.

The vital sign information may include information on blood pressure, pulse rate, respiration rate, body temperature, oxygen saturation, and state of consciousness, and may include any indicator indicating vitality.

In operation 120 , the computing device according to an embodiment may determine disease information of the subject based on the acquired subject information through the pre-trained artificial neural network. More specifically, the pre-trained artificial neural network may diagnose the subject's disease by determining the subject's disease information based on an input corresponding to at least one of the aforementioned subject information. For example, the disease information may include any disease information, and may additionally be extended to a subject's death probability, a subject's severity, and the like.

The computing device may determine disease information of the subject based on single subject information or may determine disease information of the subject based on a combination of a plurality of subject information. For example, by inputting pulse rate information of the subject to a pre-trained artificial neural network, disease information on the presence or absence of heart disease in the subject is determined, or by inputting blood pressure information and obesity degree information of the subject as inputs. It may be learned to determine disease information regarding the presence or absence of a disease, and the computing device may determine disease information using such a pre-trained artificial neural network. The manner in which disease information is generated based on any combination of subject information is not limited to the presented example, and the pre-trained artificial neural network is a combination of different subject information (eg, demographic information and vital signs). By inputting a combination of information or a combination of symptom information, vital sign information, and electrocardiogram information), the same disease information (eg, the presence or absence of heart disease) can be learned to be determined. It will be readily understood by those skilled in the art that the combination of inputs is not limited to the examples presented.

As described above, the present invention may provide a means for performing disease diagnosis adaptive to various environments by determining the same disease information based on various types of inputs.

In addition, the present invention may determine a plurality of disease information based on the same subject information. For example, the computing device may determine disease information regarding the presence or absence of heart disease and cerebral hemorrhage of the subject by inputting a combination of obesity degree and electrocardiogram information of the subject into a pre-trained artificial neural network.

In addition, the pre-learned artificial neural network uses source information (e.g., electrocardiogram, EEG, cerebral blood flow, etc.) of biological signals (e.g., electrocardiogram, brain wave, cerebral blood flow, etc.) , may be trained to determine disease information, and the computing device may determine disease information based on the learned artificial neural network. Through this, the present invention can determine disease information based on source information without additional processing of biosignals such as electrocardiogram, and can provide a means for more rapidly determining disease information.

According to an embodiment, the computing device may output the disease information determined in step 130 through a display or provide it to an external entity.

According to an embodiment, the computing device may further include a process of performing optimization according to a target disease or a diagnosis situation.

For example, the computing device may further perform a process of determining an optimal combination for diagnosis among currently obtainable combinations of subject information according to a diagnosis target disease, or adjusting a weight of combination target subject information. there is. In this way, it is possible to provide a pre-process for obtaining optimal diagnostic results for individual diseases in the current situation. This optimization process can be implemented through a separate module (which can also be implemented through an artificial neural network) from a pre-trained artificial neural network that determines disease information, or can be implemented in combination with a pre-trained artificial neural network that determines disease information. can

The present invention can provide a means for performing simple and accurate disease diagnosis according to various individual circumstances. Through this, the present invention can provide a more effective system for treating an emergency subject by obtaining a diagnosis result at a point in time before a more detailed diagnosis is made (eg, in an ambulance transport situation, etc.).

2A to 2B are diagrams illustrating an example of an artificial neural network used in a method for providing disease information according to an embodiment.

Referring to FIG. 2A , the artificial neural network is based on a first artificial neural network that receives demographic information and ECG features as inputs and a second artificial neural network that receives pre-processed ECG source information as inputs. It is possible to predict predetermined disease information. That is, the artificial neural network may determine disease information based on a combination of demographic information and electrocardiogram information.

Referring to FIG. 2B , the artificial neural network may be trained to determine disease information corresponding to anemia based on input electrocardiogram information (ECG) information and demographic information corresponding to age and gender. According to an embodiment, it will be understood by those skilled in the art that the artificial neural network can be trained to predict various diseases other than anemia based on electrocardiogram information and demographic information (age or gender).

Referring to FIG. 2C , the artificial neural network obtains disease information corresponding to pulmonary hypertension based on electrocardiogram information and other information (eg, demographic information such as age, biometric information such as heart rate, etc.) It can be learned in advance to decide.

It will be understood by those skilled in the art that the artificial neural network presented through the drawings is only an example of the present invention, and that the present invention can be pre-trained to determine various disease information based on various subject information.

Based on the description of the above embodiments, those skilled in the art will recognize that the method and/or processes of the present invention, and the steps thereof, may be implemented in hardware, software, or any combination of hardware and software suitable for a particular application. point can be clearly understood. The hardware may include general purpose computers and/or dedicated computing devices or specific computing devices or special features or components of specific computing devices. The processes may be realized by one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices, having internal and/or external memory. Additionally, or alternatively, the processes may be configured to process an application specific integrated circuit (ASIC), programmable gate array, programmable array logic (PAL) or electronic signals. It may be implemented with any other device or combination of devices. Moreover, the objects of the technical solution of the present invention or parts contributing to the prior arts may be implemented in the form of program instructions that can be executed through various computer components and recorded in a machine-readable recording medium. The machine-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the machine-readable recording medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software. Examples of the machine-readable recording medium include a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM, DVD, Blu-ray, and a magneto-optical medium such as a floppy disk (magneto-optical media), and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include any one of the devices described above, as well as heterogeneous combinations of processors, processor architectures or combinations of different hardware and software, or stored and compiled or interpreted for execution on a machine capable of executing any other program instructions. can be created using a structured programming language such as C, an object-oriented programming language such as C++, or This includes not only bytecode, but also high-level language code that can be executed by a computer using an interpreter or the like.

Accordingly, in one aspect according to the present invention, when the method and combinations thereof described above are performed by one or more computing devices, the methods and combinations of methods may be implemented as executable code for performing respective steps. In another aspect, the method may be implemented as systems that perform the steps, the methods may be distributed in various ways across devices or all functions may be integrated into one dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such sequential combinations and combinations are intended to fall within the scope of this disclosure.

For example, the hardware device may be configured to operate as one or more software modules to perform processing in accordance with the present invention, and vice versa. The hardware device may include a processor such as an MPU, CPU, GPU, TPU coupled with a memory such as ROM/RAM for storing program instructions and configured to execute instructions stored in the memory, and an external device and signal It may include a communication unit that can send and receive. In addition, the hardware device may include a keyboard, a mouse, and other external input devices for receiving commands written by developers.

In the above, the present invention has been described with specific matters such as specific components and limited embodiments and drawings, but these are only provided to help a more general understanding of the present invention, and the present invention is not limited to the above embodiments, Those of ordinary skill in the art to which the present invention pertains can make various modifications and variations from these descriptions.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and not only the claims described below, but also all modifications equivalently or equivalently to the claims described below belong to the scope of the spirit of the present invention. will do it

Such equivalent or equivalent modifications shall include, for example, logically equivalent methods capable of producing the same results as practiced by the methods according to the present invention, the spirit and scope of the present invention. should not be limited by the above examples, and should be understood in the broadest sense permitted by law.

Claims (1)

In the disease information providing method for providing disease information of a subject using a pre-learned artificial neural network,
obtaining subject information;
determining disease information of the subject based on the subject information through the pre-learned artificial neural network; and
providing the disease information to an external entity;
A method of providing disease information, including.

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