Patent Description:
As a method for measuring an antigen or an antibody, immunoassay using antigen-antibody reaction is generally used for testing a measurement sample which is derived from an organism and contains blood, urine, or the like. It is generally known that, in the test using the immunoassay, nonspecific reaction progresses due to rheumatoid factor, complement component C1q, or the like which is mixed in a biological sample such as blood or urine, and the test result may indicate false-positive. <CIT> discloses a method for reducing influence caused by such nonspecific reaction.

According to <CIT>, an antibody, in which a constant region (Fc portion) that has antibody amino acid sequences indicating high similarity and that may cause such nonspecific reaction is blocked by a blocking agent, is used to reduce nonspecific reaction.

However, even when the method described in <CIT> is used, it is difficult to completely inhibit occurrence of nonspecific reaction in the immunoassay. Occurrence of nonspecific reaction makes a test result false-positive. Therefore, occurrence of nonspecific reaction needs to be detected to prevent report of false-positive test results. Frutiger, Andreas, et al. is a review on fundamental concepts and consequences for biosensing application discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how to address and harness nonspecific interactions for in vivo systems.

An object of the present invention is to provide a detection method for detecting occurrence of nonspecific reaction, an analysis method, an analyzer, and a detection program for detecting occurrence of nonspecific reaction, which are capable of detecting occurrence of nonspecific reaction in an analysis system using antigen-antibody reaction.

The invention relates to the following embodiments:.

The present invention is directed to a detection program for detecting occurrence of nonspecific reaction. The detection program causes a computer to execute, when the computer is caused to execute the program, generating a data group about progress of antigen-antibody reaction between an antigen or an antibody contained in a biological sample and an antibody or an antigen contained in a measurement reagent, inputting the data group to a deep learning algorithm, and generating information about occurrence of nonspecific reaction based on a result outputted from the deep learning algorithm.

The detection method for detecting occurrence of nonspecific reaction, the analysis method, the analyzer, and the detection program for detecting occurrence of nonspecific reaction can detect occurrence of nonspecific reaction in an analysis system using antigen-antibody reaction.

Occurrence of nonspecific reaction can be detected in an analysis system using antigen-antibody reaction.

An analyzer of the present embodiment as defined in the appended claims (hereinafter, simply referred to as "analyzer <NUM>") will be described with reference to <FIG>.

The analyzer <NUM> measures a biological sample by turbidimetric immunoassay. The turbidimetric immunoassay is a measurement method for measuring a process of antigen-antibody reaction between an antigen or an antibody in a biological sample and an antibody or an antigen, in a measurement reagent, which specifically binds to the antigen or the antibody in the biological sample. The analyzer <NUM> analyzes, for example, D-dimer as a fibrin degradation product or FDP as a fibrin and/or fibrinogen degradation product.

In a case where D-dimer is measured, a measurement reagent containing carrier particles to which an antibody for recognizing D-dimer is fixed, can be used as a measurement reagent. Examples of the D-dimer measurement reagent include LIAS AUTO (registered trademark)·D-dimer NEO manufactured by SYSMEX CORPORATION, Nanopia (registered trademark) D-dimer manufactured by SEKISUI MEDICAL CO. , and LPIA-GENESIS (registered trademark) D-dimer manufactured by LSI Medience Corporation.

In a case where FDP is measured, a measurement reagent containing carrier particles to which an antibody for recognizing FDP is fixed, can be used as a measurement reagent. Examples of the FDP measurement reagent include LIAS AUTO (registered trademark) P-FDP manufactured by SYSMEX CORPORATION, Nanopia (registered trademark) P-FDP manufactured by SEKISUI MEDICAL CO. , and LPIA (registered trademark) FDP-P manufactured by LSI Medience Corporation.

The analyzer <NUM> may perform, for example, analysis of a biological sample by nephelometric immunoassay as well as measurement by turbidimetric immunoassay. The analyzer <NUM> may analyze not only D-dimer or FDP but also, for example, soluble fibrin monomer complex (hereinafter, may be abbreviated as "FMC"), von Willebrand factor antigen (hereinafter, may be abbreviated as "VWF: Ag"), immunoglobulins IgG, IgA, and IgM, complement markers C3 and C4, antistreptolysin-O, vancomycin, µ-albumin, prealbumin (P-Alb), lipoprotein (a), adenosine <NUM>'-diphosphate (ADP), collagen, epinephrine, or CRP.

A biological sample measured by the analyzer <NUM> is plasma. The biological sample measured by the analyzer <NUM> may be a blood sample such as whole blood or serum, urine, pleural fluid, ascites, lymph, interstitial fluid, cerebrospinal fluid, and the like in addition to plasma.

The analyzer <NUM> can prepare a measurement sample by adding, to a biological sample, a measurement reagent containing an antibody or an antigen that specifically binds (that is, causes antigen-antibody reaction with) to an antigen or an antibody in the biological sample, apply light to the prepared measurement sample, detect transmitted light from the measurement sample, and analyze the antigen or the antibody in the biological sample based on the detected light, to detect occurrence of nonspecific reaction in the measurement sample. <FIG> illustrates an example of an outer appearance of the analyzer <NUM>. The analyzer <NUM> includes a measurement device <NUM> for applying light to a measurement sample for a predetermined time period, and obtaining detection information indicating an intensity of light transmitted through the measurement sample, and an input/output device <NUM> capable of performing data input and output.

<FIG> illustrates an example of a hardware configuration of the analyzer <NUM>. The measurement device <NUM> of the analyzer <NUM> includes a controller <NUM> and a detection unit <NUM>. The controller <NUM> includes an arithmetic processing unit <NUM> for performing data processing, a storage unit <NUM> used as a work area for the data processing, a storage unit <NUM> for storing information to be transferred to the storage unit <NUM>, a bus <NUM> for transmitting data between the units, and interfaces (I/Fs) 206a and 206b for performing data input/output with an external device. The controller <NUM> is connected to an electronic medical chart system <NUM> via a network <NUM>.

The arithmetic processing unit <NUM> is implemented by a CPU (central processing unit). The input/output device <NUM> is connected to the interface (I/F) 206a, and the network <NUM> is connected to the interface (I/F) 206b. The interface (I/F) 206a is a USB and the interface (I/F) 206b is Ethernet. The storage unit <NUM> is implemented by a DRAM and an SRAM. The storage unit <NUM> is implemented by a solid-state drive. The input/output device <NUM> is implemented by a touch-panel type display, and receives input from an operator by contact with the display and displays information on the display. Signals are transmitted via the bus <NUM> in the measurement device. The configuration of the controller <NUM> is not limited to the above-described one. For example, IEEE1394 may be used as the interface (I/F) 206a or the interface (I/F) 206b. For example, a hard disk may be used as the storage unit <NUM>. The input/output device <NUM> may include a keyboard and/or a mouse as an input device and a liquid crystal display or an organic EL display as an output device.

<FIG> illustrates information stored in the storage unit <NUM>. The storage unit <NUM> stores: a measurement program 202a for causing the measurement device <NUM> to perform a measurement process and output detection information; a test substance analysis/nonspecific reaction detection program 202b for generating a data group from the detection information and analyzing the data group; a deep learning algorithm database DB1 for storing a deep learning algorithm used for analysis; a calibration curve/threshold value database DB2 for storing a calibration curve corresponding to each measurement item, a high concentration determination threshold value corresponding to each measurement item, and a nonspecific reaction determination threshold value corresponding to each measurement item; a re-analysis item database DB3 for storing information of items of a re-analysis performed when the re-analysis is needed because of suspicion of occurrence of nonspecific reaction; and a re-analysis result database DB4 for associating a data group about progress of antigen-antibody reaction for each biological sample for which presence or absence of occurrence of nonspecific reaction has been specified by the re-analysis, with identification information (sample No. or the like) of the biological sample, and presence or absence of occurrence of nonspecific reaction, and storing the data group associated with the identification information and the presence or absence of occurrence of nonspecific reaction.

Referring again to <FIG>, the detection unit <NUM> includes a sample preparation unit <NUM>, a light applying unit <NUM>, and a detector <NUM>.

<FIG> illustrates a structure of the light applying unit <NUM>. The light applying unit <NUM> includes five light sources <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>, five optical fiber sections 330a, 330b, 330c, 330d, and 330e disposed so as to correspond to the five light sources <NUM> to <NUM>, and one holding member <NUM> for holding the light sources <NUM> to <NUM> and incident ends <NUM> of the optical fiber sections 330a to 330e. The light sources <NUM> to <NUM>, the optical fiber sections 330a to 330e, and the holding member <NUM> are housed in a metal housing <NUM>. Each of the light sources <NUM> to <NUM> is implemented by a LED. The first light source <NUM> emits <NUM> (first wavelength) light. The second light source <NUM> emits <NUM> (second wavelength) light. The third light source <NUM> emits <NUM> (third wavelength) light. The fourth light source <NUM> emits <NUM> (fourth wavelength) light. The fifth light source <NUM> emits <NUM> (fifth wavelength) light. The first light source <NUM> to the fifth light source <NUM> are controlled by the controller <NUM> so as to sequentially emit light one by one repeatedly such that each of the first light source <NUM> to the fifth light source <NUM> emits light for a predetermined time period (for example, T seconds (T is less than <NUM>)) as described below, until a predetermined time (for example, <NUM> seconds) elapses since start of light application.

The optical fiber sections 330a to 330e are each implemented by a cable which has one core and is obtained by bundling optical fiber element wires. The optical fiber sections 330a to 330e are bundled into one at an intermediate section <NUM>, and the optical fiber sections 330a to 330e bundled into one are divided into two bundles, and an emission end <NUM> of each bundle is held at an outlet <NUM> disposed in the housing <NUM>. An incident end <NUM> of an optical fiber <NUM> that connects between the light applying unit <NUM> and the detector <NUM> is also held at each outlet <NUM>. A homogenizing member <NUM> for homogenizing an intensity distribution of light emitted through the emission end <NUM> is disposed between the emission end <NUM> for the optical fiber sections 330a to 330e and the incident end <NUM> of the optical fiber <NUM>. The homogenizing member <NUM> reflects therein light incident on an incident surface <NUM> multiple times, and is implemented by, for example, a polygonal-prism-shaped rod homogenizer.

<FIG> illustrate a structure of the detector <NUM>. The detector <NUM> is disposed at each optical fiber <NUM> connected to the light applying unit <NUM>. The structure for connection between the optical fiber <NUM> and the detector <NUM> and the hardware configuration of the detector <NUM> are common in all the detectors <NUM>. Therefore, in the description herein, one detector <NUM> will be described. <FIG> illustrates the detector <NUM> in which a transparent container <NUM> for mixing a measurement reagent and a biological sample is not held. The detector <NUM> has a hole 22b into which the other end <NUM> of the optical fiber <NUM> having the incident end <NUM> (<FIG>) held at the outlet <NUM> of the light applying unit <NUM> is inserted. Furthermore, a connection hole 22c that connects the hole 22b to a holding portion 22a for holding the container <NUM> is formed in the detector <NUM>.

The diameter of the hole 22b is larger than the diameter of the connection hole 22c. A lens 22d for condensing light from the optical fiber 21is disposed at the end portion of the hole 22b. Furthermore, a hole 22f is formed at an inner wall surface of the holding portion 22a so as to oppose the connection hole 22c. A light receiver <NUM> is disposed deeply in the hole 22f. The light receiver <NUM> is implemented by, for example, a photodiode, and outputs an electrical signal corresponding to an amount of received light. Light transmitted through the lens 22d is incident on a light receiving surface of the light receiver <NUM> through the connection hole 22c, the holding portion 22a, and the hole 22f. The optical fiber <NUM> is fixed by a plate spring 22e in a state where the end <NUM> is inserted in the hole 22b.

<FIG> illustrates the detector <NUM> that holds the container <NUM>. By holding the container <NUM> in the holding portion 22a, light condensed by the lens 22d is incident on the light receiver <NUM> through the container <NUM> and the measurement sample contained in the container <NUM>. When antigen-antibody reaction progresses in the measurement sample, a turbidity of the measurement sample is enhanced. According thereto, an amount (amount of transmitted light) of light transmitted through the measurement sample is reduced, and a level of an electrical signal outputted from the light receiver <NUM> is lowered. The electrical signal outputted by the light receiver <NUM> is converted to a digital signal by an A/D converter <NUM>, and transmitted via the I/F <NUM> (see <FIG>) to the controller <NUM>. The signal that is outputted by the light receiver <NUM> and converted by the A/D converter <NUM> represents digital data based on the amount of transmitted light. The detector <NUM> may be structured such that the light receiver <NUM> is disposed on a straight line intersecting a straight line that connects between the lens 22d and the light receiver <NUM>, and light scattered by the measurement sample is detected.

The sample preparation unit <NUM> dispenses a biological sample and a measurement reagent into the container <NUM>, and transfers the container <NUM> that contains a measurement sample in which the biological sample and the measurement reagent are mixed, to the holding portion 22a of the detector <NUM>.

As the light applying unit <NUM>, the sample preparation unit <NUM>, and the detector <NUM>, for example, the units disclosed in <CIT> can be used.

<FIG> shows a flow of an analysis process (measurement/test substance analysis/nonspecific reaction detection process) performed by the controller <NUM>. The controller <NUM> executes a measurement process according to the measurement program 202a in step S1. Subsequently, the controller <NUM> executes a test substance analysis/nonspecific reaction detection process according to the test substance analysis/nonspecific reaction detection program 202b in step S2. The measurement process is started when the controller <NUM> receives a process start instruction inputted through the input/output device <NUM> by an operator.

<FIG> shows a flow of the measurement process executed by the controller <NUM> according to the measurement program 202a. In step S11, the controller <NUM> obtains, for each biological sample, information of a measurement item ordered for the biological sample, based on the information inputted from the input/output device <NUM> by the operator. The controller <NUM> may obtain the information of the measurement item via the network <NUM> from, for example, the electronic medical chart system <NUM> of a medical institution. The biological sample and the information of the measurement item can be associated with each other by, for example, an identifier issued when the test is requested.

The controller <NUM> causes the sample preparation unit <NUM> to dispense the biological sample into the container <NUM> in step S12. In step S13, the controller <NUM> causes the sample preparation unit <NUM> to dispense, into the container <NUM>, a measurement reagent corresponding to the measurement item obtained in step S11 and prepare a measurement sample.

In step S14, the controller <NUM> causes the light applying unit <NUM> to start applying light to the container <NUM> in which the measurement sample has been prepared in step S13. Specifically, the first light source <NUM> to the fifth light source <NUM> sequentially emit light one by one repeatedly such that each of the first light source <NUM> to the fifth light source <NUM> emits light for a predetermined time period (for example, T seconds (T is less than <NUM>)), until a predetermined time (for example, <NUM> seconds) elapses since start of light application. The detector <NUM> outputs electrical signals (digital data) corresponding to intensities (that is, intensities of transmitted light) of light received via the container <NUM> one by one such that each electrical signal is outputted for the predetermined time period (for example, T seconds). The digital data having been output indicates an intensity of light transmitted through the measurement sample. The outputted digital data set is transmitted as the detection information to the controller <NUM>. Thus, each piece of data of the detection information expresses an intensity of the transmitted light at each measurement time point from start of light application to elapse of the predetermined time (for example, <NUM> seconds).

<FIG> shows a flow of the test substance analysis/nonspecific reaction detection process executed by the controller <NUM> according to the test substance analysis/nonspecific reaction detection program 202b.

The controller <NUM> receives the detection information from the detector <NUM> and generates a data group about progress of antigen-antibody reaction from the received detection information, in step S21. Specifically, the controller <NUM> receives the detection information from the detector <NUM>, classifies the received detection information for each of the first light source <NUM> to the fifth light source <NUM> (that is, for each of wavelengths of light emitted from the light sources), and stores the detection information in the storage unit <NUM>, in step S21. The controller <NUM> converts each piece of digital data of the stored detection information to absorbance, chronologically arranges absorbances obtained by the conversion, and stores the absorbances as a data group about progress of antigen-antibody reaction in the storage unit <NUM>. Since each piece of the digital data of the detection information represents an intensity of transmitted light, the detection information can be converted to absorbance by, for example, expressing a reciprocal of each piece of the digital data of the detection information by a common logarithm. <FIG> illustrates an example of the data groups about progress of antigen-antibody reaction. As illustrated in <FIG>, data groups D11 to D15 are generated for respective wavelengths of light emitted from the light sources and include absorbances arranged in the order of output from the detector <NUM>. Each piece of data in the data group D11 expresses an absorbance at each measurement time point from the start of light application to elapse of a predetermined time (for example, <NUM> seconds). Similarly, each piece of data in the data groups D12 to D15 also expresses an absorbance at each measurement time point from the start of light application to elapse of the predetermined time (for example, <NUM> seconds).

<FIG> illustrates the data group D11, among the data groups illustrated in <FIG>, which corresponds to the wavelength (first wavelength) of light emitted from the first light source <NUM> such that the data group D11 is plotted in a graph in which the vertical axis (Y axis) represents absorbances and the horizontal axis (X axis) represents elapse of time (seconds) from the start of output of the detection information. When an antigen or an antibody as a test substance (hereinafter, an antigen or an antibody to be analyzed is referred to as "test substance") is in the biological sample, antigen-antibody reaction between the antigen or the antibody and an antibody or an antigen fixed to carrier particles progresses, whereby the carrier particles agglutinate, and turbidity of the measurement sample in the container <NUM> is enhanced. Therefore, when a test substance is in the biological sample, absorbance is enhanced according to the concentration.

The controller <NUM> analyzes the test substance in step S22 shown in <FIG>. That is, the controller <NUM> obtains a concentration of the test substance in the biological sample, based on the data group (for example, data group D11) that corresponds to a wavelength according to the measurement item and a calibration curve stored in the calibration curve/threshold value database DB2. Specifically, the controller <NUM> calculates a change amount or change rate of the absorbance from the data group. The change amount or change rate of the absorbance can be calculated from the data group by using a Rate method or a VLin method. The Rate method is a method in which a start point and an end point are preset, and an absorbance change amount per one minute is calculated by linear regression, for a data group included between the start point and the end point. The VLin method is a method in which a start point and an end point at which an absorbance change amount is maximal and linear approximation is optimal, are set for a data group, and an absorbance change amount per one minute is calculated by linear regression, for the data group included between the start point and the end point. Whether calculation of the change amount or change rate is performed in the Rate method or the VLin method is determined by the controller <NUM> according to the measurement item. Before the change amount or change rate of the absorbance is calculated, the data group may be subjected to pretreatment such as smoothing, sharpness process, or caving.

The calibration curve is obtained in a manner in which a reference substance having a known concentration of a test substance is diluted at different dilution rates, each diluted reference substance is measured to obtain a change amount or change rate of absorbance, and each change amount or change rate is plotted in a graph in which the vertical axis represents change amounts or change rates of the absorbance and the horizontal axis represents concentrations of the test substance, and linear regression is performed. The controller <NUM> applies, to the calibration curve, the change amount or change rate of the absorbance obtained from the data group, and obtains the concentration of the test substance in the biological sample.

In step S23 shown in <FIG>, the controller <NUM> determines whether or not the concentration obtained in step S22 exceeds a high concentration determination threshold value stored in the calibration curve/threshold value database DB2. In a case where the concentration of the test substance obtained in step S22 is less than or equal to the threshold value ("No" in step S23), the process is advanced to step S27 to output the concentration of the test substance obtained in step S22 to the input/output device <NUM>, and the process is ended. In a case where the concentration obtained in step S22 exceeds the threshold value ("Yes" in step S23), the controller <NUM> advances the process to step S24. The controller <NUM> performs detection of occurrence of nonspecific reaction in the measurement sample in steps S24 and S25.

A method for generating a deep learning algorithm <NUM> illustrated in <FIG> will be described. The deep learning algorithm <NUM> is not limited to any specific one as long as the deep learning algorithm <NUM> is an algorithm having a neural network structure. The deep learning algorithm <NUM> may include, for example, a convolutional neural network, a full-connect neural network, and combinations thereof. The deep learning algorithm <NUM> is generated by training an untrained deep learning algorithm <NUM> (<FIG> illustrates an outline of training of the deep learning algorithm. The training of the deep learning algorithm is performed by a training apparatus <NUM> described below.

For training data for training the deep learning algorithm <NUM>, a data group, about progress of antigen-antibody reaction, obtained from a biological sample for which whether or not nonspecific reaction has occurred is known, is used as first training data. The first training data can be generated in the method described in step S21 shown in <FIG>. As illustrated in <FIG>, the first training data includes a first data group D1 corresponding to a first wavelength, a second data group D2 corresponding to a second wavelength, a third data group D3 corresponding to a third wavelength, a fourth data group D4 corresponding to a fourth wavelength, and a fifth data group D5 corresponding to a fifth wavelength. The data group D1 includes a plurality of data pieces d11, d12, d13··· each representing an absorbance. Similarly, the data group D2 to the data group D5 each include a plurality of data pieces each representing an absorbance. The first training data is inputted to an input layer 50a of the neural network <NUM> illustrated in <FIG>. A label ("presence/absence of occurrence of nonspecific reaction" in the example illustrated in <FIG>) that indicates presence or absence of occurrence of nonspecific reaction and corresponds to the inputted first training data is inputted as second training data to 50b as an output layer. Through these inputs, the first training data and the second training data are associated with each other for each biological sample, and a weight for each layer in an intermediate layer 50c in the neural network <NUM> is calculated. Input of the first training data and the second training data, and calculation of the weight of each layer in the intermediate layer 50c are performed for multiple biological samples, thereby generating the deep learning algorithm <NUM>, illustrated in <FIG>, having been trained. A softmax function is applied to the output layer 60b of the deep learning algorithm <NUM>. Thus, a value representing a probability of occurrence of nonspecific reaction is outputted from the deep learning algorithm <NUM>.

With reference to <FIG>, in step S24 shown in <FIG>, the controller <NUM> operates to input the data group D11, obtained in step S21, corresponding to the wavelength (first wavelength) of light emitted from the first light source <NUM>, the data group D12, obtained in step S21, corresponding to the wavelength (second wavelength) of light emitted from the second light source <NUM>, the data group D13, obtained in step S21, corresponding to the wavelength (third wavelength) of light emitted from the third light source <NUM>, the data group D14, obtained in step S21, corresponding to the wavelength (fourth wavelength) of light emitted from the fourth light source <NUM>, and the data group D15, obtained in step S21, corresponding to the wavelength (fifth wavelength) of light emitted from the fifth light source <NUM>, to the input layer 60a of the deep learning algorithm <NUM> stored in the deep learning algorithm database DB1. In the deep learning algorithm <NUM>, the inputted data groups D11 to D15 are processed at the intermediate layer 60c, and a probability (<NUM>% in the example illustrated in <FIG>) of occurrence of nonspecific reaction is outputted from the output layer 60b. The value representing the probability may not necessarily be indicated by a percentage, and may be indicated by a relative value. In the description herein, information based on a value representing a probability such as a percentage and a relative value is referred to as information indicating probability.

The "probability of occurrence of nonspecific reaction" represents a probability that nonspecific reaction has actually occurred in a measurement sample to be analyzed. For example, this means that, in a case where the probability of occurrence of nonspecific reaction is <NUM>%, a probability that the nonspecific reaction has actually occurred in the measurement sample is <NUM>%. In other words, the probability of occurrence of nonspecific reaction being <NUM>% means that, in a case where the number of measurement samples for which the probability of occurrence of the nonspecific reaction is <NUM>% is <NUM>, the number of the measurement samples in which the nonspecific reaction has actually occurred is <NUM>, and the nonspecific reaction has not occurred in the remaining <NUM> samples.

As illustrated in <FIG> and <FIG>, the structure of the data group in the first training data and the structure of the data group obtained in step S21 are preferably the same. However, the present invention is not limited thereto. For example, although the first wavelength to the fifth wavelength in the first training data are the same as the first wavelength to the fifth wavelength in the data group obtained in step S21, only a part of wavelengths may be used in the data group in the first training data and/or the data group obtained in step S21. Furthermore, although the number of pieces of data in each data group in the first training data and the number of pieces of data in each data group in the data groups obtained in step S21 are equal to each other, the number of pieces of data in each data group in the first training data may be different from the number of pieces of data of each data group in the data groups obtained in step S21.

The structure of the data group in the first training data and the data group obtained in step S21 may not necessarily be obtained for a plurality of wavelengths, and may be obtained for one wavelength, and the obtained data group may be inputted to the deep learning algorithm <NUM> or the deep learning algorithm <NUM>.

The deep learning algorithm <NUM> may be generated so as to be commonly used for a plurality of measurement items, or may be generated for each measurement item. In a case where the deep learning algorithm <NUM> is generated for each measurement item, the controller <NUM> selects, in step S24 shown in <FIG>, the deep learning algorithm <NUM> to which the data group is to be inputted, from a plurality of deep learning algorithms, according to the measurement item obtained in step S11.

In step S25 shown in <FIG>, the controller <NUM> determines whether or not the probability, of occurrence of nonspecific reaction, outputted from the deep learning algorithm <NUM> in step S24 exceeds the nonspecific reaction determination threshold value stored in the calibration curve/threshold value database DB2. In a case where the probability, outputted in step S24, of occurrence of nonspecific reaction is less than or equal to the threshold value ("No" in step S25), the process is advanced to step S27 to output the concentration of the test substance obtained in step S22 to the input/output device <NUM>, and the process is ended. In a case where the probability, outputted in step S24, of occurrence of nonspecific reaction exceeds the threshold value ("Yes" in step S25), the controller <NUM> advances the process to step S26. The controller <NUM> operates to output the concentration obtained in step S22 to the input/output device <NUM> in step S26. Furthermore, the controller <NUM> generates information about occurrence of nonspecific reaction and outputs the information to the input/output device <NUM> in step S26.

The deep learning algorithm <NUM> may be generated so as to output a probability that nonspecific reaction has not occurred, from the output layer 60b. In this case, the controller <NUM> determines whether or not a probability, outputted from the deep learning algorithm <NUM> in step S24, that nonspecific reaction has not occurred is less than the nonspecific reaction determination threshold value stored in the calibration curve/threshold value database DB2. In a case where the probability, outputted in step S24, that nonspecific reaction has not occurred is less than the threshold value, the controller <NUM> advances the process to step S27. In a case where the probability is greater than or equal to the threshold value, the controller <NUM> advances the process to step S26.

<FIG> illustrates an example of a message box MB1 for outputting information, about occurrence of nonspecific reaction, which is generated in step S26 and outputted to the input/output device <NUM>. The message box MB1 includes a measurement item information region MB11 that indicates measurement item information including a measurement date, a measurement time, a biological sample identifier (biological sample ID), a measurement result, and the like, and a message region MB12 indicating information about occurrence of nonspecific reaction for the measurement result. As the measurement result in the measurement item information region MB11, the concentration, of the test substance as a measurement target, which is obtained in step S22 shown in <FIG> is displayed. In the example illustrated in <FIG>, a D-dimer concentration (DD C) and an FDP concentration (FDP C) are indicated. In the message region MB12, the measurement item (DD C), an output label (+++) for qualitatively indicating suspicion of nonspecific reaction, and information (<NUM>%) indicating the probability are indicated. The information indicating the probability represents a probability, of occurrence of nonspecific reaction, outputted from the deep learning algorithm <NUM> in step S24. As the output label, "+", "++", "+++", and the like are used according to a magnitude of difference between the probability and the nonspecific reaction determination threshold value. Instead of "+", "++", and "+++", "low", "medium", "high", and the like may be used.

<FIG> illustrates a message box MB1' as a modification of the message box MB1. In the message box MB1', for a measurement item for which the probability of occurrence of nonspecific reaction exceeds the threshold value, the previous measurement result of the same subject is displayed in the measurement item information region MB11'. The previous measurement result of the same subject can be obtained from the electronic medical chart system <NUM> by using a biological sample ID as a key. In the measurement item information region MB11', for a measurement item for which a probability of occurrence of nonspecific reaction has exceeded the threshold value, a history of the previous measurement results or a previous measurement result for a measurement item different from the current measurement item may be displayed. In the message box MB1', in a case where there is a discrepancy between the concentration of a test substance obtained in step S22, and a concentration of another test substance or clinical information of the subject, a message indicating that the concentration displayed in the measurement item information region MB11' is suspected to have a false high value due to nonspecific reaction, is displayed in the message region MB12'. Thus, an operator can clearly know that a re-analysis is necessary. In the message box MB1', clinical information of the same subject is displayed in a clinical information region MB13. The clinical information can be obtained from the electronic medical chart system <NUM> by using the biological sample ID as a key. The clinical information is obtained by diagnosing a subject, and includes a CT scan result, an MRI scan result, an ultrasonography result, an angiography result, a medication history, and the like. A treatment history includes information of a medication history indicating, for example, whether or not an anticoagulant agent is used.

In the measurement item information region MB11 and/or the measurement item information region MB11', a measurement result of a measurement item for which the probability of occurrence of nonspecific reaction exceeds the threshold value may not be displayed. Furthermore, in the measurement item information region MB11 and/or the measurement item information region MB11', a measurement result of an measurement item for which the probability of occurrence of nonspecific reaction exceeds the threshold value may be displayed with a label indicating that the result is indicated as a reference value or a value that is required to be confirmed. The label may be text representing "reference value", "confirmation is required", or the like, or a symbol such as "*" and "!".

The process step of step S24 shown in <FIG> may be performed also in a case where the concentration of the test substance obtained in step S22 does not exceed the threshold value ("No" in step S23). In a case where the process step of step S24 is not performed when the concentration of the test substance does not exceed the threshold value, a processing speed of the controller <NUM> can be inhibited from being reduced due to increase of an arithmetic amount. Meanwhile, in a case where the process step of step S24 is performed also when the concentration of the test substance does not exceed the threshold value, occurrence of nonspecific reaction can be more assuredly detected.

The analyzer <NUM> may be structured so as to perform a re-analysis process described below. In a case where the analyzer <NUM> is structured so as to perform a re-analysis process, the controller <NUM> executes a re-analysis process shown in <FIG> after step S26 shown in <FIG>. The controller <NUM> operates to output a dialog box MB3 for causing an operator to input selection of a re-analysis, to the input/output device <NUM>, in step S28. As illustrated in <FIG>, the dialog box MB3 includes a message region MB31 including a message for inquiring of an operator whether or not a re-analysis is to be performed, and a selection region MB33 for causing the operator to select a re-analysis item. The selection region MB33 includes a selection region MB331 for selecting a re-analysis of the same measurement item as the measurement item (D-dimer in the example illustrated in <FIG>) for which nonspecific reaction is likely to have occurred, a selection region MB332 for selecting an analysis of a measurement item (FDP in the example illustrated in <FIG>) different from the measurement item (D-dimer in the example illustrated in <FIG>) for which nonspecific reaction is likely to have occurred, a selection region MB333 for selecting a re-analysis performed by diluting a biological sample for the same measurement item as the measurement item (D-dimer in the example illustrated in <FIG>) for which nonspecific reaction is likely to have occurred, and a selection region MB334 for performing selection so as not to perform a re-analysis.

The measurement item displayed in the selection region MB332 is stored in the re-analysis item database DB3. As illustrated in <FIG>, in the re-analysis item database DB3, a measurement item for an initial measurement and a measurement item for a re-analysis are stored so as to be associated with each other. The measurement item for the re-analysis is preferably an item related to the measurement item for the initial measurement. For example, in a case where the measurement item for the initial measurement is a thrombus·fibrinolysis-related measurement item, a thrombus·fibrinolysis-related measurement item different from the measurement item for the initial measurement is preferably selected as the measurement item for the re-analysis. In <FIG>, in a case where the measurement item for the initial measurement is D-dimer, the measurement item for the re-analysis is FDP. In a case where the measurement item for the initial measurement is FDP, the measurement item for the re-analysis is D-dimer. In a case where the measurement item for the initial measurement is FMC, the measurement item for the re-analysis is soluble fibrin (SF), thrombin-antithrombin III complex (TAT), and/or prothrombin fragment <NUM>+<NUM> (F1+<NUM>). In a case where the measurement item for the initial measurement is VWF:Ag, the measurement item for the re-analysis is von Willebrand factor activity (ristocetin cofactor) (VWF:RCo), VWF binding ability (VWF:GPIbR) to recombinant GPIb in the presence of ristocetin, VWF binding ability (VWF:GPIbM) to recombinant GPIb mutant (gain-of-function), and/or monoclonal antibody binding ability (VWF:Ab) to A1 domain of VWF. In a case where a plurality of measurement items for the re-analysis correspond to the measurement item for the initial measurement, the controller <NUM> causes the input/output device <NUM> to display the selection regions MB332 of the respective measurement items one by one.

The controller <NUM> determines whether or not the selection region MB334 is selected by an operator, in step S29 shown in <FIG>. In a case where the selection region MB334 is selected ("Yes" in step S29), the controller <NUM> returns the process to the test substance analysis/nonspecific reaction detection process shown in <FIG>. In a case where the selection region MB334 is not selected ("No" in step S29), the controller <NUM> advances the process to step S30. The controller <NUM> determines in step S30 whether or not the selection region MB331 is selected. In a case where the selection region MB331 is selected ("Yes" in step S30), the controller <NUM> advances the process to step S12 shown in <FIG>, and executes a measurement process for the measurement item obtained in step S11. In a case where the selection region MB331 is not selected ("No" in step S30), the controller <NUM> advances the process to step S31. The controller <NUM> determines in step S31 whether or not the selection region MB332 is selected. In a case where the selection region MB332 is selected ("Yes" in step S31), the controller <NUM> advances the process to step S11 shown in <FIG>, obtains a measurement item (FDP in the example illustrated in <FIG>) designated in the selection region MB332, and executes a measurement process for the measurement item. In a case where the selection region MB332 is not selected ("No" in step S31), the controller <NUM> advances the process to step S32. The controller <NUM> determines in S32 whether or not the selection region MB333 is selected. In a case where the selection region MB333 is selected ("Yes" in step S32), the controller <NUM> advances the process to step S12' to cause the sample preparation unit <NUM> to dispense the biological sample into the container <NUM> and dilute the biological sample. Subsequently, the controller <NUM> advances the process to step S13 shown in <FIG>, and executes a measurement process for the measurement item obtained in step S11. In a case where the selection region MB333 is not selected ("No" in step S32), the controller <NUM> returns the process to the test substance analysis/nonspecific reaction detection process shown in <FIG>. In a case where the process is advanced to step S27 in the test substance analysis/nonspecific reaction detection process shown in <FIG> ("No" in step S23 or step S25), the controller <NUM> does not execute the re-analysis process.

Selection from the selection region MB331 to the selection region MB334 may not necessarily be performed by an operator, and may be performed by the controller <NUM> according to initial setting or setting performed by an operator before start of the measurement. Furthermore, the controller <NUM> may select from the selection region MB331 to the selection region MB334 according to a probability, outputted in step <NUM> shown in <FIG>, of occurrence of nonspecific reaction.

In the re-analysis for the same measurement item as the measurement item for the initial measurement, a measurement reagent in the same vial as for the initial measurement may be used or a measurement reagent in a vial different from the vial for the initial measurement may be used. In a case where a measurement reagent in a vial different from that for the initial measurement is used, a measurement reagent in another vial in the same lot as for the initial measurement may be used, or a measurement reagent in another vial in a lot different from a lot for the initial measurement may be used. Furthermore, in a case where a measurement reagent in a vial different from that for the initial measurement is used, a measurement reagent, for the same measurement item, which has been developed and manufactured by a company different from a company for the initial measurement, may be used.

<FIG> illustrates an example of a message box MB1" displayed by the input/output device <NUM> in the re-analysis process, in step S27 shown in <FIG>. In the message box MB1", the concentration obtained in the re-analysis, the concentration obtained in the initial measurement, and a message indicating that the concentration obtained in the initial measurement indicates a reference value, are displayed in the measurement item information region MB11". In the message region MB12", a message indicating that the displayed information is related to a result of the re-analysis, is displayed.

In a case where an operator specifies presence or absence of occurrence of nonspecific reaction by the re-analysis, the operator inputs information about presence or absence of occurrence of nonspecific reaction from the input/output device <NUM>. The controller <NUM> may associate the inputted information about presence or absence of occurrence of nonspecific reaction, with the identification information of the biological sample and a data group, about progress of antigen-antibody reaction, obtained in step S21 shown in <FIG> for the biological sample, and store them in the re-analysis result database DB4 of the storage unit <NUM>. <FIG> illustrates an example of the re-analysis result database DB4. The information stored in the re-analysis result database DB4 can be used as original data of the first training data and the second training data for further training the deep learning algorithm <NUM>, and an analysis accuracy of the deep learning algorithm <NUM> can be further enhanced.

<FIG> illustrates an example of an outer appearance of the training apparatus <NUM> (hereinafter, simply referred to as "training apparatus <NUM>") for training the deep learning algorithm <NUM>. The training apparatus <NUM> is connected to an input device <NUM> and an output device <NUM> so as to be able to communicate therewith. A general-purpose computer can be used as the training apparatus <NUM>.

<FIG> illustrates an example of a hardware configuration of the training apparatus <NUM>. The training apparatus <NUM> includes a controller <NUM>. The controller <NUM> includes an arithmetic processing unit <NUM>, a storage unit <NUM>, a storage unit <NUM>, an interface (I/F) <NUM>, and a bus <NUM>. The arithmetic processing unit <NUM> is implemented by, for example, a CPU (central processing unit). The input device <NUM>, the output device <NUM>, and the network <NUM> are connected to the interface (I/F) <NUM>. The interface (I/F) <NUM> is implemented by, for example, a USB, IEEE1394, or Ethernet. The storage unit <NUM> is implemented by, for example, a solid-state drive or a hard disk drive. The storage unit <NUM> stores a training program 502b for performing training of the deep learning algorithm <NUM> as described above. The storage unit <NUM> is implemented by, for example, a DRAM or an SRAM. The input device <NUM> is implemented by, for example, a keyboard or a mouse. The output device <NUM> is implemented by, for example, a liquid crystal display or an organic EL display. Signals are transmitted via the bus <NUM> in the training apparatus <NUM>.

The training apparatus <NUM> is connected to the analyzer <NUM> over the network <NUM>. Thus, the deep learning algorithm <NUM> having been trained can be transmitted to the analyzer <NUM> over the network <NUM>, and information stored in the re-analysis result database DB4 of the analyzer <NUM> can be received from the analyzer <NUM> to further train the deep learning algorithm <NUM>.

Information about occurrence of nonspecific reaction in antigen-antibody reaction can be obtained in a method described below in addition to or instead of the method for obtaining the information by using the deep learning algorithm <NUM> as described above. That is, in the method according to the modification, as shown in <FIG>, the controller <NUM> generates, based on a predetermined function, a distribution of calculated values (S values) obtained from the function for data groups, about progress of antigen-antibody reaction, obtained from a plurality of biological samples for which nonspecific reaction does not occur, in step S81. In step S82, the controller <NUM> acquires, based on a predetermined function, a calculated value (S value) obtained from the function for a data group, about progress of antigen-antibody reaction, obtained from a biological sample to be analyzed. In step S83, the controller <NUM> obtains information about occurrence of nonspecific reaction based on the distribution generated in step S81 and the calculated value obtained in step S82 for the biological sample to be analyzed.

Specifically, in step S81, the controller <NUM> executes the process step of step S1 shown in <FIG> and the process step of step S21 shown in <FIG> for a biological sample for which nonspecific reaction does not occur, and obtains the data group about progress of the antigen-antibody reaction. The controller <NUM> calculates the S value according to the following mathematical expression <NUM> based on the obtained data group. <NUM>] <MAT> (In expression (<NUM>), α<NUM> represents an amplitude of a waveform rendered by the data group about progress of the antigen-antibody reaction);
[Math. <NUM>] <MAT> (In expression (<NUM>), α<NUM>, β<NUM>, γ<NUM>, and t<NUM> represent an amplitude, a wavelength inclination, y-axis direction transfer, and x-axis direction transfer, respectively, of the waveform rendered by the data group about progress of antigen-antibody reaction, for progress of the antigen-antibody reaction.

α<NUM> is determined by fitting of expression (<NUM>) to the obtained data group about progress of the antigen-antibody reaction. α<NUM> in expression (<NUM>) determined in this manner is substituted into expression (<NUM>). The maximum value and the minimum value of absorbance included in expression (<NUM>) are determined from the obtained data group about progress of the antigen-antibody reaction. The S value is calculated according to expression (<NUM>) based on these values.

The controller <NUM> calculates the S values similarly for the other multiple biological samples for which nonspecific reaction does not occur, and generates a histogram in which the vertical axis represents the number of biological samples and the horizontal axis represents the S values, as illustrated in <FIG>. At the most frequent value (S value=<NUM> in the example illustrated in <FIG>) in the S value distribution, a possibility of occurrence of nonspecific reaction is <NUM>%, and, at the maximum value (S value=<NUM> in the example illustrated in <FIG>) of the S value, the possibility of occurrence of nonspecific reaction is <NUM>%.

In step S82, the controller <NUM> calculates the S value based on the function of mathematical expression <NUM> for the data group, about progress of antigen-antibody reaction, obtained from the biological sample to be analyzed. In step S83, the controller <NUM> converts the S value obtained in step S82 to a probability of occurrence of nonspecific reaction in the biological sample to be analyzed. The S value can be converted to the probability of occurrence of nonspecific reaction, by, for example, assigning the S value to a linear regression expression generated from the most frequent value·probability (=<NUM>%) in the S value distribution and the maximum value·probability (=<NUM>%) in the S value distribution.

In measurement of D-dimer, presence or absence of occurrence of nonspecific reaction was confirmed by an immunoglobulin absorption test. A prediction accuracy in the analysis method of the present embodiment was evaluated by using <NUM> biological samples for which occurrence of nonspecific reaction was confirmed by the immunoglobulin absorption test, and <NUM> biological samples for which occurrence of nonspecific reaction was not confirmed by the immunoglobulin absorption test. In the deep learning algorithm, a data group set composed of the data group D11 corresponding to the first wavelength, the data group D12 corresponding to the second wavelength, the data group D13 corresponding to the third wavelength, and the data group D15 corresponding to the fifth wavelength was inputted. <FIG> and <FIG> illustrate the result thereof. <FIG> illustrates a histogram in which the horizontal axis represents a probability (<NUM> to <NUM>), of occurrence of nonspecific reaction, outputted from the deep learning algorithm, the horizontal axis is divided at intervals of <NUM>, and the vertical axis represents a frequency of the biological samples belonging to each divided range. The height of the gray bar indicates a frequency of biological samples for which occurrence of nonspecific reaction was confirmed by the immunoglobulin absorption test, and the height of the white bar indicates a frequency of biological samples for which occurrence of nonspecific reaction was not confirmed by the immunoglobulin absorption test. <FIG> illustrates an ROC curve obtained as a result of an ROC analysis of the biological samples illustrated in <FIG>.

Claim 1:
A computer-implemented detection method for detecting occurrence of nonspecific reaction in analysis for an antigen or an antibody contained in a biological sample with use of a measurement reagent containing an antibody or an antigen that causes antigen-antibody reaction with the antigen or the antibody in the biological sample, the detection method comprising:
generating a data group about progress of antigen-antibody reaction between the antigen or the antibody contained in the biological sample and the antibody or the antigen contained in the measurement reagent;
inputting the data group to a deep learning algorithm; and
generating information about occurrence of nonspecific reaction, based on a result outputted by the deep learning algorithm, wherein
the generating of the data group comprises receiving detection information indicating an intensity of detected light that is detected through a measurement sample to which light is applied for a predetermined time period, and generating the data group from the detection information having been received, wherein each piece of data included in the data group expresses an intensity of the detected light that is detected at each measurement time point in the predetermined time period.