WO2023106738A1 - Méthode et système de diagnostic d'une maladie éosinophile - Google Patents

Méthode et système de diagnostic d'une maladie éosinophile Download PDF

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WO2023106738A1
WO2023106738A1 PCT/KR2022/019443 KR2022019443W WO2023106738A1 WO 2023106738 A1 WO2023106738 A1 WO 2023106738A1 KR 2022019443 W KR2022019443 W KR 2022019443W WO 2023106738 A1 WO2023106738 A1 WO 2023106738A1
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refractive index
image
odt
eosinophils
diagnosing
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PCT/KR2022/019443
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English (en)
Korean (ko)
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백찬기
조유숙
이지향
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재단법인 아산사회복지재단
울산대학교 산학협력단
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Priority claimed from KR1020220156390A external-priority patent/KR20230085076A/ko
Application filed by 재단법인 아산사회복지재단, 울산대학교 산학협력단 filed Critical 재단법인 아산사회복지재단
Publication of WO2023106738A1 publication Critical patent/WO2023106738A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B9/00—Measuring instruments characterised by the use of optical techniques
    • G01B9/02—Interferometers
    • G01B9/02001—Interferometers characterised by controlling or generating intrinsic radiation properties
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B9/00—Measuring instruments characterised by the use of optical techniques
    • G01B9/02—Interferometers
    • G01B9/02015—Interferometers characterised by the beam path configuration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B9/00—Measuring instruments characterised by the use of optical techniques
    • G01B9/02—Interferometers
    • G01B9/0209—Low-coherence interferometers
    • G01B9/02091—Tomographic interferometers, e.g. based on optical coherence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/41—Refractivity; Phase-affecting properties, e.g. optical path length
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47—Scattering, i.e. diffuse reflection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material

Definitions

  • the present invention relates to methods and systems for diagnosing eosinophilic diseases.
  • asthma is treated with steroids, lyocotriene regulators, long-acting inhaled beta 2 agonists, theophylline, etc., aimed at relieving symptoms and controlling inflammation.
  • steroids lyocotriene regulators
  • long-acting inhaled beta 2 agonists theophylline, etc.
  • Eosinophilic asthma a type of severe asthma, is accompanied by a high eosinophil count (more than 150 cells/ ⁇ L) as a symptom.
  • Eosinophil is a type of granulocytic leukocyte with eosinophilic granules in the cytoplasm and is a major cell participating in allergic reactions.
  • Small granules in the cytoplasm of eosinophils contain peroxidase, RNase, DNA degradation It contains various chemical mediators such as enzyme (DNase), lipolytic enzyme, plasminogen, etc. These mediators are secreted by the degranulation process following the activation of eosinophils and destroy both parasites and surrounding tissues.
  • DNase enzyme
  • lipolytic enzyme plasminogen
  • An object of the present invention is to provide a diagnostic method and system capable of determining whether eosinophils are abnormal by quantifying and comparing/verifying the physical characteristics of organelles included in eosinophils.
  • Another object of the present invention is to provide a diagnostic method and system capable of effectively diagnosing eosinophilic diseases by determining whether eosinophils are abnormal.
  • one aspect of the present invention is the step of obtaining an ODT image from eosinophils, the step of obtaining a refractive index-mapped image by mapping the obtained ODT image for each region according to the refractive index, and from the refractive index-mapped image
  • the method may include extracting data and diagnosing whether or not the eosinophil is abnormal based on the extracted data.
  • the present invention can provide a diagnostic method and system capable of determining whether eosinophils are abnormal by quantifying and comparing/verifying the physical characteristics of organelles included in eosinophils.
  • the present invention can provide a diagnostic method and system capable of effectively diagnosing eosinophilic diseases by determining whether eosinophils are abnormal.
  • FIG. 1 is a block diagram illustrating components of a system for diagnosing eosinophilic diseases according to an embodiment of the present invention.
  • FIG. 2 is a block diagram for explaining the internal configuration of a processor according to an embodiment of the present invention.
  • FIG. 3 is a view for explaining the execution sequence of the method for diagnosing eosinophilic disease according to an embodiment of the present invention.
  • 4 and 5 are actual examples of 3D ODT images.
  • 6 is an actual example picture for explaining a process of refractive index mapping of an ODT image.
  • FIG. 8 is a photograph comparing eosinophils of a normal person and a patient with an eosinophilic disease.
  • 9 is a graph comparing eosinophils of normal people and patients with eosinophilic disease.
  • one aspect of the present invention is the step of obtaining an ODT image from eosinophils, the step of obtaining a refractive index-mapped image by mapping the obtained ODT image for each region according to the refractive index, and from the refractive index-mapped image
  • the method may include extracting data and diagnosing whether or not the eosinophil is abnormal based on the extracted data.
  • the ODT image may be a 3D image.
  • the 3D image may be a 4D image including changes in the eosinophil over time.
  • the obtaining of the refractive index-mapped image may further include measuring a refractive index of each region of the obtained ODT image and dividing the ODT region based on the measured refractive index.
  • the obtaining of the refractive index-mapped image may further include classifying organelles included in eosinophils based on the partitioned area.
  • the obtaining of the refractive index-mapped image may further include classifying the refractive index measured for each region of the ODT image according to at least two preset refractive index intervals.
  • a region having a refractive index corresponding to the refractive index region corresponding to the granule is extracted from the refractive index mapped image, and the dynamic characteristics of the granule are confirmed from the extracted region. It may further include steps to do.
  • the dynamic characteristics of the granules may include fusion or division of the granules over time.
  • a region having a refractive index corresponding to a low refractive index region corresponding to a vacuole is extracted from the refractive index mapped image, and the vacuole is included in the eosinophil from the extracted region. It may further include determining whether or not it is.
  • another aspect of the present invention is an image acquisition unit for obtaining an ODT image from eosinophils, an image processing unit for obtaining a refractive index mapped image by mapping the obtained ODT image for each region according to the refractive index, and the above It may include a data extraction unit that extracts refractive index data from the refractive index-mapped image and an abnormality diagnosis unit that diagnoses whether or not the eosinophil is abnormal based on the extracted data.
  • the x-axis, y-axis, and z-axis are not limited to the three axes of the Cartesian coordinate system, and may be interpreted in a broad sense including these.
  • the x-axis, y-axis, and z-axis may be orthogonal to each other, but may refer to different directions that are not orthogonal to each other.
  • FIG. 1 is a block diagram for explaining the components of a diagnosis system 100 for eosinophilic disease according to an embodiment of the present invention.
  • an eosinophilic disease diagnosis system 100 is for diagnosing eosinophilic disease.
  • Eosinophil a type of granulocytic leukocyte, contains eosinophilic granules (Eosinophil or specific granule) and has a characteristic of increasing when allergic reactions, parasitic infections, drug reactions, or inflammatory diseases occur.
  • eosinophilia the symptom of increasing eosinophils.
  • eosinophilic diseases such as severe asthma occur, eosinophilia is accompanied.
  • the eosinophilic disease diagnosis system 100 can accurately (quantitatively) and efficiently diagnose the occurrence of eosinophilic disease by analyzing the physical characteristics of eosinophils.
  • the present invention is used to diagnose eosinophilic diseases by identifying the physical characteristics of eosinophils
  • the present invention diagnoses the occurrence and type of diseases through correlation with diseases by analyzing the physical characteristics of cells.
  • the technical idea of may be applicable to diseases related to other white blood cells such as neutrophils and basophils.
  • the diagnosis system 100 for eosinophilic disease includes an image acquisition unit 110, an image processing unit 120, a memory 130, and a processor 140 electrically connected to the above components.
  • an image acquisition unit 110 an image processing unit 120, a memory 130, and a processor 140 electrically connected to the above components.
  • the image acquisition unit 110 may acquire an image of eosinophils through the eosinophil sample.
  • the image acquisition unit 110 may be a variety of photographing devices, and may obtain an image of the eosinophil by photographing an eosinophil sample using the same.
  • the image acquisition unit 110 may use an optical diffraction tomography (ODT) imaging technique. Accordingly, the image acquiring unit 110 may obtain an ODT image of the eosinophil by photographing and imaging the eosinophil sample at high speed. Specifically, the image acquisition unit 110 may obtain an ODT image of eosinophils through a label-free ODT imaging technique.
  • ODT optical diffraction tomography
  • the ODT image acquired by the image acquiring unit 110 may be a 3D image of eosinophils. Therefore, as will be described later, the image acquisition unit 110 may obtain a 3D image of the eosinophil, and the image processing unit 120 and the processor 140 digitize (absolutely quantify) the physical characteristics of organelles within the eosinophil. can be analyzed clearly.
  • the image acquisition unit 110 may capture eosinophils for a certain period of time.
  • the ODT image acquired by the image acquiring unit 110 may be a 4-dimensional image including changes in eosinophils over time in a 3-dimensional image. Therefore, as will be described later, the image acquisition unit 110 can obtain a 4-dimensional image of eosinophils, and the image processing unit 120 and processor 140 can clearly identify the kinetic characteristics of eosinophils and the dynamic characteristics of organelles within eosinophils. can be analyzed.
  • the image processor 120 may perform preprocessing for analyzing the ODT image acquired by the image acquisition unit 110 .
  • the image processor 120 may map the ODT image obtained by the image acquirer 110 for each region according to the refractive index.
  • the image processing unit 120 may measure the refractive index of each position of the ODT image acquired through the image acquisition unit 110 and map the ODT image to each specific range based on the measured refractive index.
  • the refractive index mapped image processed by the image processing unit 120 may be analyzed by the processor 140 .
  • the memory 130 may store various data.
  • the memory 130 may store a program driven by the processor 140 .
  • the program stored in the memory 130 may be used to control each component of the eosinophilic disease diagnosis system 100 or to analyze the acquired ODT image or the acquired refractive index mapped image. That is, the memory 130 may store various applications and application programs that are driven in the eosinophilic disease diagnosis system 100 .
  • the memory 130 may store data required for diagnosis of eosinophilic disease.
  • the data stored in the memory 130 may include data on physical and dynamic characteristics of normal eosinophils.
  • the data stored in the memory 130 may include data on physical and dynamic characteristics of abnormal eosinophils.
  • the memory 130 may include various data necessary for diagnosing eosinophilic disease by the diagnostic system 100 for eosinophilic disease.
  • the processor 140 may control the eosinophilic disease diagnosis system 100 .
  • the processor 140 may control the operation of the eosinophilic disease diagnosis system 100 through various application programs or applications stored in the memory 130 .
  • the processor 140 may control the eosinophilic disease diagnosis system 100 to determine whether or not eosinophils are abnormal and diagnose eosinophilic diseases. For example, the processor 140 may diagnose eosinophilic disease using the refractive index-mapped image obtained from the image processing unit 120 .
  • the processor 140 may extract data from the refractive index mapped image and determine whether the eosinophil is abnormal based on the extracted data.
  • the processor 140 may extract data from the refractive index mapped image and process the extracted data.
  • the processor 140 may be implemented as an array of a plurality of logic gates, or may be implemented as a combination of a general-purpose microprocessor and a memory 130 storing programs executable by the microprocessor.
  • a general-purpose microprocessor and a memory 130 storing programs executable by the microprocessor.
  • the present invention may be implemented in other types of hardware.
  • the processor 140 may be implemented as one or a plurality of processors. When the processor 140 is implemented in plurality, it may be physically located at a distance.
  • FIG. 2 is a block diagram for explaining the internal configuration of the processor 140 according to an embodiment of the present invention.
  • the processor 140 may include a data extraction unit 141, a data processing unit 142, and an abnormality diagnosis unit 143.
  • the data extractor 141 may extract data from the refractive index mapped image processed by the image processor 120 .
  • the data extractor 141 may extract refractive index data from each region of the refractive index mapped image. Specifically, the data extractor 141 may extract the refractive index for each organelle of the eosinophil from the refractive index mapped image.
  • the data extractor 141 may extract data about the volume of organelles included in eosinophils from the refractive index mapped image.
  • the present invention is not limited thereto, and the data extractor 141 may extract various data required for diagnosis of eosinophilic disease from the refractive index mapped image.
  • the data processing unit 142 may process the data extracted by the data extraction unit 141 .
  • the data processing unit 142 may process the extracted data and may specifically quantify the extracted data.
  • the data processor 142 may analyze the presence, density, and dynamic characteristics of organelles included in eosinophils based on the data extracted by the data extractor 141 .
  • the data processing unit 142 may analyze the extracted data to derive physical and/or dynamic characteristics required for diagnosis of eosinophilic disease.
  • the abnormality diagnosis unit 143 may determine whether or not there is an abnormality in eosinophils based on the data processed by the data processing unit 142 .
  • the abnormality diagnosis unit 143 may determine whether or not there is an abnormality in eosinophils based on physical characteristics and/or dynamic characteristics of organelles included in eosinophils.
  • the abnormality diagnosis unit 143 may compare the characteristics of the organelle derived by the data processing unit 142 with the characteristics of normal eosinophils to determine whether the eosinophil to be analyzed is normal. In addition, the abnormality diagnosis unit 143 may determine whether the eosinophil to be analyzed is normal by comparing the organelle characteristics derived by the data processing unit 142 with the abnormal characteristics of the eosinophil.
  • the method for diagnosing eosinophilic diseases according to the present invention may be performed through the diagnostic system 100 for eosinophilic diseases described above.
  • FIG. 3 is a view for explaining the execution sequence of the method for diagnosing eosinophilic disease according to an embodiment of the present invention.
  • the method for diagnosing eosinophilic disease includes obtaining an ODT image from eosinophils (S100), mapping the obtained ODT image by region according to the refractive index to obtain a refractive index-mapped image (S200), and refractive index.
  • a step of diagnosing abnormality of eosinophils from the mapped image (S300) may be included.
  • Obtaining an ODT image from eosinophils may be performed by the image acquisition unit 110.
  • the image acquisition unit 110 may acquire an image of eosinophils through the eosinophil sample.
  • the image acquisition unit 110 may obtain an image of an eosinophil using an optical diffraction tomography (ODT) imaging technique.
  • ODT optical diffraction tomography
  • the image acquisition unit 110 may acquire an image of eosinophils through a label-free optical diffraction tomography imaging technique. Accordingly, the image acquisition unit 110 may obtain an ODT image of the eosinophil by photographing and imaging the eosinophil sample at high speed.
  • the ODT image may be a 3D image. Therefore, physical properties such as volume and refractive index of eosinophils can be clearly analyzed through ODT images.
  • volume and refractive index of eosinophils can be clearly measured and analyzed using the three-dimensional ODT image. That is, it can be confirmed that the ODT image is a means suitable for confirming the volume, cross section, etc. of eosinophils in a 3D space.
  • the ODT image may be a 4-dimensional image in which changes in eosinophils over time are included in the 3-dimensional image. Therefore, the dynamic characteristics of organelles in eosinophils can be clearly analyzed through the 4-dimensional ODT image.
  • the step of obtaining a refractive index-mapped image by mapping the obtained ODT image by region according to the refractive index (S300) may be performed by the image processing unit 120.
  • the image processor 120 may perform preprocessing for analyzing the ODT image acquired by the image acquirer 110, and in detail, map the ODT image for each region according to the refractive index.
  • the image processing unit 120 may measure the refractive index of each position of the ODT image acquired through the image acquisition unit 110 and map the ODT image to each specific range based on the measured refractive index.
  • the acquiring of the refractive index mapped image ( S300 ) may include measuring the refractive index of each region of the acquired ODT image and dividing the region of the ODT image based on the measured refractive index.
  • the refractive index of each of a plurality of regions included in the ODT image may be measured, and the plurality of regions included in the ODT image may be divided into regions having similar refractive index values.
  • regions partitioned into equal regions can exhibit similar refractive indices.
  • the obtaining of the refractive index mapped image ( S300 ) may further include classifying organelles included in eosinophils based on the partitioned area.
  • each of the plurality of regions partitioned based on the refractive index may represent an organelle corresponding thereto. Therefore, when the ODT image is divided into a plurality of regions based on refractive index, various organelles included in eosinophils can be distinguished from each other.
  • the obtaining of the refractive index-mapped image (S300) may further include classifying the refractive index measured for each region of the ODT image according to at least two preset refractive index ranges.
  • the preset refractive index range may mean a range of refractive indices having regular intervals to correspond to refractive index values represented by organelles of eosinophils. That is, a plurality of sections having a predetermined range may be preset, and preferably, a preset refractive index section may be stored in the memory 130 .
  • the image processing unit 120 may classify the refractive index measured for each region of the ODT image to correspond to a preset refractive index range.
  • the ODT image can clearly represent the organelles included in the eosinophil.
  • picture A of FIG. 6 shows a region having a refractive index of 1.3378 to 1.3380 in the refractive index mapped ODT image.
  • the refractive index range of 1.3378 to 1.3380 corresponds to the refractive index range of the eosinophil cell membrane, and through this, it can be confirmed that the eosinophil cell membrane is expressed.
  • the B picture of FIG. 6 further displays a region having a refractive index of 1.3413 to 1.3614 in the refractive index mapped ODT image.
  • the refractive index range of 1.3413 to 1.3614 corresponds to the refractive index range of the cytoplasm and nucleus of the eosinophil, and through this, it can be confirmed that the cytoplasm and the nucleus of the eosinophil are expressed.
  • the photo C of FIG. 6 further displays a region having a refractive index of 1.3635 to 1.4147 in the refractive index mapped ODT image.
  • the refractive index range of 1.3635 to 1.4147 corresponds to the refractive index range of acidophilic granules, and through this, it can be confirmed that acidophilic granules are expressed.
  • the D to F photographs of FIG. 6 are further subdivided and displayed regions having refractive indices of 1.3635 to 1.4147 in the refractive index mapped ODT image.
  • the refractive index range of 1.3635 to 1.4147 corresponds to the refractive index range of acidophilic granules, and through this, it can be confirmed that acidophilic granules are expressed. In addition, it can be confirmed that the refractive index is higher in the part where the density of acidophilic granules is high.
  • the refractive index of the eosinophilic granules is higher than that of other organelles of eosinophils.
  • the ODT image is analyzed through refractive index mapping, it can be confirmed that eosinophils can be classified by organelle, and furthermore, the relative density of eosinophilic granules can be compared and analyzed.
  • a photograph G of FIG. 6 is an enlarged photograph of the acidophilic granules in the refractive index mapped ODT image. In this case, it can be confirmed that the refractive index of the central portion of the acidophilic granules is higher than that of other portions.
  • the step of diagnosing whether or not the eosinophil is abnormal may be performed by the processor 140 .
  • the processor 140 may determine whether or not there is an abnormality in eosinophils and diagnose whether or not there is an eosinophilic disease by controlling the branch system of the eosinophilic disease.
  • the step of diagnosing whether or not the eosinophil is abnormal may include extracting data from the refractive index mapped image, and diagnosing whether or not the eosinophil is abnormal based on the extracted data.
  • Extracting data from the refractive index mapped image may be performed by the processor 140 .
  • extracting data from the refractive index mapped image may be performed by the data extractor 141 .
  • the presence or absence of each organelle, the location of the organelle, and the volume and/or density of the organelle may be extracted from the refractive index mapped image.
  • the refractive index value of each organelle may be extracted.
  • data such as whether vacuoles exist, whether granules exist, and the density of granules may be extracted.
  • data that can be extracted from the refractive index mapped image to diagnose abnormality of eosinophils can be variously extracted without limitation.
  • the data extracted from the refractive index mapped image can be used to diagnose whether or not there is an abnormality in eosinophils.
  • the step of diagnosing whether the eosinophil is abnormal based on the extracted data may be performed by the processor 140 .
  • the step of diagnosing an abnormality of eosinophils based on the extracted data may be performed by the abnormality diagnosis unit 143 .
  • whether or not the eosinophil is abnormal may be diagnosed based on physical characteristics and/or dynamic characteristics of the eosinophil.
  • data of the presence of granules and the density of granules among eosinophils are extracted by the data extraction unit 141, and the abnormal diagnosis unit 143 compares the density of the granules with the density of normal eosinophil granules. It can diagnose whether eosinophils are normal or not.
  • data on the density of eosinophil granules in a normal state may be stored in the memory 130 .
  • the data extractor 141 extracts a region showing a refractive index corresponding to the granules from the refractive index range from the refractive index mapped image, and determines whether or not the eosinophil is abnormal.
  • the diagnostic unit can check the dynamic characteristics of the granules from the extracted area.
  • the dynamic characteristics of the granules may include whether the granules are fused or split.
  • the abnormal diagnosis unit 143 can diagnose whether the eosinophil is normal by comparing the fusion or division of the granules and the degree thereof with the dynamic characteristics of the eosinophil in a normal state.
  • data on the dynamic characteristics of eosinophils in a normal state may be stored in the memory 130 .
  • the 4-dimensional ODT image of eosinophils can reflect the dynamic characteristics of eosinophil granules. That is, it can be confirmed that the eosinophilic granules repeat fusion and division several times while dynamically moving within a few seconds. Therefore, the state of eosinophils can be diagnosed by comparing the dynamic characteristics of eosinophilic granules in an abnormal state with those in a normal state.
  • the data extractor 141 extracts a region corresponding to the refractive index region corresponding to the vacuole from the refractive index mapped image. And, from the extracted region, it is possible to determine whether eosinophils contain vacuoles.
  • the data extractor 141 may extract data on the number, density, volume, and the like of vacuoles. Thereafter, the abnormal diagnosis unit 143 may diagnose whether the eosinophil is normal by comparing the data related to the extracted vacuole with eosinophils in a normal state.
  • data on vacuoles present in eosinophils in a normal state may be stored in the memory 130 .
  • a step of processing the extracted data may be further performed after the step of extracting data from the refractive index mapped image.
  • Processing the extracted data may be performed by the processor 140 .
  • processing the extracted data may be performed by the data processing unit 142 .
  • processing the extracted data may include quantifying the extracted data after the data extractor 141 extracts the data from the refractive index mapped image.
  • the data processing unit 142 may quantify data related to physical quantities of vacuoles included in eosinophils, such as the number of vacuoles, refractive index of vacuoles, and volume.
  • the data processor 142 may quantify the refractive index of each region of each ODT image, the refractive index of each organelle of eosinophils, the density of granules, the refractive index of granules, and numerical data related to dynamic characteristics of granules.
  • FIG. 8 is a photograph comparing eosinophils in a normal person and an asthmatic patient
  • FIG. 9 is a graph quantifying the numerical data extracted from FIG. 8 and is a graph comparing eosinophils in a normal person and an asthmatic patient.
  • HI indicates a normal person
  • AP indicates an eosinophilic disease patient, that is, an asthmatic patient.
  • the cell number (n) is 60, and p-value ⁇ 0.05.
  • physical and dynamic characteristics of eosinophils of a normal person and a patient with eosinophilic disease may be compared through a refractive index mapped image and quantified data extracted therefrom.
  • the refractive index of the eosinophilic granules (G) is significantly high in patients with asthma
  • the refractive index of the cell membrane (PM) is the same
  • the refractive index of the cytoplasm (Cyt) is also asthmatic. It can be confirmed that the patient is significantly higher.
  • system 100 and method for diagnosing eosinophilic diseases according to the present invention can efficiently and effectively diagnose eosinophilic diseases by observing physical and dynamic characteristics of eosinophils using ODT images.
  • ODT images can be used as input data for deep learning to determine the presence or absence of abnormalities in cells. That is, according to the present invention, it is possible to build a deep learning model capable of deriving the presence or absence of abnormalities in cells based on the ODT image as input data.
  • the present invention has been introduced above that diagnosis of eosinophilic diseases can be performed using ODT images, but this is a representative example to which the technical idea of the present invention can be applied, and the analysis of eosinophils has been described. Therefore, those skilled in the art can understand that the technical concept of the present invention can be applied to other leukocyte cell-related diseases such as eosinophilic diseases, neutrophils and basophils, and the like.
  • the present invention relates to a method and system for diagnosing eosinophilic diseases that can effectively and efficiently diagnose eosinophilic diseases.
  • embodiments of the present invention can be applied to methods and systems for diagnosing eosinophilic diseases.

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Abstract

La présente invention se rapporte à une méthode et à un système de diagnostic d'une maladie éosinophile, la méthode et le système permettant de diagnostiquer de manière efficace et efficiente une maladie éosinophile. À cet effet, un aspect de la présente invention peut comprendre les étapes consistant : à acquérir une image ODT d'un éosinophile; à mapper l'image ODT acquise par zone en fonction d'un indice de réfraction de façon à acquérir une image mappée par indice de réfraction; et à extraire des données de l'image mappée par indice de réfraction, et à diagnostiquer si les éosinophiles sont anormaux en fonction des données extraites.
PCT/KR2022/019443 2021-12-06 2022-12-02 Méthode et système de diagnostic d'une maladie éosinophile WO2023106738A1 (fr)

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KR10-2021-0173240 2021-12-06
KR20210173240 2021-12-06
KR1020220156390A KR20230085076A (ko) 2021-12-06 2022-11-21 호산구성 질병의 진단 방법 및 시스템
KR10-2022-0156390 2022-11-21

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Citations (5)

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JP2014130155A (ja) * 2008-05-09 2014-07-10 Sysmex Corp 血液分析装置
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KR101855366B1 (ko) * 2016-11-08 2018-05-09 주식회사 토모큐브 간세포 내 지질을 3d 비표지 영상화 및 정량화하는 방법 및 장치
KR20200058662A (ko) * 2018-11-19 2020-05-28 노을 주식회사 이미지 분석 시스템 및 분석 방법

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8351665B2 (en) * 2005-04-28 2013-01-08 The General Hospital Corporation Systems, processes and software arrangements for evaluating information associated with an anatomical structure by an optical coherence ranging technique
JP2014130155A (ja) * 2008-05-09 2014-07-10 Sysmex Corp 血液分析装置
JP2016027903A (ja) * 2009-10-06 2016-02-25 ザ ジェネラル ホスピタル コーポレイション 好酸球を含めた特定の細胞を撮像するための装置および方法
KR101855366B1 (ko) * 2016-11-08 2018-05-09 주식회사 토모큐브 간세포 내 지질을 3d 비표지 영상화 및 정량화하는 방법 및 장치
KR20200058662A (ko) * 2018-11-19 2020-05-28 노을 주식회사 이미지 분석 시스템 및 분석 방법

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