KR20230115629A - Apparatus for refining liguid and diagnosis system including the same - Google Patents

Abstract

A liquid purification device is disclosed. The liquid purification device includes a substrate, a loading part formed on the substrate and accommodating a first liquid, and a second liquid in which the concentration of at least one substance is reduced by reducing the concentration of at least one component included in the first liquid. The obtained filter unit mixes the second liquid and the reaction material for detecting the target material, and among the plurality of materials included in the second liquid, a first material having a predetermined reaction with the reactive material and a predetermined reaction with the reactive material are not made. It includes a reaction unit for obtaining a third liquid containing a second material, and a separation unit for separating the first material and the second material.

Description

Liquid purification device and diagnostic system including the same {APPARATUS FOR REFINING LIGUID AND DIAGNOSIS SYSTEM INCLUDING THE SAME}

An embodiment relates to a liquid purification device.

An embodiment relates to a medical diagnostic device.

An embodiment relates to a medical diagnostic system.

Research on a technique for diagnosing a health condition or disease of a subject based on liquid (eg, blood, urine, etc.) collected from the subject is being actively conducted. For example, the technology may obtain a diagnosis result of the subject by analyzing spectrum information of the liquid. Typically, the technology obtains a diagnosis result based on spectral information of blood in which an antigen-antibody reaction has been performed.

However, there are various components including blood cell components in addition to antigens in blood, and due to this, spectral information includes errors. Therefore, in order to obtain an accurate diagnosis result, a technique for preventing or compensating for an error in spectrum information is required.

One technical problem to be solved by the present invention is to provide a liquid purification device capable of improving the accuracy of diagnostic results for a subject.

Another technical problem to be solved by the present invention is to provide a diagnostic device capable of compensating for an error in spectral information of a target material.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below.

According to an exemplary embodiment of the present disclosure for solving the above technical problem, in a liquid purification device, a substrate; a loading part formed on the substrate and accommodating a first liquid; a filter unit reducing the concentration of at least one component included in the first liquid to obtain a second liquid having a reduced concentration of the at least one substance; By mixing the second liquid and a reactant for detecting the target material, a first material having a predetermined reaction with the reactant among a plurality of materials included in the second liquid and a predetermined reaction with the reactant are not made. a reaction unit for obtaining a third liquid containing a second material; and a separator for separating the first material and the second material.

The first liquid may include blood in an undiluted state, and the at least one component may include a blood cell component.

The predetermined reaction may include an antigen-antibody reaction.

The liquid separation device may further include an anti-coagulation unit adding an anti-coagulant to the first liquid.

The reaction unit may include a reaction material storage unit for storing the reaction material, and a zigzag-shaped mixing channel for increasing a mixing ratio of the second liquid and the reaction material.

The reaction material may be combined with metal nanoparticles combined with Raman active molecules.

The liquid purifying device may include: a first chamber formed on the upper portion of the substrate and connected to the loading part to store the first liquid; a second chamber formed on the upper portion of the substrate and connected to the filter unit to store the second liquid; and a third chamber formed on the upper portion of the substrate and connected to the separator to store the first material.

The liquid purification apparatus may further include an enrichment unit that performs a concentration process on the third liquid, and the concentration process may include at least one of drying, heating, and baking.

The liquid purifying device further includes a pumping unit for moving the first liquid, the second liquid, and the third liquid, and the pumping unit includes at least one of a pneumatic pump, a vibration pump, a mechanical pump, and a capillary pump. can include

The reaction unit includes a first reaction material storage unit for storing a first reaction material for detecting a first target, and a first mixing channel for increasing a mixing ratio of the second liquid and the first reaction material. A second reaction comprising a reaction unit, a second reaction material storage unit for storing a second reaction material for detecting a second target, and a second mixing channel for increasing a mixing ratio of the second liquid and the second reaction material. It may include a unit, a first channel for transferring the second liquid to the first reaction unit, and a second channel for transferring the second liquid to the second reaction unit.

The first channel and the second channel may have a structure branching from an output terminal of the filter unit.

The separation unit is connected to the output end of the first reaction unit, and is a first separation unit for separating a third material having an antigen-antibody reaction with the first reaction material from among a plurality of materials included in the third liquid and other materials. and a second separator connected to an output end of the second reaction unit and configured to separate a fourth material having an antigen-antibody reaction with the second reactant among a plurality of materials included in the third liquid and other materials. can include

The filter unit may include a Lateral Cavity Acoustic Transducer (LCAT) protruding outward of the filter channel through which the first liquid flows.

The separation unit separates the first material and the second material according to molecular weight based on sound waves, and includes a first outlet channel for moving the first material and a second outlet channel for moving the second material. can include

According to an exemplary embodiment of the present disclosure for solving the above technical problem, in a diagnostic system including a liquid purification device, a liquid information acquisition device, and a diagnosis device, the liquid purification device includes a substrate; a loading unit formed on the substrate and accommodating the first liquid collected from the test subject; a filter unit reducing the concentration of at least one component included in the first liquid to obtain a second liquid having a reduced concentration of the at least one substance; By mixing the second liquid and a reactant for detecting the target material, a first material having a predetermined reaction with the reactant among a plurality of materials included in the second liquid and a predetermined reaction with the reactant are not made. a reaction unit for obtaining a third liquid containing a second material; and a separator configured to separate the first material and the second material, wherein the liquid information obtaining device obtains a Raman signal of the first material by irradiating light to the first material, The diagnosis apparatus may be provided with a diagnosis system that obtains a diagnosis result for the subject based on the Raman signal.

The solutions to the problems of the present disclosure are not limited to the above-described solutions, and solutions not mentioned will be clearly understood by those skilled in the art from this specification and the accompanying drawings. You will be able to.

According to various embodiments of the present disclosure as described above, an error in spectrum information about a liquid collected from a test subject can be reduced. The diagnosis device may compensate for errors in spectrum information. Accordingly, the accuracy of the diagnosis result for the subject may be improved.

In addition, effects that can be obtained or predicted due to the embodiments of the present disclosure will be directly or implicitly disclosed in the detailed description of the embodiments of the present disclosure. For example, various effects predicted according to an embodiment of the present disclosure will be disclosed within the detailed description to be described later.

Other aspects, advantages and salient features of the present disclosure will become apparent to those skilled in the art from the following detailed description, which discloses various embodiments of the present invention in conjunction with the accompanying drawings.

Aspects, features, and advantages of specific embodiments of the present disclosure will become more apparent from the following description with reference to the accompanying drawings.
1 is a block diagram showing the configuration of a diagnosis system according to an embodiment of the present disclosure.
2 is a block diagram showing the configuration of a liquid purification device according to an embodiment of the present disclosure.
3 is a top view of a liquid purifying apparatus according to an embodiment of the present disclosure.
4 is a bottom view of a liquid purifying apparatus according to an embodiment of the present disclosure.
5 is a top view of a liquid purifying device according to an embodiment of the present disclosure.
6 is a diagram illustrating a filter unit according to an embodiment of the present disclosure.
7 is a view showing a separator according to an embodiment of the present disclosure.
8 is a diagram for explaining a liquid information obtaining method of a liquid information obtaining device according to an embodiment of the present disclosure.
9 is a block diagram illustrating a configuration of a diagnosis device according to an exemplary embodiment of the present disclosure.
10 is a diagram of information about a spectrum according to an embodiment of the present disclosure.
11 is a diagram for explaining a method of obtaining concentration information according to a first embodiment of the present disclosure.
12 is a diagram for explaining a method for learning a first neural network model according to an embodiment of the present disclosure.
13 is a diagram for explaining a method of obtaining concentration information according to a second embodiment of the present disclosure.
14 is a diagram for explaining a method of obtaining concentration information according to a third embodiment of the present disclosure.
15 is a diagram for explaining a method of obtaining concentration information according to a fourth embodiment of the present disclosure.
16 is a diagram for explaining a method of obtaining concentration information according to an embodiment of the present disclosure.
17 is a diagram for explaining a method of obtaining concentration information according to an embodiment of the present disclosure.
18 is a diagram for explaining a method of obtaining concentration information according to an embodiment of the present disclosure.
19 is a flowchart illustrating a control method of a diagnosis apparatus according to an embodiment of the present disclosure.

Terms used in this specification will be briefly described, and the present disclosure will be described in detail.

The terms used in the embodiments of the present disclosure have been selected from general terms that are currently widely used as much as possible while considering the functions in the present disclosure, but they may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technologies, and the like. . In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the disclosure. Therefore, terms used in the present disclosure should be defined based on the meaning of the term and the general content of the present disclosure, not simply the name of the term.

Embodiments of the present disclosure may apply various transformations and may have various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the scope to specific embodiments, and should be understood to include all transformations, equivalents, and substitutes included in the spirit and scope of technology disclosed. In describing the embodiments, if it is determined that a detailed description of a related known technology may obscure the subject matter, the detailed description will be omitted.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. Terms are only used to distinguish one component from another.

Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprise" or "consist of" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other It should be understood that the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Hereinafter, with reference to the accompanying drawings, embodiments of the present disclosure will be described in detail so that those skilled in the art can easily carry out the present disclosure. However, the present disclosure may be implemented in many different forms and is not limited to the embodiments described herein. And in order to clearly describe the present disclosure in the drawings, parts irrelevant to the description are omitted, and similar reference numerals are attached to similar parts throughout the specification.

1 is a block diagram showing the configuration of a diagnosis system according to an embodiment of the present disclosure.

Referring to FIG. 1 , a diagnosis system 1000 may include a liquid purification device 100 , a liquid information acquisition device 200 and a diagnosis device 300 .

The liquid purification device 100 is a device for separating liquid. For example, the liquid purification device 100 may separate blood cell components and plasma components of blood. Alternatively, the liquid purifying device 100 may separate the first blood to which the first reactant is added and the second blood to which the second reactant is added. The liquid may include at least one of urine, saliva, semen, sweat, tears, and cerebrospinal fluid of the subject. The subject may be a human or an animal.

The liquid purification device 100 may be implemented as a biochip.

The liquid information acquisition device 200 is a device for obtaining liquid information, which is information about liquid. The liquid information may include spectrum information corresponding to the liquid. The spectrum information may indicate intensity for each wavelength (or each frequency). The spectrum information (or referred to as spectrum information) may include a Raman signal.

The liquid information obtaining device 200 may obtain spectrum information based on Raman scattering generated when light passes through a certain medium. For example, the liquid information acquisition device 200 may irradiate a laser beam to the liquid acquired by the liquid purification device 100 and obtain a laser beam scattered from microparticles in the liquid. The liquid purifying apparatus 100 may obtain spectrum information corresponding to the liquid based on the scattered laser beam. Spectral information, that is, the intensity of the wavelength and wavelength band of the Raman signal may vary depending on the components of the microparticles.

The liquid information obtaining device 200 may include an intensity corresponding to only a specific frequency among spectrum information. For example, when Raman active molecules are coupled to microparticles, the liquid information obtaining device 200 may obtain a peak value of a Raman signal at a frequency corresponding to the Raman active molecules.

The liquid information acquisition device 200 may include a light source for radiating light to the liquid. The light source may emit a laser beam for inducing Raman scattering from microparticles in the liquid.

The liquid information acquisition device 200 may include an optical system. The optical system may include a component for transferring light irradiated from a light source to the liquid and sensing light scattered from microparticles in the liquid. For example, the optical system may include at least one of a filter, a mirror, a lens, a slit, a grating, and a sensor. The sensor may sense light scattered from microparticles in the liquid.

The liquid information acquisition device 200 may be implemented as a spectrometer.

The diagnostic device 300 is a component for acquiring various analysis data related to liquid. The diagnosis device 300 may be a medical diagnosis device. For example, the diagnosis apparatus 300 may acquire identification information and concentration information about a plurality of substances constituting the liquid obtained by the liquid information acquisition apparatus 200 . Also, the diagnosis apparatus 300 may obtain concentration ratios of a plurality of substances included in the liquid.

The diagnostic device 300 may obtain various diagnostic results. For example, the diagnosis apparatus 300 may obtain a diagnosis result including the presence or absence of a health condition or disease corresponding to the subject. The diagnosis result may include identification information about a disease that the subject is expected to have and probability information that the subject has the disease.

The diagnosis device 300 may be implemented as a server or a user terminal device.

2 is a block diagram showing the configuration of a liquid purification device according to an embodiment of the present disclosure.

Referring to FIG. 2 , the liquid purification device 100 includes a loading unit 110, an anti-coagulation unit 120, a filter unit 130, a reaction unit 140, a concentration unit 160 and a separation unit 150. can include

The loading unit 110 may include an inlet through which liquid is injected and a channel for moving the liquid. The loading unit 110 may receive liquid through the inlet and transfer the received liquid to other components of the liquid purifying device 100 . For example, the loading unit 110 may receive the first liquid and deliver a portion of the first liquid to the anti-coagulation unit 120 . Alternatively, the loading unit 110 may transfer some of the first liquid to the chamber.

The anti-coagulation unit 120 is a component for performing anti-coagulation treatment on liquid. The anticoagulant unit 120 may include a chamber in which an anticoagulant is stored. For example, the anticoagulation unit 120 may be located on a path along which the first liquid received by the loading unit 110 moves to the filter unit 130 . The anticoagulation unit 120 may include an inlet for receiving the first liquid and an outlet for outputting the anticoagulated first liquid. Alternatively, the anticoagulant unit 120 may include a pipe for injecting the anticoagulant into a channel through which the first liquid flows into the filter unit 130 .

The filter unit 130 may accommodate the anticoagulant-treated liquid. The filter unit 130 may reduce the concentration of at least one component included in the received liquid. For example, at least one component may include a blood cell component. Blood cell components may include red blood cells, white blood cells and platelets.

The filter unit 130 may include a filter channel through which liquid flows and a fluid structure for filtering at least one component included in the liquid. The filter channel may accommodate the first liquid anticoagulated by the anticoagulation unit 120 . The fluid structure may include a protrusion protruding outward of the filter channel and including an air bladder. For example, the fluid structure may include a Lateral Cavity Acoustic Transducer (LCAT).

The filter unit 130 may filter at least one component included in the liquid based on vibration transmitted to the filter channel and the air bag. When vibration is transmitted to the filter channel and the air bladder, a liquid vortex may be generated at the interface between the filter channel and the air bladder. The swirl of liquid may trap at least one component contained in the liquid passing through the filter channels. Accordingly, the filter unit 130 may obtain a liquid having a reduced concentration of at least one component. For example, the filter unit 130 may receive the anticoagulated first liquid and filter blood cell components included in the anticoagulated first liquid. Accordingly, the filter unit 130 may obtain the second liquid having a reduced concentration of blood cell components. The second liquid may be plasma or serum.

The reaction unit 140 may induce a predetermined reaction between a target material included in the liquid and a reaction material for detecting the target material. A target substance may include an antigen. A reactive substance may include an antibody. The reactant may be bound to a metal (eg, gold) nanoparticle to which a Raman active molecule (or Raman reporter) is bound. A predetermined reaction means a chemical reaction, and one example may include an antigen-antibody reaction. A plurality of metal nanoparticles may bind to antigens through antibodies. Accordingly, a lump of metal nanoparticles in which a plurality of metal nanoparticles are bound around the antigen may be formed.

The reaction unit 140 may obtain a third liquid by reacting the second liquid with the reactant. The second liquid may include a first material that undergoes a predetermined reaction with the reactant and a second material that does not undergo a predetermined reaction. The third liquid may include a first material having a predetermined reaction with the reactant and a second material not having a predetermined reaction with the reactant.

The reaction unit 140 may include a reaction material storage unit for storing a reaction material. The reaction unit 140 may include a mixing channel for increasing a mixing rate of the second liquid and the reactant. For example, the mixed channel may have a zigzag shape.

Separation unit 150 is a component for separating substances included in the liquid. For example, the separator 150 may separate the first material and the second material included in the third liquid. Separator 150 may separate the first material and the second material based on various methods. For example, the separation unit 150 may separate the first material and the second material according to molecular weight based on a surface acoustic wave (SAW). Separator 150 may include a surface acoustic wave filter.

Separator 150 may include a channel for moving a plurality of separated materials. For example, the separator 150 may include a first outlet channel for moving the first material and a second outlet channel for moving the second material.

The enrichment unit 160 is a component for increasing the concentration of a target material included in the liquid by performing a concentration process on the liquid. Concentration treatment may include at least one of drying, heating, and baking. Alternatively, the enrichment unit 160 may include a filter (eg, a membrane filter) or a pipe through which only substances having a size smaller than a predetermined size pass. At this time, the enrichment unit 160 may pass materials smaller in size than the first metal nanoparticles combined with the target material through the reaction material, but may not pass the first metal nanoparticles. Accordingly, the enrichment unit 160 may obtain a liquid having an increased concentration of the first metal nanoparticles. That is, the enrichment unit 160 may obtain a liquid having an increased concentration of the target material.

The enrichment unit 160 may obtain a fourth liquid including the first material separated by the separation unit 150 . The fourth liquid may include first metal nanoparticles bound to the first material through antibodies. The fourth liquid may include a third material (eg, protein) having a molecular weight greater than that of the first metal nanoparticle or a second material that is not accurately separated by the separator 150 . Alternatively, the fourth liquid may include second metal nanoparticles that are not combined with the first material. The enrichment unit 160 may obtain a fifth liquid having an improved concentration of the first metal nanoparticles by passing at least a portion of the materials other than the first metal nanoparticles and not passing the first metal nanoparticles. Accordingly, the enrichment unit 160 may obtain a fifth liquid having an increased concentration of the first material compared to the fourth liquid.

3 is a top view of a liquid purifying apparatus according to an embodiment of the present disclosure. 4 is a bottom view of a liquid purifying apparatus according to an embodiment of the present disclosure.

Referring to FIG. 3 , the liquid purifying device 100 includes a substrate 10, a loading unit 110, an anti-coagulation unit 120, a filter unit 130, a reaction unit 140, a separation unit 150, a concentration It may include a portion 160 and chambers 31 , 32 , 33 , 34 , and 35 .

The loading part 110 may be formed on top of the substrate 10 . The loading unit 110 may contain the first liquid. The loading unit 110 may transfer some of the first liquid to the first chamber 31 . The first chamber 31 may store the delivered first liquid.

The loading unit 110 may deliver the first liquid to the anti-coagulation unit 120 . The anti-coagulation unit 120 may perform anti-coagulation treatment on the first liquid. The anti-coagulation unit 120 may deliver the anti-coagulation-treated first liquid to the filter unit 130 .

The filter unit 130 includes a filter channel 131 through which the first liquid flows and a fluid structure 132 protruding outward from the filter channel 131 and including an air bag, the filter channel 131 and the fluid structure 132 It may include a first vibration generating unit 133 that provides sound waves.

The filter unit 130 may filter at least one first component (eg, blood cell component) included in the first liquid based on the sound wave generated by the first vibration generating unit 133 . The filter channel 131 and the fluid structure 132 may vibrate by sound waves generated by the first vibration generator 133 . As the filter channel 131 and the fluid structure 132 vibrate, a vortex of the first liquid may be generated at the interface between the filter channel 131 and the air bag. At least one first component may be entrapped in the vortex of the first liquid created. Accordingly, the filter unit 130 may obtain a second liquid having a reduced concentration of at least one first component compared to the first liquid.

The filter unit 130 may move the first liquid and the second liquid based on the air bag included in the fluid structure 132 . The air bladder may be repeatedly compressed and expanded based on sound waves generated by the first vibration generating unit 133 . The air bladder can pump the first liquid and the second liquid. The filter unit 130 may output the second liquid. The fluid structure 132 and the first vibration generating unit 133 may constitute a Lateral Cavity Acoustic Transducer (LCAT).

Referring to FIG. 4 , the first vibration generating unit 133 may be located below the substrate 10 . The first vibration generating unit 133 may include a plurality of electrodes 1331 and 1332 . The plurality of electrodes 1331 and 1332 may be disposed to face each other. The plurality of electrodes 1331 and 1332 may be piezoelectric electrodes.

Referring back to FIG. 3 , the filter unit 130 may transfer some of the second liquid to the second chamber 32 . The second liquid may be blood obtained by removing blood components from the first liquid.

The filter unit 130 may deliver the second liquid to the reaction unit 140 . The reaction unit 140 may include a reaction material storage unit 141 that stores a reaction material bound to the metal nanoparticles. Raman active molecules may be bound to the metal nanoparticles.

The second liquid may be mixed with the reactant while passing through the reactant storage unit 141 . An antigen-antibody reaction may occur between the target material (or the first material) and the reaction material included in the second liquid. In this process, a lump of metal nanoparticles in which at least one metal nanoparticle is aggregated around the antigen may be formed.

The reaction unit 140 may include a zigzag-shaped mixing channel 142 for increasing a mixing ratio of the second liquid and the reactant. While passing through the mixing channel 142, an antigen-antibody reaction between the target material and the reactive material may occur. Accordingly, the number of lumps of metal nanoparticles may be increased. Although not shown, the reaction unit 140 may include a Lateral Cavity Acoustic Transducer (LCAT) for increasing a mixing ratio of the second liquid and the reactant.

The reaction unit 140 may obtain a third liquid based on the second liquid. The third liquid may include a first material that forms a lump of metal nanoparticles through a reaction with the reactant, and a plurality of second materials that do not form a lump of metal nanoparticles by not reacting with the reactant. The reaction unit 140 may deliver the third liquid to the separation unit 150 .

The separation unit 150 may include a second vibration generating unit 151 that generates sound waves, a first outlet channel 152 and a second outlet channel 153 . The second vibration generating unit 151 may be provided on the rear surface of the substrate 10 . The second vibration generating unit 151 may be an IDT electrode. The IDT electrode may include a plurality of bars 1511 and 1512 facing each other and a plurality of fingers 1513 protruding from the plurality of bars 1511 and 1512 . The sound wave may be a surface acoustic wave (SAW).

Separation unit 150 may separate a plurality of substances included in the third liquid according to molecular weight based on sound waves. The separation unit 150 may generate vibration around the channel through which the liquid flows and separate a plurality of materials based on the vibration. For example, the separation unit 150 may separate a material bound to the target material (eg, metal nanoparticles bound to an antibody) and a material not bound to the target material. The separation unit 150 may obtain a fourth liquid by filtering a material not combined with the target material. The fourth liquid may be a liquid in which the concentration of a substance not combined with the target material in the third liquid is reduced.

The separator 150 may deliver the fourth liquid to the third chamber 33 through the first outlet channel 152 . The separator 150 may transfer a material that is not combined with the target material to the fourth chamber 34 through the second outlet channel 153 .

The enrichment unit 160 may perform a concentration process on the fourth liquid accommodated in the third chamber 33 . The enrichment unit 160 may not pass a lump of metal nanoparticles including a target material among a plurality of materials included in the fourth liquid, but pass other materials. Accordingly, the enrichment unit 160 may obtain a fifth liquid having an improved concentration of the metal nanoparticles or the target material.

Meanwhile, a plurality of biomarkers (or target substances) may be required to obtain a diagnostic result for a subject. Hereinafter, a liquid purification device for purifying a liquid containing a plurality of biomarkers will be described.

5 is a top view of a liquid purifying device according to an embodiment of the present disclosure.

Referring to FIG. 5 , the liquid purification device 500 includes a loading unit 110, an anti-coagulation unit 120, a filter unit 130, a first reaction unit 540, a second reaction unit 542, a first It may include a separation unit 551 , a second separation unit 552 , a first enrichment unit 561 and a second enrichment unit 562 . Meanwhile, since the loading unit 110, the anticoagulation unit 120, and the filter unit 130 have been described with reference to FIGS. 3 and 4, duplicate descriptions will be omitted. In addition, the basic operations of the first reaction unit 541 and the second reaction unit 542 can be clearly understood through the reaction unit 140 of FIG. 3, and the first separation unit 551 and the second separation unit ( 552) can be clearly understood through the separation unit 150 of FIG. 3, and the basic operations of the first enrichment unit 561 and the second enrichment unit 562 refer to the enrichment unit 560 of FIG. can be clearly understood through Therefore, a description overlapping with that of FIG. 3 will be omitted.

The first reaction unit 541 may include a first reaction material storage unit 543 and a first reaction channel 544 . The first reactant storage unit 543 may store a first reactant material for detecting a first target material. The first reaction channel 544 may increase a mixing ratio of the first target material and the first reaction material. The first reaction channel 544 may induce an antigen-antibody reaction between the first target material and the first reaction material.

The second reaction unit 542 may include a second reaction material storage unit 543 and a first reaction channel 544 . The second reactant storage unit 545 may store a second reactant material for detecting the second target material. The second reaction channel 546 may increase a mixing ratio of the second target material and the second reactant material. The second reaction channel 546 may induce an antigen-antibody reaction between the second target material and the second reaction material.

The first liquid that has passed through the first reaction unit 541 contains a lump of first metal nanoparticles formed by a reaction between a first target material and a first reactant material and a plurality of first materials not combined with the first target material. can include The second liquid that has passed through the second reaction unit 542 contains a mass of second metal nanoparticles formed by the reaction between the second target material and the second reactant material and a plurality of second materials that are not combined with the second target material. can include

The first separator 551 may separate the lump of first metal nanoparticles and the plurality of first materials included in the first liquid. The first separation unit 551 may transfer the lump of first metal nanoparticles to the third chamber 33 and transfer a plurality of first materials to the fourth chamber 34 . The first enrichment unit 561 may perform a concentration process on the lump of first metal nanoparticles accommodated in the third chamber 33 .

The second separation unit 552 may separate a lump of second metal nanoparticles and a plurality of second materials included in the second liquid. The second separator 552 may transfer the second metal nanoparticle lump to the sixth chamber 36 and transfer a plurality of second materials to the seventh chamber 37 . The second enrichment unit 562 may perform a concentration process on the lump of second metal nanoparticles accommodated in the sixth chamber 36 . Through the concentration process of the second enrichment unit 562 , at least one material may be separated from the second metal nanoparticles and accommodated in the eighth chamber 38 .

6 is a diagram illustrating a filter unit according to an embodiment of the present disclosure.

Referring to FIG. 6 , the filter unit 130 may include a first filter channel 131 and a fluid structure 132 protruding outward from the first filter channel 131 . The fluid structure 132 may be formed to form an obtuse angle with the direction (x) of the first filter channel 131 . That is, the angle A1 between the direction (x) of the first filter channel 131 and the protrusion direction (y) of the fluid structure 132 may be 90 degrees to 180 degrees.

The first liquid 60 flowing through the first filter channel 131 may include a first material 61 and a second material 62 . The first material 61 may be one of blood cell components, and the second material 62 may be one of blood plasma components.

The fluid structure 132 may include an air bag 1321 through which the first liquid 60 does not flow. Air bladder 1321 can function as a pump. Based on the first sound wave generated by the first vibration generator 133, the air bag 1321 may repeat contraction and expansion. Accordingly, the air bag 1321 may pump the first liquid 60 in the direction (x) of the first filter channel 131 .

Fluid structure 132 may function as a filter. When a first sound wave having a frequency corresponding to the size of the first material 61 is generated from the first vibration generator 133, the periphery of the interface 1322 of the first filter channel 131 and the air bag 1321 A vortex 63 of the first liquid 60 may be generated in the region. The first material 61 may be trapped in the vortex 63 . Accordingly, the fluid structure 132 may filter the first material 61 .

7 is a view showing a separator according to an embodiment of the present disclosure.

Referring to FIG. 7 , the separation unit 150 may include a channel 70 through which liquid 71 flows and a second vibration generating unit 151 positioned on a side of the channel 70 to generate sound waves. The separation unit 150 may include a first outlet channel 152 and a second outlet channel 153 diverging from the channel 70 . In FIG. 7 , the separator 150 is illustrated as having a symmetrical structure, but is not limited thereto and may have an asymmetrical structure. Separator 150 may include three or more outlet channels.

The liquid 71 may include a target material 72, a reactant 73 reacting with the target material 72, and metal nanoparticles 75 to which the reactant 73 and the Raman active molecule 74 are combined. there is. A lump 76 of metal nanoparticles may be formed by an antigen-antibody reaction between the target material 72 and the reactant material 73 . The target material 72 may react with a plurality of reactants 73 . The metal nanoparticle mass 76 may include a plurality of metal nanoparticles.

Separation unit 150 may separate a plurality of substances included in the liquid 71 according to molecular weight based on sound waves. A plurality of materials included in the liquid 71 may be aligned according to molecular weight when they meet with sound waves. At this time, the plurality of materials may move to form an acute angle with the direction of the channel 70 .

The separator 150 may transfer the first material having a molecular weight greater than a predetermined value to the third chamber 33 through the first outlet channel 152 . The separation unit 150 may transfer the second material having a molecular weight smaller than a predetermined value to the fourth chamber 34 through the second outlet channel 153 . The first material may include a lump 76 of metal nanoparticles formed by an antigen-antibody reaction between the target material 72 and the reactant material 73 . The second material may include various materials other than the target material 72 or a material to which the target material 72 is combined. For example, the second material may include a reactive material 73 that does not react with the target material 72 . Alternatively, the second material may include metal nanoparticles and Raman active molecules bound to the reactive material 73 that has not reacted with the target material 72 .

8 is a diagram for explaining a liquid information obtaining method of a liquid information obtaining device according to an embodiment of the present disclosure.

Referring to FIG. 8 , a substrate 82 on which a liquid 81 is applied may be provided. The liquid 81 may be one of liquids stored in the plurality of chambers 31 , 32 , and 33 of the liquid purifying device 100 . The light source 210 of the liquid information acquisition device 200 may irradiate light to the liquid 81 applied to the substrate 82 . The sensor 220 may sense light 84 scattered from microparticles included in the liquid 81 . The liquid information obtaining device 200 may acquire spectrum information of the liquid 81 based on the scattered light 84 .

A metal pattern 83 may be formed on the substrate 82 for surface enhanced scattering. Scattered light 84 sensed by the sensor 220 may be amplified by the metal pattern 83 .

Liquid information acquisition methods for a plurality of liquids collected from the plurality of chambers 31, 32, and 33 may be different. The first liquid stored in the first chamber 31 and the second liquid stored in the second chamber 32 may not include metal nanoparticles for surface enhanced scattering. Accordingly, when liquid information on the first liquid and the second liquid is obtained, the first liquid and the second liquid may be applied to the substrate 82 on which the metal pattern 83 is formed. The third liquid stored in the third chamber 33 may include metal nanoparticles. The third liquid may be applied to the substrate 82 on which the metal pattern 83 is not formed. Alternatively, the third liquid may be applied to the substrate 82 on which the metal pattern 83 is formed to further enhance the surface enhanced scattering effect.

9 is a block diagram illustrating a configuration of a diagnosis device according to an exemplary embodiment of the present disclosure. Referring to FIG. 9 , the diagnosis device 300 may include a communication interface 310 , a memory 320 and a processor 330 .

The communication interface 310 includes at least one communication circuit and can perform communication with various types of external devices or external servers. For example, the communication interface 310 may receive information about a spectrum corresponding to blood of a subject from an external device.

The communication interface 310 is a Wi-Fi communication module, cellular communication module, 3G (3rd generation) mobile communication module, 4G (4th generation) mobile communication module, 4th generation LTE (Long Term Evolution) communication module, 5G (5th generation) mobile communication It may include at least one of a module and wired Ethernet.

The memory 320 may store an operating system (OS) for controlling overall operations of the components of the diagnostic device 300 and commands or data related to the components of the diagnostic device 300 . The memory 320 may store information about a plurality of neural network models. The information on the plurality of neural network models may include information on parameters corresponding to each of the plurality of neural network models and learning data for learning the plurality of neural network models. The training data may include label data (or ground truth) representing concentrations corresponding to spectrum intensities. The training data may include label data indicating a diagnosis result corresponding to the intensity or concentration of the spectrum. The memory 320 may be implemented as non-volatile memory (ex: hard disk, solid state drive (SSD), flash memory) or volatile memory.

The processor 330 may be electrically connected to the memory 320 to control overall functions and operations of the diagnostic device 300 . The processor 330 may receive spectrum information (or information on a spectrum) of the liquid collected from the test object from an external device through the communication interface 310 . Alternatively, the processor 330 may obtain spectrum information through an input interface. The information on the spectrum may include a numerical value representing the spectrum, a vector, and at least one peak value included in the spectrum. A frequency at which a peak value of the spectrum appears may correspond to a Raman active molecule bound to a metal nanoparticle bound to a target material.

The processor 330 may obtain information about the spectrum of a plurality of liquids. For example, the processor 330 may obtain information on a spectrum corresponding to the first liquid collected from the test subject. The first liquid may be the whole blood of the subject. The processor 330 may obtain information about a spectrum corresponding to the second liquid in which the concentration of blood cell components is reduced in the first liquid. The processor 330 obtains information on a spectrum corresponding to a third liquid in which a first reaction material (eg, a first antibody) for detecting a first target material (eg, a first antigen) is added to the second liquid. can do. The processor 330 obtains information on a spectrum corresponding to a fourth liquid in which a second reaction material (eg, a second antibody) for detecting a second target material (eg, a second antigen) is added to the second liquid. can do.

The processor 330 may obtain concentration information of the target material based on the spectrum information. The concentration information of the target material may include a numerical value representing the concentration of the target material, a feature vector corresponding to the concentration of the target material, and a peak value of a spectrum corresponding to the concentration of the target material.

The processor 330 may obtain first concentration information of the first target material based on the information on the first spectrum and the information on the second spectrum. Here, the information on the first spectrum may be information on a first liquid that includes a first target material and does not include a first reactant material that reacts with the first target material. The information on the second spectrum may be information on the second liquid in which the first reactant is added to the first liquid. For example, the first liquid may be a liquid accommodated in the second chamber 32 of FIG. 3 . The second liquid may be a liquid accommodated in the third chamber 33 of FIG. 3 . Compared to the first liquid, the second liquid may have a smaller content of components other than the first target material (eg, proteins other than the first target material). The remaining components may act as noise when acquiring spectrum information. Accordingly, the processor 330 uses information on the spectrum of each of the first liquid from which the remaining components are not removed and the second liquid from which at least some of the remaining components are removed, so that the first concentration information with reduced noise due to the remaining components is used. can be obtained.

The processor 330 may obtain the concentration information based on a lookup table in which the information on the second spectrum, the peak value of the spectrum, and the concentration information are matched. The lookup table may be previously stored in the memory 320 . For example, the processor 330 may identify density information corresponding to a peak value of the second spectrum from the lookup table.

The processor 330 may correct the acquired density information based on the lookup table. For example, the processor 330 may obtain a coefficient for correcting the density information by inputting information on the first spectrum to the first neural network model. The processor 330 may acquire first concentration information of the first target material by correcting the concentration information based on the coefficient.

The processor 330 may obtain first concentration information of the first target material by inputting information about the first spectrum and information about the second spectrum to the second neural network model. The second neural network model may be a model learned to obtain concentration information based on spectrum information.

The processor 330 may obtain a feature vector by inputting information on the second spectrum to the second neural network model. The processor 330 may acquire first density information by inputting information about the feature vector and the first spectrum to the third neural network model. The third neural network model may be a model learned to obtain corrected concentration information based on spectrum information and concentration information. The feature vector may be obtained from an output terminal of a layer (eg, fully connected layer) included in the second neural network model.

The processor 330 may obtain a first feature vector by inputting information on the first spectrum to a second neural network model. The processor 330 may obtain a second feature vector by inputting information on the second spectrum to the second neural network model. The processor 330 may acquire the first density information by inputting the first feature vector and the second feature vector to the fourth neural network model. The fourth neural network model may be a model learned to obtain corrected density information based on the density information.

The processor 330 may obtain diagnosis information based on spectrum information and concentration information. For example, the processor 330 may obtain diagnostic information by inputting information on the first spectrum and information on the first concentration to the fifth neural network model. Alternatively, the processor 330 may obtain diagnostic information by inputting information on the first spectrum, first concentration information of the first target material, and second concentration information of the second target material to the fifth neural network model. The fifth neural network model may be a model learned to obtain diagnostic information based on spectrum information and concentration information.

The processor 330 may obtain diagnostic information based on information about spectrums corresponding to a plurality of liquids. For example, the processor 330 may obtain diagnosis information by inputting information on the first spectrum and information on the second spectrum to the sixth neural network model. The sixth neural network model may be a model learned to obtain diagnostic information based on spectrum information.

Meanwhile, functions related to artificial intelligence according to the present disclosure are operated through the processor 330 and the memory 320. Processor 330 may be composed of one or a plurality of processors. In this case, the one or more processors may be a general-purpose processor such as a CPU, an AP, or a digital signal processor (DSP), a graphics-only processor such as a GPU or a vision processing unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control input data to be processed according to predefined operating rules or artificial intelligence models stored in the memory 320 . Alternatively, when one or more processors are processors dedicated to artificial intelligence, the processors dedicated to artificial intelligence may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

A predefined action rule or an artificial intelligence model is characterized in that it is created through learning. Here, being made through learning means that a basic artificial intelligence model is learned using a plurality of learning data by a learning algorithm, so that a predefined action rule or artificial intelligence model set to perform a desired characteristic (or purpose) is created. means burden. Such learning may be performed in the device itself in which artificial intelligence according to the present disclosure is performed, or through a separate server and/or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the above examples.

AI models can be created through learning. An artificial intelligence model may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and a neural network operation is performed through an operation between an operation result of a previous layer and a plurality of weight values. A plurality of weights possessed by a plurality of neural network layers may be optimized by a learning result of an artificial intelligence model. For example, a plurality of weights may be updated so that a loss value or a cost value obtained from an artificial intelligence model is reduced or minimized during a learning process.

The artificial neural network may include a deep neural network (DNN), for example, a Convolutional Neural Network (CNN), a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), a Generative Adversarial Network (GAN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), or Deep Q-Networks, but is not limited to the above examples.

10 is a diagram of information about a spectrum according to an embodiment of the present disclosure.

Referring to FIG. 10 , spectrum information S (or spectrum information) may mean the intensity of a Raman signal according to a wavenumber. The spectrum information S may be data in the form of vectors in which the wave number and the intensity of the Raman signal are matched to each other. In various embodiments described below, spectrum information S may be input to a neural network model. In this case, the spectral information S input to the neural network model may include a plurality of intensities respectively corresponding to a plurality of wavenumbers. Alternatively, the spectrum information S may mean a single intensity corresponding to a specific wave number (eg, a wave number corresponding to a Raman active molecule).

The spectrum information (S) may be normalized data. For example, a first Raman signal may be obtained by irradiating a specific liquid with a laser beam of a first intensity, and a second Raman signal may be obtained by irradiating a laser beam of a second intensity to a specific liquid. In this case, the diagnostic apparatus 300 may adjust the intensity of the first Raman signal or the second Raman signal so that the area of the first Raman signal and the area of the second Raman signal become the same.

11 is a diagram for explaining a method of obtaining concentration information according to a first embodiment of the present disclosure.

Referring to FIG. 11 , the diagnostic apparatus 300 may acquire information S1 on a first spectrum corresponding to the first liquid and information S2 on a second spectrum corresponding to the second liquid. The first liquid may include a first target material (eg, antigen A) and may not include a first reaction material (eg, antibody A′) corresponding to the first target material. The second liquid is a liquid in which a first reactant is added to the first liquid, and may include a first target material combined with the first reactant.

The diagnosis apparatus 300 may obtain first concentration information 303 of the first target material based on the information S2 of the second spectrum and the look-up table 302 . The look-up table 302 may include peak values and concentrations of spectra that match each other. The diagnosis apparatus 300 may obtain a peak value of the second spectrum based on the information S2 on the second spectrum. For example, the diagnosis apparatus 300 may identify, as a peak value, an intensity corresponding to a predetermined wave number in the information S2 on the second spectrum. Here, the predetermined wave number may correspond to the Raman active molecules bound to the metal nanoparticles bound to the first reactant material.

The diagnosis apparatus 300 may obtain the first concentration information 303 based on the peak value obtained from the information S2 on the second spectrum and the look-up table 302 . For example, the diagnostic apparatus 300 may obtain the first concentration information 303 by identifying the concentration corresponding to the peak value obtained from the information S2 on the second spectrum in the look-up table 302. there is.

Meanwhile, the second liquid may include various other components (eg, proteins, etc.) other than the first target material. Accordingly, the first concentration information 303 obtained based on the look-up table 302 may include an error.

The diagnosis apparatus 300 may correct the first concentration information 303 based on the information S1 on the first spectrum. The diagnosis apparatus 300 may obtain a coefficient 301 for correcting the concentration information by inputting the information S1 on the first spectrum to the first neural network model M1. In this case, the information S1 on the first spectrum may include a plurality of intensities according to a plurality of wavenumbers. The diagnosis apparatus 300 may correct the first concentration information 303 based on the coefficient 301 . For example, the diagnosis apparatus 300 may obtain the second concentration information 304 by multiplying the first concentration information 303 by the coefficient 301 .

12 is a diagram for explaining a method for learning a first neural network model according to an embodiment of the present disclosure.

Referring to FIG. 12 , training data of the first neural network model M1 may include a plurality of first spectrum information S1, a plurality of second spectrum information S2, and a first ground truth GT1. . The first ground truth GT1 may include density information of a target material corresponding to spectrum information corresponding to a liquid including the target material. The first ground truth GT1 may be previously stored in the memory 320 of the diagnosis device 300 .

The diagnosis apparatus 300 may train the first neural network model M1 based on the first ground truth GT1. The diagnosis apparatus 300 may obtain the second concentration information 304 based on the first spectrum information S1 and the second spectrum information S2. The method for acquiring the second concentration information 304 has been described with reference to FIG. 11, so a detailed description thereof will be omitted. The diagnosis apparatus 300 may obtain a first loss value based on the second concentration information 304 and the concentration information included in the first ground truth GT1. The diagnosis apparatus 300 may update parameters (eg, weights) of the first neural network model M1 until the first loss value becomes smaller than a preset value. That is, the diagnosis apparatus 300 may learn the first neural network model M1 based on backpropagation.

Meanwhile, in FIG. 12 , the diagnosis apparatus 300 learns the first neural network model M1 based on supervised learning as an example, but this is only one embodiment, and the diagnosis apparatus 300 is unsupervised learning (eg, , reinforcement learning), the first neural network model M1 may be trained.

13 is a diagram for explaining a method of obtaining concentration information according to a second embodiment of the present disclosure.

Referring to FIG. 13 , the diagnostic apparatus 300 inputs information S1 on the first spectrum and information S2 on the second spectrum to the second neural network model M2 to obtain concentration information (S1) of the first target material. 311) can be obtained. For example, the information S2 on the second spectrum may mean a single intensity corresponding to a specific wave number (eg, a wave number corresponding to a Raman active molecule).

The first liquid and the second liquid may commonly include other components (eg, blood cell components) in addition to the first target material. The second neural network model M2 may obtain the concentration information 311 based not only on the spectral information corresponding to one liquid, but also on the basis of spectral information corresponding to a plurality of liquids including other components in common. . Accordingly, accuracy of the concentration information 311 may be improved.

The diagnosis apparatus 300 may train the second neural network model M2. The training data of the second neural network model M2 may include a plurality of first spectrum information S1, a plurality of second spectrum information S2, and a second ground truth. The second ground truth may include density information corresponding to the spectrum information. The diagnosis apparatus 300 may obtain a second loss value based on the density information 311 and the density information included in the second ground truth. The diagnosis apparatus 300 may update parameters (eg, weights) of the second neural network model M2 until the second loss value becomes smaller than the preset value.

14 is a diagram for explaining a method of obtaining concentration information according to a third embodiment of the present disclosure.

Referring to FIG. 14 , the diagnosis apparatus 300 may obtain a feature vector 321 by inputting information S2 on the second spectrum to the second neural network model M2. The feature vector 321 may be output through the fully connected layer (FC) of the second neural network model (M2). The diagnosis apparatus 300 may acquire the concentration information 322 by inputting the information S1 of the first spectrum and the feature vector 321 to the third neural network model M3.

The diagnosis apparatus 300 may train the third neural network model M3. The training data of the third neural network model M3 may include a plurality of first spectrum information S1, a plurality of second spectrum information S2, and a third ground truth. The third ground truth may include spectrum information and density information corresponding to the feature vector. The diagnosis apparatus 300 may obtain a third loss value based on the density information 322 and the density information included in the third ground truth. The diagnosis apparatus 300 may update parameters (eg, weights) of the third neural network model M3 until the third loss value is smaller than the predetermined value.

The diagnosis apparatus 300 may also train the second neural network model M2 while learning the third neural network model M3. The diagnosis apparatus 300 may update parameters of the second neural network model M2 until the third loss value becomes smaller than the predetermined value. Alternatively, the diagnosis apparatus 300 may train the third neural network model M3 after completing the learning of the second neural network model M2. At this time, the parameters of the second neural network model M2 may be frozen while the parameters of the third neural network model M3 are updated.

15 is a diagram for explaining a method of obtaining concentration information according to a fourth embodiment of the present disclosure.

Referring to FIG. 15 , the diagnosis apparatus 300 may obtain a first feature vector 331 by inputting information S1 on the first spectrum to the second neural network model M2. The diagnosis apparatus 300 may acquire the second feature vector 332 by inputting the information S2 on the second spectrum to the second neural network model M2. The diagnosis apparatus 300 may obtain the concentration information 333 by inputting the first feature vector 331 and the second feature vector 332 to the fourth neural network model M4.

The diagnosis apparatus 300 may train the fourth neural network model M4. The training data of the fourth neural network model M4 may include a plurality of first spectrum information S1, a plurality of second spectrum information S2, and a fourth ground truth. The fourth ground truth may include density information corresponding to the feature vector. The diagnosis apparatus 300 may obtain a fourth loss value based on the density information 333 and the density information included in the fourth ground truth. The diagnosis apparatus 300 may update parameters (eg, weights) of the fourth neural network model M4 until the fourth loss value becomes smaller than a preset value.

The diagnosis apparatus 300 may also train the second neural network model M2 while learning the fourth neural network model M4. The diagnosis apparatus 300 may update parameters of the second neural network model M2 until the fourth loss value becomes smaller than the predetermined value. Alternatively, the diagnosis apparatus 300 may train the fourth neural network model M4 after completing the learning of the second neural network model M2. At this time, the parameters of the second neural network model M2 may be frozen while the parameters of the fourth neural network model M4 are updated.

16 is a diagram for explaining a method of obtaining concentration information according to an embodiment of the present disclosure.

Referring to FIG. 16 , the diagnosis apparatus 300 obtains diagnosis information 342 by inputting information S1 of the first spectrum and concentration information 341 of the first target material into a fifth neural network model M5. can do. For example, the diagnosis information 342 may include information about a disease expected to be possessed by the subject. In this case, the first target material may be a biomarker of a disease.

The diagnosis apparatus 300 may train the fifth neural network model M5. The training data of the fifth neural network model M5 may include a plurality of first spectrum information S1 , a plurality of concentration information 341 , and a fifth ground truth. The plurality of concentration information 341 may be obtained according to various embodiments described above. The fifth ground truth may include diagnostic information corresponding to spectrum information and concentration information.

The diagnosis apparatus 300 may obtain a fifth loss value based on the diagnosis information 342 and the diagnosis information included in the fifth ground truth. The diagnosis apparatus 300 may update parameters (eg, weights) of the fifth neural network model M5 until the fifth loss value becomes smaller than the predetermined value. The diagnosis apparatus 300 may train the neural network model outputting the concentration information 341 while learning the fifth neural network model M5. For example, when the concentration information 341 is acquired by the second neural network model M2, the diagnosis apparatus 300 performs the second neural network model M2 until the fifth loss value becomes smaller than a preset value. Parameters can be updated. Alternatively, the diagnosis apparatus 300 may train the fifth neural network model M5 after completing the learning of the neural network model outputting the concentration information 341 .

Meanwhile, disease diagnosis may be performed based on one biomarker, but may also be performed based on a plurality of biomarkers.

17 is a diagram for explaining a method of obtaining concentration information according to an embodiment of the present disclosure.

Referring to FIG. 17 , the diagnosis apparatus 300 transmits information S1 on a first spectrum, first concentration information 351 of a first target material, and second concentration information 352 of a second target material to a fifth The diagnostic information 353 may be acquired by inputting it to the neural network model M5. For example, the diagnosis information 353 may include a diagnosis result for Alzheimer's disease. The first target material may be tau protein, and the second target material may be beta-amyloid.

18 is a diagram for explaining a method of obtaining concentration information according to an embodiment of the present disclosure.

Referring to FIG. 18 , the diagnosis apparatus 300 acquires diagnosis information 361 by inputting information S1 on the first spectrum and information S2 on the second spectrum to the sixth neural network model M6. can The information S1 on the first spectrum may correspond to the first liquid including the first target material. The information S2 on the second spectrum may correspond to a second liquid including a first target material and a first reaction material combined with the first target material.

The diagnosis apparatus 300 may train the sixth neural network model M6. The training data of the sixth neural network model M6 may include a plurality of first spectrum information S1, a plurality of second spectrum information S2, and a sixth ground truth. The sixth ground truth may include diagnostic information corresponding to the spectrum information. The diagnosis apparatus 300 may obtain a sixth loss value based on the diagnosis information 361 and the diagnosis information included in the sixth ground truth. The diagnosis apparatus 300 may update parameters (eg, weights) of the sixth neural network model M6 until the sixth loss value becomes smaller than a preset value.

Preprocessing may be performed on input data input to the plurality of neural network models M1, M2, M3, M4, M5, and M6 according to the present disclosure. For example, concatenation of a plurality of input data may be performed.

Some of the plurality of neural network models M1, M2, M3, M4, M5, and M6 may be integrated.

19 is a flowchart illustrating a control method of a diagnosis apparatus according to an embodiment of the present disclosure.

Referring to FIG. 19 , the diagnostic apparatus 300 may obtain information on a first spectrum corresponding to the first liquid collected from the test subject and information on a second spectrum corresponding to the second liquid (S1910). . The first liquid may include a first target material. The second liquid may be a liquid in which a first reaction material corresponding to the first target material is added to the first liquid. The second liquid may include a first target material combined with a first reactant material.

The diagnosis apparatus 300 may obtain first concentration information of the first target material based on the information on the first spectrum and the information on the second spectrum (S1920). For example, the diagnosis apparatus 100 may obtain the concentration information based on a look-up table in which the information on the second spectrum and the peak value and concentration information of the spectrum are matched. The diagnosis apparatus 100 may obtain a coefficient for correcting the density information by inputting information on the first spectrum to the first neural network model. The diagnosis apparatus 300 may obtain corrected density information by performing correction on the density information based on the coefficient.

The diagnosis apparatus 300 may obtain first concentration information by inputting information on the first spectrum and information on the second spectrum to the second neural network model.

The diagnosis apparatus 300 may obtain a feature vector by inputting information on the second spectrum to the second neural network model. The diagnosis apparatus 300 may acquire first concentration information by inputting information about the first spectrum and a feature vector to a third neural network model.

The diagnosis apparatus 300 may obtain a first feature vector by inputting information on the first spectrum to the second neural network model. The diagnosis apparatus 300 may obtain a second feature vector by inputting information on the second spectrum to the second neural network model. The diagnosis apparatus 300 may acquire the first concentration information by inputting the first feature vector and the second feature vector to the fourth neural network model.

The diagnostic apparatus 300 may obtain diagnostic information about the subject based on the first concentration information (S1930). The diagnosis apparatus 300 may obtain diagnosis information by inputting information on the first spectrum and information on the first concentration to the fifth neural network model.

The diagnosis apparatus 300 may obtain information on a third spectrum corresponding to a third liquid obtained by adding a second reactant to the first liquid. The third liquid may include a second target material combined with a second reactant. The diagnosis apparatus 300 may obtain second concentration information of the second target material based on the information on the first spectrum and the information on the third spectrum. The diagnosis apparatus 300 may obtain diagnosis information by inputting information on the first spectrum, first concentration information, and second concentration information to the fifth neural network model.

The diagnosis apparatus 300 may obtain diagnosis information by inputting information on the first spectrum and information on the second spectrum to the sixth neural network model.

Various embodiments described above may be implemented in a recording medium readable by a computer or a similar device using software, hardware, or a combination thereof. In some cases, the embodiments described herein may be implemented in a processor itself. When implemented in software, embodiments such as procedures and functions described in this specification may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein.

Computer instructions for performing processing operations according to various embodiments of the present disclosure described above may be stored in a non-transitory computer-readable medium. Computer instructions stored in such a non-transitory computer readable medium may cause a specific device to perform processing operations according to various embodiments described above when executed by a processor.

A non-transitory computer readable medium is a medium that stores data semi-permanently and is readable by a device, not a medium that stores data for a short moment, such as a register, cache, or memory. Specific examples of the non-transitory computer readable media may include CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, and the like.

The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-temporary storage medium' only means that it is a tangible device and does not contain signals (e.g., electromagnetic waves), and this term refers to the case where data is stored semi-permanently in the storage medium and temporary It does not discriminate if it is saved as . For example, a 'non-temporary storage medium' may include a buffer in which data is temporarily stored.

Methods according to various embodiments disclosed in this document may be included and provided in a computer program product. Computer program products may be traded between sellers and buyers as commodities. A computer program product is distributed in the form of a device-readable storage medium (e.g. compact disc read only memory (CD-ROM)), or through an application store (e.g. Play Store™) or on two user devices (e.g. It can be distributed (eg downloaded or uploaded) online, directly between smartphones. In the case of online distribution, at least a part of a computer program product (eg, a downloadable app) is stored on a device-readable storage medium such as a memory of a manufacturer's server, an application store's server, or a relay server. It can be temporarily stored or created temporarily.

Although the preferred embodiments of the present disclosure have been shown and described above, the present disclosure is not limited to the specific embodiments described above, and is common in the technical field belonging to the present disclosure without departing from the gist of the present disclosure claimed in the claims. Of course, various modifications and implementations are possible by those with knowledge of, and these modifications should not be individually understood from the technical spirit or prospect of the present disclosure.

100: liquid purification device 200: liquid information acquisition device
300: diagnostic device 1000: diagnostic system

Claims (15)

In the liquid purification device,
Board;
a loading part formed on the substrate and accommodating a first liquid;
a filter unit reducing the concentration of at least one component included in the first liquid to obtain a second liquid having a reduced concentration of the at least one substance;
By mixing the second liquid and a reactant for detecting the target material, a first material having a predetermined reaction with the reactant among a plurality of materials included in the second liquid and a predetermined reaction with the reactant are not made. a reaction unit for obtaining a third liquid containing a second material; and
A separator for separating the first material and the second material;
liquid purification device. According to claim 1,
The first liquid includes blood in an undiluted state;
The at least one component includes a blood cell component
liquid purification device. According to claim 1,
The predetermined reaction includes an antigen-antibody reaction
liquid purification device. According to claim 1,
Further comprising an anticoagulant unit for adding an anticoagulant to the first liquid
liquid separation device. According to claim 1,
The reaction part,
A reaction material storage unit for storing the reaction material, and
A zigzag-shaped mixing channel for increasing the mixing rate of the second liquid and the reactant
liquid purification device. According to claim 1,
The reactant is
Characterized in that it is combined with metal nanoparticles combined with Raman active molecules
liquid purification device. According to claim 1,
a first chamber formed on the upper portion of the substrate and connected to the loading part to store the first liquid;
a second chamber formed on the upper portion of the substrate and connected to the filter unit to store the second liquid; and
A third chamber formed on the upper part of the substrate and connected to the separator to store the first material; further comprising
liquid purification device. According to claim 1,
Further comprising: an enrichment unit performing a concentration treatment on the third liquid;
The concentration treatment,
At least one of drying, heating and baking
liquid purification device. According to claim 1,
A pumping unit for moving the first liquid, the second liquid, and the third liquid; further comprising,
the pumping unit,
At least one of a pneumatic pump, a vibration pump, a mechanical pump, and a capillary pump
liquid purification device. According to claim 1,
The reaction part,
A first reaction unit including a first reaction material storage unit for storing a first reaction material for detecting a first target, and a first mixing channel for increasing a mixing ratio of the second liquid and the first reaction material;
A second reaction unit including a second reaction material storage unit for storing a second reaction material for detecting a second target, and a second mixing channel for increasing a mixing ratio of the second liquid and the second reaction material;
A first channel for transferring the second liquid to the first reaction unit, and
Comprising a second channel for transferring the second liquid to the second reaction unit
liquid purification device. According to claim 10,
The first channel and the second channel have a structure branching from the output end of the filter unit.
liquid purification device. According to claim 10,
the separation unit,
A first separation unit connected to the output end of the first reaction unit and configured to separate a third material having an antigen-antibody reaction with the first reaction material among a plurality of materials included in the third liquid and other materials, and
It is connected to the output end of the second reaction unit and includes a second separation unit for separating a fourth material and other materials having an antigen-antibody reaction with the second reaction material among a plurality of materials included in the third liquid
liquid purification device. According to claim 1,
The filter part,
Including a LCAT (Lateral Cavity Acoustic Transducer) protruding outward of the filter channel through which the first liquid flows
liquid purification device. According to claim 1,
the separation unit,
Separating the first material and the second material according to molecular weight based on sound waves;
Including a first outlet channel for moving the first material and a second outlet channel for moving the second material
liquid purification device. In a diagnostic system including a liquid purification device, a liquid information acquisition device, and a diagnosis device,
The liquid purification device,
Board;
a loading unit formed on the substrate and accommodating the first liquid collected from the test subject;
a filter unit reducing the concentration of at least one component included in the first liquid to obtain a second liquid having a reduced concentration of the at least one substance;
By mixing the second liquid and a reactant for detecting the target material, a first material having a predetermined reaction with the reactant among a plurality of materials included in the second liquid and a predetermined reaction with the reactant are not made. a reaction unit for obtaining a third liquid containing a second material; and
A separator for separating the first material and the second material;
The liquid information obtaining device,
Obtaining a Raman signal of the first material by irradiating light onto the first material;
The diagnostic device,
Obtaining a diagnostic result for the subject based on the Raman signal
diagnostic system.

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