WO2023153635A1

WO2023153635A1 - Inflammatory disease classification method and device using machine learning-based raman spectroscopic analysis

Info

Publication number: WO2023153635A1
Application number: PCT/KR2022/021418
Authority: WO
Inventors: 김준기; 이상화; 주미연
Original assignee: 재단법인 아산사회복지재단; 울산대학교 산학협력단
Priority date: 2022-02-08
Filing date: 2022-12-27
Publication date: 2023-08-17
Also published as: KR20230119901A

Abstract

Provided are an inflammatory disease classification method and device, using machine learning-based Raman spectroscopic analysis. The inflammatory disease classification method, according to the present invention, comprises the steps of: preparing and preprocessing specimens; acquiring a Raman signal and a spectrum of the Raman signal through a Raman spectroscopic analysis; analyzing the Raman signal and the spectral distribution of the Raman signal; quantifying diagnostic data by means of a preset machine learning program; and extracting a diagnostic result by comparing the Raman signal and the spectral distribution with the quantified diagnostic data.

Description

Inflammatory disease classification method and apparatus using machine learning-based Raman spectroscopic analysis

The present invention relates to a method and apparatus for classifying inflammatory diseases using machine learning-based Raman spectroscopy.

Among urinary inflammatory diseases, interstitial cystitis/bladder pain syndrome (IC/BPS) is an intractable disease that causes pelvic pain, causes frequent urination, and causes extreme pain. In order to diagnose urinary inflammatory diseases such as cystitis, clinical diagnosis is being made through patient symptom confirmation and urine bacterial examination, but there is no quantified evaluation standard yet.

Another urinary inflammatory disease, such as urolithiasis, ureteral stricture, ureteral obstruction, and nephritis, may cause kidney damage and cause chronic or acute kidney disease, so quantified evaluation criteria for early diagnosis are further required.

In recent years, metabolomics has been studied for early diagnosis of diseases, and metabolomics is an important study that reveals mechanisms of metabolome by relating metabolome to various physiological and pathological conditions. However, although metabolomics shows great potential as a biomarker for early diagnosis and monitoring of cancer, improved analysis methods for complex biological products are needed for clinical application as a quantified diagnosis method.

Further improved early disease diagnosis methods include gas chromatography mass spectrometry (GC-MS) and mass spectrometry such as liquid chromatography, Raman spectroscopy, and surface enhanced Raman spectroscopy (Surface Enhanced Raman Spectroscopy). ), etc. have emerged.

However, the early diagnosis methods according to the prior art, such as Raman spectroscopy, have a problem in that it is difficult to clearly classify and quantify a specific disease based on the test results for a specific sample or individual factors and diagnose it. Specifically, there is a problem in that accuracy is low in clearly quantifying and diagnosing a specific disease by category by checking only the spectrum distribution or detection signals detected in various ranges for a specific sample or specimen.

An object to be solved by the present invention is to provide a method and apparatus for classifying inflammatory diseases using machine learning-based Raman spectroscopic analysis.

In addition, the problem to be solved by the present invention is an inflammatory disease classification method capable of detecting chemical biomarkers for urinary inflammatory diseases and diseases by surface-enhanced Raman spectroscopy, and classifying inflammatory disease types and early diagnosis of patients. and to provide an apparatus.

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

An inflammatory disease classification method performed by an inflammatory disease classification apparatus according to an aspect of the present invention for solving the above problems includes preparing and pre-treating specimens; Obtaining a Raman signal and a spectrum of the Raman signal through Raman spectroscopy; analyzing the Raman signal and a spectrum distribution of the Raman signal; Quantifying diagnostic data through a preset machine learning program; and extracting a diagnostic result by comparing the Raman signal and the spectrum distribution with quantified diagnostic data.

In this case, in the step of obtaining the Raman signal and the spectrum of the Raman signal, the Raman signal and the spectrum of the Raman signal may be obtained by performing a surface-enhanced Raman scattering test.

In addition, the acquiring of the Raman signal and the spectrum of the Raman signal may further include correcting the Raman spectroscopy signal by performing biomarker detection correction in the surface-enhanced Raman scattering test process.

In addition, in the step of correcting the Raman spectroscopy signal, nano-sized biomarkers on a surface-enhanced Raman scattering test kit are applied as Raman measurement targets, and intensity changes of amplified peaks of the nano-sized molecules are measured to measure the Raman spectroscopic signal. The spectral signal can be calibrated.

In addition, in the step of analyzing the Raman signal and the spectral distribution of the Raman signal, the type of metabolite contained in each sample, inflammation level, and inflammation type are analyzed for each sample, and the type of metabolite, inflammation level, Depending on the type of inflammation, the progress of urinary inflammatory diseases such as nephritis, cystitis, cancer, and acute kidney disease can be analyzed.

In addition, the quantifying the diagnostic data through the preset machine learning program may include inputting the Raman signal and the Raman spectrum detection result into the machine learning program and the database as input values; additionally inputting additional learning factors including subject detection human information as input values of the machine learning program; selecting at least one or more machine learning models and machine learning programs; allowing the selected machine learning program to learn by reflecting the input values; and quantifying the diagnostic data according to an execution result of the machine learning program.

In this case, the step of additionally inputting additional learning factors including the sample detection human information as input values of the machine learning program includes age, gender, detection human information including diseases, findings, symptoms, sample detection amount, and detection site. Information can be additionally input as the input values.

The extracting of the diagnosis result may include comparing the Raman signal and the Raman spectrum detection result with the diagnosis data quantified through the machine learning program; and setting a diagnosis result according to the comparison result and displaying it on a display screen.

In addition, an apparatus for classifying inflammatory diseases according to an aspect of the present invention for solving the above problems includes a Raman test module for obtaining a Raman signal and a spectrum of the Raman signal by performing a Raman spectroscopic test on specimens; a data learning module that receives learning factors including the Raman signal and a spectral distribution of the Raman signal and performs preset machine learning to quantify diagnostic data; and an early diagnosis module for extracting a diagnosis result by comparing the Raman signal and the spectrum distribution of the Raman signal with the quantified diagnosis data.

In this case, the Raman inspection module may include a sample inspection pre-processing unit including a surface-enhanced Raman scattering inspection kit; and a Raman signal detector for acquiring the Raman signal by performing a surface-enhanced Raman scattering test on the sample pretreated on the surface-enhanced Raman scattering test kit.

In addition, the Raman test module applies the nano-sized biomarkers on the surface-enhanced Raman scattering test kit as Raman measurement targets, and measures the intensity change of the amplified peak of the nano-sized molecule to obtain the Raman spectroscopy signal. A spectral signal corrector for correcting and quantifying may be further included.

In addition, the early diagnosis module analyzes the type of metabolite, inflammation level, and inflammation type contained in each sample for each sample, and according to the type of metabolite, inflammation level, and inflammation type, nephritis, cystitis, cancer, etc. of urinary system inflammatory disease, and progression to acute kidney disease can be analyzed.

In addition, the data learning module may include: a learning factor input unit inputting the Raman signal and a spectrum detection result of the Raman signal to a machine learning program and a database as input values; a machine learning program input unit for storing machine learning programs for each of a plurality of machine learning models and selecting and compiling at least one or more machine learning models and machine learning programs; a machine learning processing unit executing the selected machine learning program so that the selected machine learning program reflects the input values and is machine trained; and a disease diagnosis model generating unit quantifying the diagnosis data according to an execution result of the machine learning program.

In addition, the learning factor input unit may additionally input detection human information including age, gender, disease, findings, and symptoms, a sample detection amount, and detection site information as the input values.

In addition, the early diagnosis module may include: a data processing unit that reads the Raman signal and the Raman spectrum detection result and classifies them according to subject personal information including age, gender, diseases, findings, and symptoms; a disease diagnosis model extractor that reads the quantified diagnosis data from the data learning module; a data comparison detection unit that compares and analyzes the Raman signal, a spectrum detection result of the Raman signal, and the quantified diagnostic data; and a diagnosis result extraction unit configured to set and display a diagnosis result according to a comparison result with the diagnosis data.

The inflammatory disease classification method and apparatus according to an embodiment of the present invention can quickly classify and quantify the patient's inflammatory disease type through machine learning-based Raman spectroscopic analysis results and machine learning, and perform early diagnosis using the quantified results. can Therefore, it is possible to prevent damage to the kidney by diagnosing inflammation of the ureter near the kidney at an early stage.

In addition, the inflammatory disease classification method and apparatus according to the embodiment of the present invention can more quickly and accurately classify and diagnose difficult-to-diagnose urinary diseases such as cystitis and nephritis, enabling appropriate and rapid treatment.

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

1 is a flowchart for sequentially explaining a method for classifying inflammatory diseases according to an embodiment of the present invention.

2 is a graph showing Raman signal detection results obtained through a surface-enhanced Raman scattering method.

Figure 3 is a diagram showing the random forest (Random forest) results of the Raman spectrum obtained through the surface-enhanced Raman scattering method.

4A to 4C are diagrams showing results of sequentially performing principal component analysis and linear discriminant analysis of the Raman spectrum obtained through the surface-enhanced Raman scattering method.

5A to 5C are diagrams showing results of sequentially performing non-negative matrix factorization-linear discriminant analysis of the Raman spectrum obtained through the surface-enhanced Raman scattering method.

FIG. 6 is a flowchart illustrating the method of analyzing spectral data and extracting a disease diagnosis model shown in FIG. 1 in more detail.

7 is a block diagram showing a configuration of an inflammatory disease classification apparatus according to an embodiment of the present invention in detail.

Like reference numbers designate like elements throughout the specification. This specification does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present invention belongs is omitted. The term 'unit, module, member, or block' used in the specification may be implemented as software or hardware, and according to embodiments, a plurality of 'units, modules, members, or blocks' may be implemented as one component, It is also possible that one 'part, module, member, block' includes a plurality of components.

When a certain component is said to "include", this means that it may further include other components, not excluding other components unless otherwise stated.

Terms such as first and second are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

Expressions in the singular number include plural expressions unless the context clearly dictates otherwise.

In each step, the identification code is used for convenience of description, and the identification code does not explain the order of each step, and each step may be performed in a different order from the specified order unless a specific order is clearly described in context. there is.

Hereinafter, the working principle and embodiments of the present invention will be described with reference to the accompanying drawings.

Referring to FIG. 1, the inflammatory disease classification method includes preparing and preprocessing a specimen (ST1), obtaining a Raman signal through a Raman test (ST2), correcting a Raman spectroscopy signal (ST3), Raman signal and The step of processing Raman spectroscopy data by analyzing the spectrum of the Raman signal (ST4), the step of quantifying the diagnostic results (diagnostic data) analyzed by performing machine learning (ST5), and the step of extracting the diagnostic results (ST6). include

Specifically, in the step of preparing and pre-processing the specimen (ST1), the specimens detected from the inflammatory site or the specimens detected from the test subject are placed in the test kit for disease testing without a separate separation process. In order to classify and examine urinary system diseases among various inflammatory diseases, urine detected from inflammation inducing sites such as the ureter or bladder can be applied as a sample. In addition, inflammation levels and related diseases can be detected through Raman spectroscopy and analysis of urine samples.

On the other hand, urine of 8- to 10-week-old laboratory rats can be used as a sample in order to quantify the urinary diagnostic data through examination and analysis through the specimen. Hereinafter, urine of 23 laboratory rats aged 8 to 10 weeks could be used as the specimens applied to show the Raman test results in graphs and drawings. More specifically, 5 μl of 1.5 ml of urine from 4 animals with interstitial cystitis symptoms, 5 animals with mild ureteral obstruction symptoms, 5 animals with severe ureteral obstruction symptoms, 5 animals with control group, and 4 animals with normal group could be applied as samples.

In the step of obtaining a Raman signal through Raman spectroscopy (ST2), a Raman signal may be obtained by performing a surface-enhanced Raman scattering test. The surface-enhanced Raman scattering method according to an embodiment is an analysis method that can overcome the detection sensitivity limit of Raman spectroscopy and provides molecular-specific information about biological and chemical samples. In the surface-enhanced Raman scattering test, a target material is quantified by measuring a change in intensity of amplified characteristic peaks of nano-sized molecules.

Specifically, in the step of acquiring the Raman signal and the spectrum of the Raman signal (ST2), 5 μl of the sample is applied to the surface-enhanced Raman scattering substrate and waited for about 30 minutes to partially evaporate moisture while spreading more widely. In addition, a Raman signal may be obtained by measuring a portion where the sample is widely distributed in the substrate using a Raman spectroscopic method.

In the process of acquiring the Raman signal, background correction of the Raman signal may be performed. In other words, when acquiring a Raman signal through surface-enhanced Raman scattering inspection, the biomarker detection and correction may be performed to correct the Raman spectroscopy signal (ST3).

In the activation process of the surface-enhanced Raman scattering method, the intensity of the Raman spectrum is remarkably enhanced by absorption energy at the surface. Here, an enhancement factor (EF) used as a measure of the surface-enhanced Raman scattering scale is usually 10 ⁴ to 10 ⁸ , and sometimes reaches 10 ¹⁴ , which enables single-molecule level detection. Thus, in the case of the surface-enhanced Raman scattering method, the intensity of the signal is dramatically increased by about 10 ⁶ to about 10 ⁸ compared to the general Raman spectroscopy method, so that even a small amount of inflammatory markers can be detected. Therefore, in the Raman spectroscopy signal correction step (ST3) according to an embodiment of the present invention, nano-sized biomarkers on the surface-enhanced Raman scattering test kit are tested. In addition, the Raman signal and spectral distribution can be calibrated and quantified by measuring a change in intensity of a characteristic peak of the amplified biomarker Raman signal.

In the step of analyzing the Raman signal and the spectrum distribution of the Raman signal (ST4), the type of metabolite contained in the sample, the level of inflammation, the type of inflammation, etc. are confirmed for each sample collected. In addition, the degree of progression of diseases (eg, nephritis, cystitis, urinary inflammatory diseases such as cancer, and acute kidney disease) according to the type of metabolite, inflammation level, and type of inflammation can be confirmed.

2 is a graph showing Raman signal detection results obtained through a surface-enhanced Raman scattering method. Specifically, Figure 2 is a graph showing the surface-enhanced Raman signals of pyelonephritis and interstitial cystitis specimens.

Referring to FIG. 2, in the step of analyzing the Raman signal and the spectral distribution of the Raman signal (ST4), inflammation levels due to severe ureteral obstruction can be confirmed through a sample (SO_K) taken between the kidney and the ureter, and collected from the bladder. Even one sample (SO_B) can confirm the level of inflammation caused by severe ureteral obstruction. On the other hand, another sample (MO_K) taken between the kidney and ureter can confirm the level of inflammation due to mild ureteral obstruction, and another sample (MO_B) taken from the bladder can also show the level of inflammation due to mild ureteral obstruction. You can check. On the other hand, the level of inflammation according to interstitial cystitis can be confirmed through a sample (IC/BPS) taken from the bladder.

In addition, it can be seen that the inflammatory signal of pyelonephritis is prominent in the range of about 650 cm ^-1 to 750 cm ^-1 , and the inflammatory signal of interstitial cystitis is dominant in the range of about 910 cm ^-1 to 1060 cm ^-1 .

Referring to Figure 3, in the step of analyzing the Raman signal and the spectrum distribution of the Raman signal (ST4), by using a Raman spectroscopy equipment (eg, 532nm laser, 785nm laser Raman Device) between 500cm ^-1 ~ 3000cm ^-1 A preset wavelength range may be sampled and a spectrum distribution of the sampled wavelength range may be analyzed. Here, in the wavelength range of 500 cm ⁻¹ to 3000 cm ⁻¹ , related diseases such as interstitial cystitis, mild ureteral obstruction, severe ureteral obstruction, and control group can be identified and analyzed.

In addition, if the spectrum distribution of the Raman signal is analyzed through a pre-set program on a computer, the content and increase in the type of protein, lipid, RNA, DNA, etc. can be analyzed according to the unique Raman spectrum distribution and coverage of organic and inorganic molecules. there is.

For example, as shown in FIG. 3, a sample taken between the kidney and the ureter (SO_K), a sample taken from the bladder (SO_B), another sample taken between the kidney and the ureter (MO_K), and another sample taken from the bladder ( MO_B), and a disease classification result can be confirmed by calculating a disease prediction value through a random forest ensemble algorithm result of a Raman spectrum for each specific sample (IC/BPS) collected from the bladder.

Referring to Figures 4a to 4c, in the step of analyzing the Raman signal and the spectral distribution of the Raman signal (ST4), by using a Raman spectroscopy equipment (eg, 532nm laser, 785nm laser Raman Device) 500cm ^-1 ~ 3000cm ^- A preset wavelength range between ¹ and 1 may be sampled, and the spectrum distribution of the sampled wavelength range may be analyzed. Here, in the wavelength range of 500 cm ⁻¹ to 3000 cm ⁻¹ , related diseases such as interstitial cystitis, mild ureteral obstruction, severe ureteral obstruction, and control group can be identified and analyzed.

As shown in FIGS. 4A to 4C, a sample collected between the kidney and the ureter (SO_K), a sample collected from the bladder (SO_B), another sample collected between the kidney and the ureter (MO_K), and the bladder through principal component analysis (PCA) Linear discriminant analysis after obtaining 5 in FIG. 4A, 30 in FIG. 4B, and 50 in FIG. The results of disease classification can be confirmed by deriving the result value through the (LDA) model.

Referring to Figures 5a to 5c, in the step of analyzing the Raman signal and the spectrum distribution of the Raman signal (ST4), by using Raman spectroscopy equipment (eg, 532nm laser, 785nm laser Raman Device) 500cm ^-1 ~ 3000cm ^- A preset wavelength range between ¹ and 1 may be sampled, and the spectrum distribution of the sampled wavelength range may be analyzed. Here, in the wavelength range of 500 cm ⁻¹ to 3000 cm ⁻¹ , related diseases such as interstitial cystitis, mild ureteral obstruction, severe ureteral obstruction, and control group can be identified and analyzed.

As shown in FIGS. 5A to 5C, a sample collected between the kidney and the ureter through non-negative matrix decomposition (NMF) (SO_K), a sample collected from the bladder (SO_B), and another sample collected between the kidney and the ureter (MO_K) , another sample (MO_B) taken from the bladder, and the non-negative matrix decomposition component (NMFC) of the Raman spectrum for each specific sample (IC/BPS) taken from the bladder are 5 in FIG. 5, 30 in FIG. 5 b, and 30 in FIG. 5 c. After acquiring 50, the disease classification result can be confirmed by deriving the result value through a linear discriminant analysis (LDA) model.

Referring to FIG. 6, in step ST5 of quantifying diagnostic results (diagnostic data) analyzed through a preset machine learning program, the Raman signal and Raman spectrum detection results are entered into the machine learning program and database as input values. step (SS1), step of additionally inputting additional learning factors including sample detection human information as input values of a machine learning program (SS2), step of selecting and setting at least one machine learning model and machine learning program (SS3) , making the set machine learning program reflect the input values for learning processing (SS4), and quantifying diagnostic data according to the execution result of the machine learning program (SS5).

Specifically, in order to perform machine learning, first, the Raman signal detection result and the Raman spectrum detection result are entered as input values into an input program and a database (SS1). At this time, at least one machine learning program among preset machine learning programs By selecting , you can additionally input the Raman signal detection result and Raman spectrum detection result, including sample-related information, as learning factors. (SS2, SS3)

Machine learning programs include PCA (Principal Component Analysis)-LDA (Linear Discriminant Analysis) model, NMF (Non-Negative Matrix Factorization)-LDA (Linear Discriminant Analysis) model, RFML (Random Forest Machine Learning) model, deep learning (e.g. For example, Convolutional Neural Networks (CNN) and the like may be concurrently applied. When inputting learning factors, in order to clearly classify the resultant classification information, the sample detection amount, detection site information, and detection personal information (eg, age, gender, disease, findings, symptoms, etc.) may be additionally input.

Depending on the results of machine learning through the machine learning model and the program, the type of metabolite contained in the sample, the type and type of inflammation, the level of inflammation, and the disease according to the level of inflammation (urinary inflammatory diseases such as nephritis, cystitis, and cancer) , acute kidney disease) can be upgraded. In addition, according to sample detection personal information such as age, gender, disease, findings, and symptoms, and related classification information, the progress of urinary inflammatory diseases such as nephritis, cystitis, and cancer, and the progress of acute kidney disease, respectively, are diagnostic data. can be quantified and databased. (SS5)

In addition, referring to FIG. 6 , the step of extracting the diagnosis result (ST6) is the step of comparing and analyzing the Raman signal detection result and the Raman spectrum detection result and the diagnostic data quantified through a machine learning program (SS6), and the comparison result and setting a diagnostic result according to the method and displaying it on a display screen such as a monitor (SS7).

Specifically, in order to extract diagnostic results in the SS6 step, the Raman signals extracted by the surface-enhanced Raman scattering test for each sample and the spectrum distribution results of the Raman signals are compared and analyzed with diagnostic data quantified through a machine learning program.

And, in the SS7 step, the diagnosis result is finally extracted according to the comparison result of the Raman signal and the spectrum distribution result of the Raman signal and the quantified diagnosis data, and the finally extracted diagnosis result can be displayed on a separate monitor or display screen. there is.

The inflammatory disease classification apparatus shown in FIG. 7 includes a Raman examination module 100, an early diagnosis module 200, and a data learning module 300.

Specifically, the Raman inspection module 100 acquires a Raman signal by performing a surface-enhanced Raman scattering inspection on the pretreated sample. To this end, the Raman inspection module 100 includes a sample inspection and preprocessing unit 101 and a Raman signal detection unit 103 .

The sample inspection and pre-processing unit 101 of the Raman test module 100 includes a test kit in which a sample detected from an inflammation-inducing site or a sample of a test subject is placed and pre-processed so as to spread for a predetermined period of time for disease inspection.

The Raman signal detector 103 obtains a Raman signal by performing a surface-enhanced Raman scattering test on a sample pretreated on a surface-enhanced Raman scattering test kit. Then, the obtained Raman signal is stored in the database.

The Raman signal detector 103 measures the change in intensity of the amplified characteristic peak of the Raman nano-sized molecule by performing a sample inspection using a preset surface-enhanced Raman scattering method, and measures the Raman signal and Raman signal for the target material. A spectrum of the signal can be acquired and quantified.

When the Raman signal is acquired through the surface-enhanced Raman scattering method of the Raman signal detector 103, the Raman test module 100 filters molecules larger than the micro size on the surface-enhanced Raman scattering test kit, and nano-sized biomarkers. It may further include a spectroscopy signal correction unit 105 for correcting and quantifying the Raman spectroscopy signal by measuring a change in intensity of the amplified peak. In the case of the surface-enhanced Raman scattering test method, the intensity of the signal is dramatically increased by about 10 ⁶ to about 10 ⁸ compared to the general Raman spectroscopy method, so that even a small amount of inflammatory markers can be detected. Accordingly, the spectral signal corrector 105 of the Raman test module 100 quantifies the target material by measuring intensity changes of amplified characteristic peaks of nano-sized biomarkers on the surface-enhanced Raman scattering test kit.

The data learning module 300 quantifies diagnostic data by receiving learning factors including a Raman signal and a spectral distribution of the Raman signal and performing preset machine learning. To this end, the data learning module 300 may include a learning factor input unit 301, a machine learning processor 303, a machine learning program input unit 305, and a disease diagnosis model generator 307.

The learning factor input unit 301 of the data learning module 300 allows the Raman signal detection result and the Raman spectrum detection result to be input into a preset program and database as input values. At this time, the learning factor input unit 301 may further input additional learning factors including detection human information when inputting the Raman signal detection result and the Raman spectrum detection result. In other words, when inputting the learning factor, the sample detection amount, detection site information, and detection personal information (eg, age, gender, disease, findings, symptoms, etc.) can be additionally input to clearly classify the resulting classification information. there is.

The machine learning program input unit 305 stores machine learning programs for each of a plurality of machine learning models, and selects and compiles at least one machine learning model and machine learning program. At this time, the machine learning programs include PCA (Principal Component Analysis)-LDA (Linear Discriminant Analysis) model, NMF (Non-Negative Matrix Factorization)-LDA (Linear Discriminant Analysis) model, RFML (Random Forest Machine Learning) model, deep learning (eg, Convolutional Neural Networks (CNN)) may be concurrently applied.

The machine learning processing unit 303 executes the input and selected machine learning program from the machine learning program input unit 305 to determine the type of metabolite contained in each specimen, the type and type of inflammation, the level of inflammation, and the level of inflammation. Upgrade disease progression information.

The disease diagnosis model generation unit 307 quantifies diagnosis data according to a result of executing a machine learning program. At this time, the disease diagnosis model generation unit 307 provides progress information of urinary inflammatory diseases such as nephritis, cystitis, cancer, and acute kidney disease according to sample-related classification information such as age, gender, diseases possessed, findings, and symptoms, respectively. It is quantified as diagnostic data and stored and shared in a database.

The early diagnosis module 200 compares and analyzes the Raman signal and the spectral distribution of the Raman signal and the quantified diagnostic data to extract a diagnostic result for an inflammatory disease. To this end, the early diagnosis module 200 may include a data processing unit 202, a disease diagnosis model extraction unit 204, a data comparison detection unit 206, and a diagnosis result extraction unit 208.

The data processing unit 202 of the early diagnosis module 200 reads the Raman signal detection results and the Raman spectrum detection results and classifies them according to specimen-related classification information such as age, gender, diseases possessed, findings, and symptoms. At this time, the disease diagnosis model extractor 204 reads the quantified diagnosis data from the data learning module 300 in real time.

The comparison detector 206 compares and analyzes the Raman signal detection result and the Raman spectrum detection result and the quantified diagnostic data. The comparison detection unit 206 sequentially compares and analyzes the Raman signals extracted through the surface-enhanced Raman scattering test for each sample and the spectrum distribution result of the Raman signals with diagnostic data quantified through a machine learning program.

The diagnosis result extraction unit 208 sets and displays diagnosis results according to comparison results. The diagnosis result extraction unit 208 may finally extract a diagnosis result according to a comparison result between the Raman signal and the spectrum distribution result of the Raman signal and the quantified diagnosis data, and display the result on a screen.

The early diagnosis module 200 and the data learning module 300 include a memory (not shown) storing data for an algorithm or a program reproducing the algorithm for controlling the operation of components in the processor of each module, and a memory ( Alternatively, it may include a processor (not shown) that performs the above-described operation using data stored in a database. In this case, the memory and the processor may be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

Each component refers to software and/or hardware components such as Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing commands executable by the early diagnosis module 200 and the data learning module 300 . Instructions may be stored in the form of program codes, and when executed by a processor, create program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as an early diagnosis module 200, a data learning module 300, and a computer-readable recording medium.

The early diagnosis module 200 and the data learning module 300 or computer-readable recording media include all types of recording media in which instructions readable by a computer are stored. For example, there may be read only memory (ROM), random access memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, and the like.

As above, the disclosed embodiments have been described with reference to the accompanying drawings. Those skilled in the art to which the present invention pertains will understand that the present invention can be implemented in a form different from the disclosed embodiments without changing the technical spirit or essential features of the present invention. The disclosed embodiments are illustrative and should not be construed as limiting.

Claims

In the method performed by the inflammatory disease classification device,

Preparing and pre-processing samples;

Obtaining a Raman signal and a spectrum of the Raman signal through Raman spectroscopy;

analyzing the Raman signal and a spectrum distribution of the Raman signal;

Quantifying diagnostic data through a preset machine learning program; and

Comprising the step of extracting a diagnostic result by comparing the Raman signal and the spectral distribution and quantified diagnostic data, inflammatory disease classification method.
According to claim 1,

The step of obtaining the Raman signal and the spectrum of the Raman signal,

An inflammatory disease classification method for obtaining the Raman signal and the spectrum of the Raman signal by performing a surface-enhanced Raman scattering test.
According to claim 2,

The step of obtaining the Raman signal and the spectrum of the Raman signal,

Further comprising the step of correcting the Raman spectroscopy signal by performing biomarker detection correction in the surface-enhanced Raman scattering test process, the inflammatory disease classification method.
According to claim 3,

The step of correcting the Raman spectroscopy signal,

Applying nano-sized biomarkers on a surface-enhanced Raman scattering test kit as Raman measurement targets, and measuring a change in intensity of an amplified peak of the nano-sized molecule to correct the Raman spectroscopy signal, Inflammatory disease classification method.
According to claim 2,

Analyzing the Raman signal and the spectrum distribution of the Raman signal,

For each of the samples, the type of metabolite contained in each sample, the level of inflammation, and the type of inflammation are analyzed, and urinary inflammatory diseases such as nephritis, cystitis, cancer, and acute An inflammatory disease classification method for analyzing the progression of kidney disease.
According to claim 2,

The step of quantifying diagnostic data through the preset machine learning program,

inputting the Raman signal and the Raman spectrum detection result into the machine learning program and the database as input values;

additionally inputting additional learning factors including subject detection human information as input values of the machine learning program;

selecting at least one or more machine learning models and machine learning programs;

allowing the selected machine learning program to learn by reflecting the input values; and

Inflammatory disease classification method comprising the step of quantifying the diagnostic data according to the execution result of the machine learning program.
According to claim 6,

The step of additionally inputting additional learning factors including the subject detection personal information as input values of the machine learning program,

A method for classifying inflammatory diseases, wherein age, sex, disease, diagnosis, and human detection information including symptoms, specimen detection amount, and detection site information are additionally input as the input values.
According to claim 6,

The step of extracting the diagnosis result,

comparing the Raman signal and the Raman spectrum detection result with the diagnostic data quantified through the machine learning program; and

An inflammatory disease classification method comprising the step of setting a diagnosis result according to a comparison result and displaying it on a display screen.
a Raman test module for obtaining a Raman signal and a spectrum of the Raman signal by performing a Raman spectroscopy test on the specimens;

a data learning module that receives learning factors including the Raman signal and a spectral distribution of the Raman signal and performs preset machine learning to quantify diagnostic data; and

An inflammatory disease classification apparatus comprising an early diagnosis module for extracting a diagnosis result by comparing the Raman signal and the spectrum distribution of the Raman signal and the quantified diagnosis data.
According to claim 9,

The Raman inspection module,

a sample inspection pre-processing unit including a surface-enhanced Raman scattering inspection kit; and

An inflammatory disease classification apparatus comprising a Raman signal detector for obtaining the Raman signal by performing a surface-enhanced Raman scattering test on a sample pretreated on the surface-enhanced Raman scattering test kit.
According to claim 10,

The Raman inspection module,

A spectroscopic signal correction unit for applying nano-sized biomarkers on the surface-enhanced Raman scattering test kit as Raman measurement targets and measuring a change in intensity of an amplified peak of the nano-sized molecule to correct and quantify the Raman spectroscopic signal. Inflammatory disease classification device further comprising.
According to claim 11,

The early diagnosis module,

For each sample, the type of metabolite contained in each sample, the level of inflammation, and the type of inflammation are analyzed, and according to the type of metabolite, level of inflammation, and type of inflammation, urinary inflammatory diseases such as nephritis, cystitis, cancer, and acute An inflammatory disease classification device that analyzes the progression of kidney disease.
According to claim 9,

The data learning module,

a learning factor input unit inputting the Raman signal and a spectrum detection result of the Raman signal into a machine learning program and a database as input values;

a machine learning program input unit for storing machine learning programs for each of a plurality of machine learning models and selecting and compiling at least one or more machine learning models and machine learning programs;

a machine learning processing unit executing the selected machine learning program so that the selected machine learning program reflects the input values and is machine trained; and

Inflammatory disease classification apparatus comprising a disease diagnosis model generation unit for quantifying the diagnosis data according to an execution result of the machine learning program.
According to claim 13,

The learning factor input unit,

An inflammatory disease classification device for additionally inputting detection human information including age, gender, disease possessed, findings, and symptoms, sample detection amount, and detection site information as the input values.
According to claim 13,

The early diagnosis module,

a data processing unit that reads the Raman signal and the Raman spectrum detection result and classifies them according to subject personal information including age, gender, diseases, findings, and symptoms;

a disease diagnosis model extractor that reads the quantified diagnosis data from the data learning module;

a data comparison detection unit that compares and analyzes the Raman signal, a spectrum detection result of the Raman signal, and the quantified diagnostic data; and

Inflammatory disease classification apparatus comprising a diagnosis result extraction unit for setting and displaying a diagnosis result according to a comparison result with the diagnosis data.