Flow path anomaly detection system, automated analyzer, and method for detecting anomalies in flow paths

Abstract

This invention provides a flow path abnormality detection system, an automated analyzer, and a method for detecting flow path abnormalities, all capable of automatically detecting abnormalities in the flow path of an automated analyzer. [Solution] A channel anomaly detection system that detects anomalies based on data obtained from an automatic analyzer 1 comprising: a sensor 11 that applies voltage to a reaction solution obtained by antigen-antibody reaction of a sample and a reagent to cause an electrochemical reaction; a channel F1 for introducing the reaction solution and an auxiliary reagent for energization to the sensor 11; and a photoelectric conversion sensor PT that measures the emission intensity due to the electrochemical reaction. The system comprises: a processor 42 that calculates feature quantities from time-series electrical signal data during the electrochemical reaction period and identifies anomalies in the channel F1 from the feature quantities; and a data output interface 44 that outputs a signal related to the result of the anomaly identified by the processor 42. The processor 42 obtains feature quantities from the acquired electrical signal data based on the emission intensity measured by the photoelectric conversion sensor PT.

Description

Technical Field

[0001] The present invention relates to a flow path abnormality detection system for detecting an abnormality in a flow path of an automatic analyzer, an automatic analyzer, and a method for detecting an abnormality in a flow path.

Background Art

[0002] Patent Document 1 describes a method for predicting a failure state of an automatic analyzer for analyzing a biological sample, which includes a step of obtaining a prediction algorithm for predicting a failure state of the automatic analyzer, the prediction algorithm being configured to predict a failure state of the automatic analyzer based on calibration data and / or quality control data generated by the automatic analyzer, a step of obtaining calibration data and / or quality control data of the automatic analyzer, and a step of processing the calibration data and / or quality control data by using the prediction algorithm to predict a failure state of the automatic analyzer.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An automatic analyzer is generally composed of more than a thousand types of parts.

[0005] In order to reduce the downtime and maintenance costs of an automatic analyzer, it is necessary to quickly identify the failure location when some parts fail.

[0006] Patent Document 1, mentioned above, discloses a technology for generating a failure prediction algorithm from data related to the occurrence of failures in an automated analyzer, and using this algorithm to predict failures such as component damage based on data from at least one of the calibration and quality control of the automated analyzer.

[0007] In this context, automated analyzers may be equipped with sensors for measuring the sample, flow paths for supplying and discharging liquids such as the sample, reagents, and washing solutions, and containers for storing the liquids.

[0008] When a malfunction occurs in the device, it is difficult to detect a specific malfunction among these numerous components, resulting in increased maintenance effort. In particular, there is a need to automatically detect malfunctions in the flow path system, which significantly disrupt the normal operation of the device, during maintenance.

[0009] The present invention aims to provide a flow path abnormality detection system, an automatic analyzer, and a flow path abnormality detection method that can automatically detect abnormalities in the flow path of an automatic analyzer. [Means for solving the problem]

[0010] The present invention includes multiple means for solving the above problems, but to give one example, a flow path abnormality detection system that detects abnormalities based on data obtained from an automated immunoassay analyzer comprising: a container for applying voltage to a reaction solution obtained by antigen-antibody reaction of a sample and a reagent to cause an electrochemical reaction; a flow path for introducing the reaction solution and an auxiliary reagent for energization into the container; and a photoelectric conversion sensor for measuring the luminescence intensity due to the electrochemical reaction, comprising: a calculation processing unit that calculates a feature quantity from time-series electrical signal data during the period in which the electrochemical reaction is being carried out and identifies an abnormality in the flow path from the feature quantity; and a data output unit that outputs a signal related to the result of the abnormality identified by the calculation processing unit, wherein the calculation processing unit determines the feature quantity based on the luminescence intensity measured by the photoelectric conversion sensor from the acquired electrical signal data. [Effects of the Invention]

[0011] According to the present invention, abnormalities in the flow path of an automated analyzer can be automatically detected. Other problems, configurations, and effects will be clarified by the following description of the embodiments. [Brief explanation of the drawing]

[0012] [Figure 1] A schematic plan view showing one example configuration of an automated analyzer to which the flow path abnormality detection system according to the first embodiment is applied. [Figure 2] Schematic diagram of the sensor used for sample testing in the automated analyzer shown in Figure 1. [Figure 3] A schematic diagram of a sample inspection sensor installed in an automated analyzer that uses the electrochemiluminescence method shown in Figure 1 to inspect samples. [Figure 4] Figure 1 shows a timing chart illustrating an example of the detection process in an automated analyzer. [Figure 5] Figure 1 is a block diagram showing the processing flow in the automated analyzer. [Figure 6] An example of time-series data of luminescence intensity measured during sample measurement. [Figure 7] An example of time-series data of voltage values ​​measured during sample measurement. [Figure 8] An example of time-series data of current values ​​measured during sample measurement. [Figure 9] This figure shows an example of time-series data of resistance values ​​measured during sample measurement. [Figure 10] A block diagram showing the processing flow for component abnormality detection in the flow path abnormality detection system according to the first embodiment. [Figure 11] A flowchart showing the detailed steps of the flow in Figure 10. [Figure 12] This figure shows an example of an anomaly detection result notification in the case of a flow path anomaly in the flow path anomaly detection system according to the first embodiment. [Figure 13] This figure shows an example of an anomaly detection result notification in the case of no flow path anomaly in the flow path anomaly detection system according to the first embodiment. [Figure 14]A diagram showing an example of a display screen in the flow path abnormality detection system according to the first embodiment. [Figure 15] A diagram showing an example of a detailed display screen of abnormality information for a user with maintenance knowledge in the flow path abnormality detection system according to the first embodiment. [Figure 16] A diagram showing an example of a detailed display screen of alarm list abnormality information for a user without maintenance knowledge in the flow path abnormality detection system according to the first embodiment.

Embodiments for Carrying Out the Invention

[0013] Embodiments of the flow path abnormality detection system, automatic analyzer, and flow path abnormality detection method of the present invention will be described below with reference to the drawings. In the drawings used in this specification, the same or corresponding components are denoted by the same or similar reference numerals, and repeated description of these components may be omitted.

[0014] (Overview) The flow path abnormality detection system according to the present embodiment is a technology for detecting an abnormality in a flow path system that sucks and discharges liquid to and from a sensor for liquid sample inspection of an automatic analyzer.

[0015] The sensor for liquid sample inspection provided in the automatic analyzer is, for example, a sensor used for measuring analysis items of biological samples such as a biochemical analyzer, an immunoassay analyzer, a blood coagulation time measurement device, an ISE measurement device, etc.

[0016] As an example of such a sensor, a flow cell type sensor for electrochemiluminescence measurement can be mentioned. Although the details will be described later, the flow cell type sensor includes, for example, a flow cell, electrodes (working electrode, counter electrode, and reference electrode) provided in the flow cell, and a photoelectric conversion sensor (such as a photomultiplier tube) disposed on the side opposite to the working electrode with the flow cell interposed therebetween.

[0017] This type of sensor outputs not only measured values ​​of analytical parameters in a sample that indicate the concentration of the target component (for example, signal values ​​indicating the light emission intensity detected by a photoelectric conversion sensor), but also analog electrical signal data (current value, voltage value, and resistance value) generated between the counter electrode and the reference electrode when a voltage is applied between the working electrode and the reference electrode, as response values.

[0018] In automated analyzers, sensor outputs (analog electrical signal data output from the sensor) are generated at multiple timings during the measurement cycle of the same sample, such as during sample measurement, sensor cleaning, and electrode conditioning.

[0019] The liquid channel for transporting biological samples, reagents necessary for measurement, washing solutions, etc., to the above-mentioned sensor is composed of a nozzle for drawing in and discharging liquid, piping for passing liquid, etc. Other components of the liquid transport system may include, for example, multiple valves for opening and closing the channel, and syringes for creating a pressure difference in the channel to draw in and discharge liquid. However, the detection target of the abnormality detection system of the present invention is the liquid channel portion to the sensor.

[0020] Furthermore, the liquid transported by the liquid channel includes samples, reagents, reaction solutions obtained by reacting samples and reagents, and washing solutions. Samples include at least one (or more) types of patient specimens (blood, urine, etc.), calibration samples (standard samples), QC (quality control) samples, and dummy samples. Calibration samples are prepared samples measured to create a calibration curve during the calibration of the automated analyzer. QC samples are prepared samples measured during QC of the automated analyzer. Dummy samples are predetermined samples measured as a preparatory step before measuring patient specimens.

[0021] As a result of diligent research by the inventors, it was discovered that when an abnormality occurs in the liquid flow path of an automated analyzer, electrical signal data such as the current waveform generated at the electrodes and the signal waveform output from the photoelectric conversion sensor change significantly, and in particular, the peak of the waveform decreases compared to the normal state.

[0022] For example, if a malfunction occurs in the liquid flow path, hindering the replacement of liquid inside the flow cell, and resulting in the absence of liquid that should be present inside the flow cell at the appropriate time, this can be a cause of abnormal electrical signal data. It can be said that there is a certain causal relationship between this abnormal electrical signal data and abnormalities in the liquid flow path system. Therefore, it is expected that abnormalities in the flow path can be detected by analysis based on features calculated from electrical signal data, such as the peak value of the electrical signal data or the value at a certain point in time.

[0023] Furthermore, the concept of detecting abnormalities in liquid flow paths, as described above, is not necessarily limited to processing by some kind of computing device (e.g., a processor); it can also be implemented as a method. For example, a user could look at the log data of the sensor output and determine whether or not there is an abnormality in the liquid flow path based on changes in the electrical signal data.

[0024] The present invention can be applied to automated analyzers. Examples of detection units mounted on automated analyzers include biochemical analyzers and immunoassay analyzers. However, this is merely an example, and the present invention is not limited to the embodiments described below, but can be broadly applied to automated analyzers equipped with a detection unit that analyzes a sample based on reagents and reaction results. For example, automated analyzers equipped with mass spectrometers used in clinical tests or coagulation analyzers that measure blood clotting time may also be included as applicable.

[0025] Furthermore, the present invention is also applicable to a combined automatic analyzer equipped with multiple types of these detection units, as well as to an automatic analyzer that includes at least one automatic analyzer.

[0026] <First Embodiment> A first embodiment of the flow path abnormality detection system, automatic analysis device, and flow path abnormality detection method of the present invention will be described with reference to Figures 1 to 16.

[0027] -Automatic analyzer- First, the overall configuration of the automated analyzer will be explained using Figure 1. Figure 1 is a schematic plan view showing one example of the configuration of an automated analyzer to which the component abnormality detection system according to the first embodiment is applied.

[0028] The automated analyzer 1 shown in Figure 1 comprises a transport line 2, an incubator (reaction disk) 3, a first transport mechanism 4, a sample dispensing nozzle 6, a reagent disk 7, a reagent dispensing nozzle 8, a second transport mechanism 9, a detection unit 10, a controller 21, an operating device 22, and a control device 30.

[0029] The transport line 2 is a device that transports the rack R, and transports the rack R to the sample dispensing position by the sample dispensing nozzle 6. Multiple sample containers C1 for holding samples can be installed on the rack R. In the example in Figure 1, a configuration in which samples are transported along a line is illustrated, but in some cases, a disc-shaped transport unit that rotates to transport the samples may be provided.

[0030] Incubator 3 is a rotary table-shaped device that houses reaction vessels C2, which are responsible for containing the reaction solution. Multiple reaction vessels C2 can be arranged in a ring shape. Incubator 3 is driven by a drive device (not shown) and rotates, allowing any of the reaction vessels C2 to be moved to any of several predetermined positions, such as the dispensing position by the sample dispensing nozzle 6.

[0031] The first transport mechanism 4 is a device for transporting sample dispensing tips T and reaction vessel C2. This first transport mechanism 4 is movable in the three axes XYZ along a rail and transports sample dispensing tips T and reaction vessel C2 between the incubator 3, stirring mechanism M, disposal position D, tip mounting position P, and tray 5. The stirring mechanism M is a device for stirring the sample contained in reaction vessel C2. Disposal position D is a position equipped with a disposal hole for discarding used sample dispensing tips T and reaction vessel C2. Tip mounting position P is a position for mounting sample dispensing tips T onto the sample dispensing nozzle 6.

[0032] Tray 5 is a container that holds multiple unused sample dispensing tips T and reaction vessels C2. Unused reaction vessels C2 picked up from Tray 5 are placed in a designated position in Incubator 3 by the first transport mechanism 4. Similarly, unused sample dispensing tips T picked up from Tray 5 are transported by the first transport mechanism 4 and placed in the tip mounting position P.

[0033] The sample dispensing nozzle 6 is a device for aspirating and dispensing a sample. This sample dispensing nozzle 6 is configured to rotate and move up and down. The nozzle tip is moved above the tip mounting position P and lowered, and the sample dispensing tip T prepared at the tip mounting position P is pressed into the nozzle tip and attached. Once the sample dispensing tip T is attached to the nozzle tip, the sample dispensing nozzle 6 moves its nozzle tip above the sample container C1 installed in the rack R and lowers it, aspirating a predetermined amount of sample from the sample container C1. After aspirating the sample, the sample dispensing nozzle 6 moves its nozzle tip above the incubator 3 and lowers it, dispensing the sample into an unused reaction vessel C2 installed in the incubator 3. When the dispensing of the sample is complete, the sample dispensing nozzle 6 moves its nozzle tip above the disposal position D and discards the used sample dispensing tip T into the disposal hole.

[0034] The reagent disk 7 is a rotating table-shaped device on which multiple reagent containers C3 are placed. The top of the reagent disk 7 is covered by a disk cover 7a (shown partially broken in Figure 1), and the inside is kept warm at a predetermined temperature. The disk cover 7a has an opening (omitted for illustrative purposes) at a reagent aspiration position 7b set near the incubator 3.

[0035] The reagent dispensing nozzle 8 is a device for aspirating and dispensing reagents. Like the sample dispensing nozzle 6, the reagent dispensing nozzle 8 can rotate and move up and down. It moves its nozzle tip to the reagent aspiration position 7b on the reagent disk 7 and lowers it, aspirating a predetermined amount of reagent from a predetermined reagent container C3 that has been transported to the reagent aspiration position 7b. Next, the reagent dispensing nozzle 8 lifts its nozzle tip from the reagent container C3, moves it to a predetermined position on the incubator 3 and lowers it, dispensing the reagent into the reaction vessel C2 containing the sample that has been transported to this position.

[0036] The reaction vessel C2, into which the sample and reagents have been injected, is transported to a predetermined position by the rotation of the incubator 3 and then transferred to the stirring mechanism M by the first transport mechanism 4. The stirring mechanism M mixes the sample and reagents inside the reaction vessel C2 by, for example, rotating the reaction vessel C2. After stirring is complete, the reaction vessel C2 is again transferred to a predetermined position on the incubator 3 by the first transport mechanism 4.

[0037] The second transport mechanism 9 is a device that transfers the reaction vessel C2 between the incubator 3 and the detection unit 10, and is configured to rotate and move up and down. This second transport mechanism 9 picks up the reaction vessel C2 containing the reaction solution, which has been returned to the incubator 3 after mixing the sample and reagents and after a predetermined reaction time has elapsed, and transfers it to the detection unit 10.

[0038] The detection unit 10 is a measuring instrument that measures specific biological components, chemical substances, and other measurement items contained in the reaction liquid inside the reaction vessel C2. In this embodiment, the object of anomaly detection is the liquid flow path (described later) used by this detection unit 10.

[0039] The controller 21 is a computer attached to (forming a unit with) the mechanism 91 of the automatic analyzer 1 (the disk, transport mechanism, dispensing nozzle, detection unit 10, etc., as described above). The controller 21 controls the mechanism 91 of the automatic analyzer 1 in response to signals input from the operating device 22 and signals input from the control device 30 in response to user operations.

[0040] The control device 30 is a computer comprising a storage device 31 such as RAM, ROM, HDD, or SSD, a processor 32 such as a CPU, and is connected to the mechanical unit 91 of the automatic analyzer 1 via the controller 21. This control device 30 controls each device in the mechanical unit 91 of the automatic analyzer 1 and records and processes data input from the detection unit 10, etc.

[0041] The control device 30 may, for example, form a unit with the mechanism 91 or controller 21 of the automatic analyzer 1, or it may be installed separately from the mechanism 91 of the automatic analyzer 1 and connected directly to the controller 21 by wire or wireless connection.

[0042] In this embodiment, the control device 30 is connected to the server 40 via a communication interface 33, a network NW, and a data acquisition interface 43. The server 40 is also a computer composed of a storage device 41 such as RAM, ROM, HDD, or SSD, and a processor 42 such as a CPU. In this embodiment, the abnormality detection function for components of the liquid transport system used in the automatic analyzer 1 is installed in the server 40. The server 40 records the electrical signal data output by the sensor 11 of the automatic analyzer 1 in the storage device 41, processes the data recorded in the storage device 41 with the processor 42, and detects abnormalities in the components of the liquid transport system of the automatic analyzer 1 based on the sensor output. The details will be described later.

[0043] The control processes for execution described below are performed by programs, but they may be combined into a single program, divided into multiple programs, or even a combination of these.

[0044] Some or all of the programs contained within each device may be implemented using dedicated hardware, or they may be modularized. Furthermore, various programs may be installed on each device via a program distribution server or external storage media, or existing devices may be updated.

[0045] Furthermore, each device may be an independent device connected by a wired or wireless network, or two or more devices may be integrated into a single unit.

[0046] -Liquid Conveying System- Figure 2 is a schematic diagram of the liquid transport system installed in the automated analyzer shown in Figure 1.

[0047] As shown in Figure 2, the detection unit 10 of the automatic analyzer 1 is equipped with a flow cell type sensor 11, a liquid transport system 12, and a turntable 13. Here, the configuration of the turntable 13 and the liquid transport system 12 will be described.

[0048] The turntable 13 is the section where the auxiliary reagent container RG for storing auxiliary reagents and the detergent container CL for storing washing solution are installed, and is equipped with a standby position SP and a reaction vessel installation position SM.

[0049] The auxiliary reagent is a chemical solution used to induce a luminescence reaction of the reaction product in the reaction solution. The washing solution is a liquid used to clean the flow path of the liquid transport system 12 and the flow cell FC of the sensor 11.

[0050] The reaction vessel C2, which is transferred from the incubator 3, is placed in the reaction vessel installation position SM. The turntable 13 is equipped with a drive device (not shown) and rotates and moves up and down when driven by a drive device controlled by a signal from the controller 21. The turntable 13 is used to transport, for example, the reaction vessel C2, detergent container CL, or auxiliary reagent container RG to the liquid suction position of the liquid transport system 12 in an appropriate time, or to align it with the standby position SP.

[0051] The liquid transport system 12 consists of flow paths F1, F2, F3, F4, F5, F6 through which the liquid passes, a plurality of valves V1, V2 for opening and closing these flow paths F1, F2, F3, F4, F5, F6, and a syringe SY that creates a pressure difference in the flow paths F1, F2, F3, F4, F5, F6 for aspirating and discharging the liquid.

[0052] The channel F1 is a channel that sends the aspirated liquid to the sensor 11. It consists of a nozzle for aspirating and releasing the liquid and a pipe for passing the liquid, with the other end connected to the sensor 11. This channel F1 is a channel that introduces the reaction solution and auxiliary reagents for energization to the sensor 11. It consists of a nozzle or pipe that transports the reaction solution from the reaction vessel C2 to the sensor 11, and a nozzle or pipe that transports the auxiliary reagents from the auxiliary reagent container RG to the sensor 11.

[0053] Flow path F2 is connected to sensor 11 on the opposite side from flow path F1, and connects sensor 11 to valve V1. Flow path F3 connects valves V1 and V2. Flow path F4 connects valve V2 to the drain tank (not shown). Flow path F5 branches off from flow path F3 and connects flow path F3 to syringe SY. Flow path F6 connects syringe SY to the system water supply pump (not shown).

[0054] Valves V1 and V2 are, for example, solenoid valves. Normally open solenoid valves can be used, but in this embodiment, normally closed solenoid valves are used.

[0055] For example, if a reaction vessel C2, auxiliary reagent container RG, or detergent container CL is transported to the suction position of the liquid transport system 12, and valve V1 is opened with valve V2 closed to drive the syringe SY to suction, liquid is drawn from the container such as reaction vessel C2 via the suction nozzle. As a result, the liquid is drawn into the sensor 11 via flow path F1, and further into flow paths F3 and F5. Alternatively, if valve V1 is opened with valve V1 closed and the syringe SY is driven to discharge, the liquid drawn into flow paths F3 and F5 is discharged into the drain tank.

[0056] In this embodiment, the abnormality detection system detects the portion of the flow path F1 used to transport liquid to the sensor 11 mentioned above.

[0057] -Sensor- Figure 3 is a schematic diagram of the sensor used for measuring a sample in the automated analyzer shown in Figure 1. The detection unit 10 of the automated analyzer 1 is equipped with a flow cell type sensor 11.

[0058] Sensor 11 is the part that applies a voltage to a reaction solution obtained by antigen-antibody reaction between a sample and a reagent to cause an electrochemical reaction, and includes a flow cell FC, three electrodes (working electrode E1, counter electrode E2, and reference electrode E3) located inside the flow cell FC, and a photoelectric conversion sensor PT, which measures the emission intensity of the reaction product RP produced by the electrochemical reaction, and is composed of, for example, a photomultiplier tube.

[0059] The working electrode E1, counter electrode E2, and reference electrode E3 are each controlled by a potentiostat 15 to achieve the desired voltage. When the reaction product RP of the sample and reagent in the reaction solution is collected on the working electrode E1, a specific voltage is applied between the working electrode E1 and the reference electrode E3 by the potentiostat 15, causing an electrochemical reaction to occur and the reaction product RP to emit light. A photoelectric conversion sensor PT is positioned on the opposite side of the flow cell FC from the working electrode E1 (upper side in Figure 3), and this photoelectric conversion sensor PT detects the light emission intensity of the reaction product RP.

[0060] The light emission intensity detected by the photoelectric conversion sensor PT is digitized by the A / D converter 18 and, after raw data recording processing P1, is recorded as raw data of the measured value of the measured item in the storage device 31 or the storage device of the sensor 11, along with the measurement date and time.

[0061] In this case, the current, voltage, and resistance values ​​generated between the counter electrode E2 and the reference electrode E3 by applying a voltage between the working electrode E1 and the reference electrode E3 are measured by the potentiostat 15. In this embodiment, not only the output of the photoelectric conversion sensor PT, but also the applied voltage values ​​to the working electrode E1 and the reference electrode E3, and the current, voltage, and resistance values ​​generated between the counter electrode E2 and the reference electrode E3 are recorded in the raw data recording process P1.

[0062] - Operation of automated analyzers - Calibration and QC measurements are performed in a timely manner to enable high-precision qualitative and quantitative analysis of target components contained in patient samples, which are unknown samples. For example, in the case of quantitative analysis, automated analyzer 1 is operated daily according to the following procedures (1)-(5).

[0063] (1) Equipment startup First, the power is turned on to start up the automatic analyzer 1. Then, the reagent container C3 is set up and the reagents are initially filled, the internal temperature of the reagent disk 7 is adjusted, a constant voltage is applied to the electrode to continuously measure the internal standard solution, and the potential of the electrode of the sensor 11 is checked to ensure it is stable, and maintenance is performed as needed.

[0064] (2) Calibration High-concentration and low-concentration standard samples of the analyte (component to be measured) with known concentrations are measured. These measurements are used to create a relationship equation (calibration curve) between the concentration of the analyte and the output of sensor 11 (photoelectric conversion sensor PT). However, the calibration frequency varies depending on the analyte; for example, calibration for each analyte is performed periodically (e.g., monthly cycle) in sequence.

[0065] (3) QC measurement Multiple QC samples with different concentration levels, where the possible concentration ranges of the measurement items are known, are measured, and the concentration of the measurement item in the QC samples is calculated using a calibration curve created during calibration. The appropriateness of the calibration curve is confirmed by checking whether the calculated concentration is within the known concentration range of the QC samples. QC measurements are performed frequently because they serve as a condition check to guarantee the measurement results of patient specimens. For example, QC measurements are performed 1-3 times per day in parallel for multiple measurement items.

[0066] (4) Measurement of patient specimens Patient samples with unknown concentrations of the target parameters are measured, and the concentrations of the target parameters are calculated using a calibration curve. Before measuring these patient samples, a so-called background measurement or dummy measurement may be performed to check the status of the automated analyzer 1.

[0067] (5) Device shutdown If necessary, clean and inspect each part of the automatic analyzer 1, then turn off the power and shut down the automatic analyzer 1.

[0068] -Measurement cycle- Figure 4 is a timing chart showing the measurement cycle in the automated analyzer shown in Figure 1.

[0069] As described above, calibration, QC measurement, and patient sample measurement are performed in a timely manner from the time the device is started up until it is shut down, and the sample (patient sample, standard sample, QC sample, or dummy sample, etc.) is measured in each process. The measurement operation in each process is carried out in a series of cycles, as shown in Figure 4, consisting of electrode conditioning, sample introduction, measurement, and cleaning.

[0070] For example, in the electrode conditioning process, as shown in Figure 4, the auxiliary reagent container RG is transported to the suction position of the liquid transport system 12 by the operation of the turntable 13. When the auxiliary reagent container RG reaches the suction position, valve V1 opens with valve V2 closed, syringe SY is driven, and the auxiliary reagent is drawn from the auxiliary reagent container RG and introduced into the flow cell FC.

[0071] Simultaneously with the aspiration of the auxiliary reagent by syringe SY, a specific voltage pattern is applied to the electrode by potentiostat 15 for a certain period of time, preparing the electrode for measurement. Once the electrode conditioning is complete, valves V1 and V2 are closed, stopping the aspiration by syringe SY and the application of voltage to the electrode. The sensor output during the period when voltage was applied to the electrode is also recorded.

[0072] In the subsequent sample introduction step, reaction vessel C2, which is installed at reaction vessel installation position SM, is transported to the suction position of the liquid transport system 12 by the operation of the turntable 13. When reaction vessel C2 reaches the suction position, valve V1 opens while valve V2 remains closed, syringe SY is driven, and the reaction liquid is drawn from reaction vessel C2 and introduced into flow cell FC.

[0073] Furthermore, with valve V1 still open, the auxiliary reagent container RG is transported to the suction position of the liquid transport system 12 by the operation of the turntable 13. When the auxiliary reagent container RG reaches the suction position, the syringe SY is driven to draw the auxiliary reagent from the auxiliary reagent container RG, and the auxiliary reagent is introduced into the flow cell FC. Once the sample introduction process is complete, both valves V1 and V2 are closed, and the suction operation by syringe SY is stopped.

[0074] In the measurement process, the turntable 13 moves the standby position SP to the suction position of the liquid transport system 12, and the potentiostat 15 applies the voltage necessary for the luminescence reaction of the reaction product RP captured on the working electrode E1 in the sample introduction process. The sensor output is also recorded while the voltage is applied to the electrode.

[0075] In the subsequent washing process, valve V2 opens while valve V1 remains closed, driving syringe SY to discharge auxiliary reagents, etc., into the drain tank. After that, both valves V1 and V2 close, and the detergent container CL is transported to the suction position of the liquid transport system 12 by the operation of the turntable 13. When the detergent container CL reaches the suction position, valve V1 opens while valve V2 remains closed, driving syringe SY to draw cleaning solution from the detergent container CL, and the cleaning solution is introduced into the flow cell FC.

[0076] During this time, to prevent reaction product RP from remaining in the flow cell FC, a voltage with a different pattern than that used in the electrode conditioning process is applied to the electrodes for a certain period of time while the cleaning solution flows through the flow cell FC. Applying voltage to the electrodes causes the reaction product RP and other substances adhering to the electrodes to detach, and the detached reaction product RP is washed away by the cleaning solution and discharged from the flow cell FC. In this cleaning process as well, the sensor output is recorded while voltage is applied to the electrodes, and both valves V1 and V2 are closed once the suction of the cleaning solution is complete.

[0077] Subsequently, the turntable 13 moves the standby position SP to the suction position of the liquid transport system 12, valve V2 opens with valve V1 closed, and the cleaning fluid is discharged into the drain tank by syringe SY. Finally, valve V2 closes, returning to the state before the start of the electrode conditioning process.

[0078] Since the voltage settings applied to the electrodes differ in each step of electrode conditioning, measurement, and cleaning, the voltage applied to the electrodes at the timing specified by the potentiostat 15 during the same measurement cycle is controlled. In the sensor 11, complex voltage patterns are precisely and repeatedly applied to the electrodes in accordance with the measurement of the sample (patient sample, standard sample, QC sample, dummy sample, etc.), and electrical signals such as voltage, current, and resistance generated at the electrodes, as well as measured values ​​of the concentration of the analyte, are output.

[0079] -Data Processing Flow (Automated Analyst)- The automated analyzer 1 records an alarm if an abnormality is detected during the electrode conditioning, measurement, or cleaning process.

[0080] Specifically, an alarm is recorded when the measured value (concentration of the analyte) falls outside the appropriate range (high or low), when the EV value of the current generated when voltage is applied is higher than the appropriate value, and when the EV value of the luminescence of the reaction product RP is lower than the appropriate value.

[0081] For example, the current value measured during the electrode conditioning process may increase from a normal value (e.g., around 10 mA) to (e.g., around 15 mA) if the liquid inside the flow cell FC has not been replaced from the washing solution to the auxiliary reagent.

[0082] Figure 5 is a block diagram showing the processing flow in the automated analyzer shown in Figure 1. Data is input to the control device 30 of the automated analyzer 1 from the sensor 11, the mechanism unit 91, the sample data reader 92, the reagent data reader 93, and the user interface 94.

[0083] The control device 30 receives data recorded in the raw data recording process P1 (Figure 3) as input from the sensor 11. The data input from the sensor 11 to the control device 30 includes not only the measurement cycle data for patient sample measurement, but also the measurement cycle data for each measurement type, such as QC measurement and calibration, as well as dummy measurement data performed as a preparatory operation immediately before measuring the patient sample.

[0084] As mentioned above, the mechanism unit 91 is a collective term for the various hardware components (such as the sample dispensing nozzle 6 and the incubator 3) mounted on the automatic analyzer 1. The data input from this mechanism unit 91 to the control device 30 includes, for example, the operating timing, operating amount, and current value of each motor, the signals from sensors used to control each motor, and log data such as the opening and closing timing and current value of fluid valves (valves V1, V2, etc.).

[0085] The sample data reader 92 is a device that reads registered sample data, such as a barcode or RFID reader, and is installed in the automated analyzer 1. The sample container C1 is equipped with a storage medium such as a barcode or RFID, and the sample data recorded on the storage medium is read by the sample data reader 92. The data read by the sample data reader 92 and input to the control device 30 is, for example, the sample ID.

[0086] The reagent data reader 93 is a device equipped with a barcode or RFID reader, for example, to read registered reagent data, and is installed in the automated analyzer 1. The reagent container C3 is fitted with a storage medium such as a barcode or RFID, and the reagent data recorded on the storage medium is read by the reagent data reader 93. The data read by the reagent data reader 93 and input into the control device 30 includes, for example, the reagent ID, lot number, and expiration date.

[0087] The user interface 94 consists of a monitor and input devices provided in the automated analyzer 1, and is used by the user to view data and input data into the control device 30. Various types of data can be input into the control device 30 using the user interface 94, but examples of data related to reagents and auxiliary reagents include reagent ID, lot number, expiration date, onboard expiration date, and required remaining amount. Examples of data related to samples include sample ID, measurement type for the sample ID (patient sample measurement, QC measurement, calibration, dummy measurement, etc.), and measurement items.

[0088] The various data input from the sensor 11 and other devices to the control device 30 are processed in real time by the processor 32 and sent to the server 40 via the communication interface 33 as log files. The processes executed by the processor 32 include, for example, sensor output conversion processing P2 and sensor output recording processing P3 for sensor output. Other processes executed by the processor 32 include operation log recording processing P4, reagent data recording processing P5, sample data recording processing P6, alarm data recording processing P7, and log file generation processing P8. Each process will be described sequentially below.

[0089] • Sensor output conversion processing In the sensor output conversion process P2, the processor 32 converts the sensor output (raw data) input from the sensor 11 into valid values. The sensor output input from the sensor 11 consists of raw data for light emission intensity, current value, voltage value, and resistance value.

[0090] Here, Figure 6 shows an example of time-series data of luminescence intensity measured during sample measurement, Figure 7 shows an example of time-series data of voltage value, Figure 8 shows an example of time-series data of current value, and Figure 9 shows an example of time-series data of resistance value. The horizontal axis in each figure corresponds to time, and the time change of each value is shown. During sample measurement, the control device 30 applies voltage to the electrodes at a specific timing from the start of measurement to acquire data. In the examples shown in Figures 6 to 9, in all cases a predetermined voltage is applied to the working electrode E1 and the reference electrode E3 at the same timing (in this embodiment, 10 ms after the midpoint of the sensor output cycle).

[0091] Under these control characteristics, in the sensor output conversion process P2, the processor 32 converts the sensor output (raw data) input from the sensor 11 for each measurement into an EV value (effective value) using the following equations (1) and (2) which are pre-stored in the storage device 31 (e.g., ROM).

[0092]

number

[0093]

number

[0094] • Sensor output recording processing In the sensor output recording process P3, the processor 32 assigns a measurement ID for each measurement and records the raw data and valid values ​​of the measured values ​​in the storage device 31, linked to the measurement ID.

[0095] • Operation log recording process In the operation log recording process P4, the processor 32 records the operation logs input from the mechanical unit 91 and the sensor 11 in the storage device 31. The operation logs input from the mechanical unit 91, etc., include, for example, the operating timing and amount of each motor, the motor current value, the sensor signals for controlling the motor operation, and the opening and closing timing and current value of fluid valves (valves V1, V2, etc.).

[0096] • Reagent data recording and processing In reagent data recording processing P5, the processor 32 compares the reagent data input from the reagent data reader 93 with condition data pre-recorded in the storage device 31, and records it in the storage device 31 as a usable reagent if it matches the condition data. The condition data used to compare the reagent data includes the reagent ID, lot number, expiration date, onboard expiration date, required remaining amount, etc., and is input via the user interface 94 or another computer, input to the control device 30 via the communication interface 33, and recorded in the storage device 31. In addition, in reagent data recording processing P5, the processor 32 records the history of used reagents in the storage device 31 for each measurement ID. As a result, the raw data and valid values ​​of the measured values ​​and the data of the reagents used in the measurement are linked via the measurement ID.

[0097] • Sample data recording and processing In the sample data recording process P6, the processor 32 compares the sample data input from the sample data reader 92 with condition data previously recorded in the storage device 31. If the sample matches the condition data, it is recorded in the storage device 31 as a sample that can be measured, and measurement is performed in a timely manner.

[0098] The condition data used to match sample data includes the sample ID, measurement type (QC measurement, patient sample measurement, etc.), and measurement items. This data is entered via the user interface 94 or another computer, input to the control device 30 via the communication interface 33, and recorded in the storage device 31.

[0099] • Alarm data recording process In the alarm data recording process P7, the processor 32 determines whether there is an abnormality in the measurement each time a measurement is taken (each time a control voltage is applied between the working electrode E1 and the reference electrode E3 of the sensor 11). If there is an abnormality in the measurement, alarm data is added to the dataset related to the measurement that was determined to be abnormal and recorded in the storage device 31.

[0100] Whether or not there is an abnormality in the measurement is determined by comparing the measured value (concentration of the analyte), the EV value of the current generated when a control voltage is applied to the electrode, and the EV value of the amount of light emitted by the reaction product with preset values.

[0101] More specifically, if the measured value is higher than the set value (upper limit of the appropriate range), or if the measured value is lower than the set value (lower limit of the appropriate range), it is determined that there is an abnormality in the measurement, and an alarm is added to the data set of sensor 11 output acquired in this measurement. Also, if the EV value of the current generated between the counter electrode E2 and the reference electrode E3 is higher than the set value, or if the amount of light emitted by the reaction product RP is lower than the set value, it is determined that there is an abnormality in the measurement, and an alarm is added to the data set of sensor output acquired in this measurement.

[0102] The alarm data includes information about the nature of the anomaly (measured value is too high / measured value is too low / current EV value is too high / luminescence EV value is too low).

[0103] • Log file generation process In the log file generation process P8, the processor 32 generates a log file containing electrical signal data aggregated from the data stored in the storage device 31 for each measurement (each measurement timing in the measurement cycle), including data necessary for detecting abnormalities in the liquid flow path, such as the measurement type. The processor 32 also transmits the log file to the server 40 via the communication interface 33 and the network NW. A log file is created for each measurement and uploaded sequentially, and stored in the server 40. Thus, the electrical signal data used in this embodiment is data stored on the network.

[0104] —Component abnormality detection processing flow (server)— Figure 10 is a block diagram showing the processing flow for component abnormality detection by the server according to the present invention.

[0105] In this embodiment, the component abnormality detection function is performed by the server 40, and the server 40 constitutes an abnormality detection system that detects abnormalities in the flow path F1 based on data obtained from the automatic analysis device 1.

[0106] As shown in Figure 10, the server 40 includes a storage device 41 for storing acquired electrical signal data, a processor 42 for calculating feature quantities from the time-series electrical signal data during the electrochemical reaction and identifying abnormalities in the flow path F1 from the feature quantities, and a data output interface 44 for outputting signals related to the results of the abnormalities identified by the processor 42.

[0107] When the processor 42 receives a log file from the automatic analyzer 1 via the network through the data acquisition interface 43, it stores the log file in the storage device 41 during the log file storage process P21.

[0108] The processor 42 then extracts electrical signal data from the log file stored in the storage device 41, which serves as the basis for detecting abnormalities in the components of the flow path F1. Specifically, it extracts time-series waveform signal data of current and signals measured by the photoelectric conversion sensor PT in QC samples and patient specimens extracted over a specified period.

[0109] In this way, the processor 42 can obtain feature quantities based on the time-dependent current waveform obtained during the electrochemical reaction period from the electrical signal data.

[0110] The processor 42 then stores the extracted electrical signal data in the storage device 41 in the electrical signal data storage process P23.

[0111] Subsequently, in the feature calculation process P24, the processor 42 calculates the features (e.g., peak values ​​or start values) of the time-series waveform signal data (e.g., current waveform signal data or signal waveform signal data) during the set period based on the extracted electrical signal data.

[0112] In this way, the processor 42 can obtain feature quantities from the acquired electrical signal data based on the light emission intensity measured by the photoelectric conversion sensor PT.

[0113] Furthermore, the processor 42 can obtain feature quantities based on a value at a predetermined point in time in the current waveform over time, or on values ​​at multiple points in time in the current waveform over time.

[0114] Although we have explained an example where the peak value of the electrical signal data over time and the start value immediately after voltage application are used as feature quantities, these settings can also be changed as appropriate.

[0115] Furthermore, the processor 42 can calculate the average or integrated value of values ​​from multiple points in time as a feature. For example, it is possible to use the value of electrical signal data at a predetermined point in time, or the values ​​from multiple points in time in the above-mentioned current waveform, or their average or integrated value (effective value) as a feature.

[0116] In the flow path anomaly detection process P25, the processor 42 counts the number of waveforms in which the feature quantity falls below a threshold during the set period, and detects whether or not there is an anomaly in the flow path F1 and the location where the anomaly occurred by comparing the count with the set value.

[0117] The setting period is a fixed period during which the anomaly detection function is operated, for example, the day on which the anomaly detection function is operated or the day before. The peak value is the maximum value in the entire space of the waveform signal data, and the start value is the value of the waveform signal data immediately after voltage is applied (10 ms later in this embodiment). The threshold is either a newly set absolute threshold (e.g., 10) or a relative threshold calculated by the processor 42 based on the waveform signal data during the setting period (e.g., the average maximum value of the current waveform signal data from the previous day). The setting value is a pre-set value (e.g., 5).

[0118] In this way, the processor 42 can calculate feature quantities from the electrical signal data stored in the memory device 41, and from these feature quantities, it can identify whether or not there is an abnormality in the flow path F1 and the abnormality mode.

[0119] The detection results are transmitted via the data output interface 44 to the server user interface 45 and then to the user interface 46 via the network, and the user is notified. In this way, the data output interface 44 outputs the results of identifying the abnormal mode.

[0120] In addition, specific examples of user notifications include displaying the detection results on the server user interface 45 of the server 40, on a user interface 46 at any location, or on the user interface 94 of the control device 30 of the automatic analyzer 1.

[0121] —Detection calculation process— Figure 11 is a flowchart showing the detailed steps of the flow in Figure 10.

[0122] As shown in Figure 11, the processor 42 acquires electrical signal data for the period targeted for anomaly detection from the electrical signal data stored in the storage device 41 (S11). The procedure in step S11 corresponds to the detection data extraction process P22 described in Figure 10.

[0123] Subsequently, the processor 42 performs anomaly detection for multiple patterns and determines whether or not there is an abnormality in the flow path based on the anomaly detection results.

[0124] For example, in Pattern 1, a set of features X1 calculated from electrical signal data for a set period 1 is calculated (S111), and then the features calculated from electrical signal data for a set period 2 are set as threshold 1 (S112). Next, the number of waveforms in set X1 that are below threshold 1 is set as count 1 (S113), and if count 1 is equal to or greater than a preset value of 1, a "red" display is given; otherwise, a "green" display is given (S114).

[0125] In parallel, as Pattern 2, a set of feature quantities Y1 calculated from electrical signal data for a set period of 1 is calculated (S121). Next, the number of waveforms in set Y1 that fall below a predetermined threshold of 2 is counted as Count 2 (S122). If Count 2 is equal to or greater than a predetermined value of 2, a "red" display is given; otherwise, a "green" display is given (S124).

[0126] Finally, it is determined whether the number of "red" indicators exceeds a predetermined specified number. If it is determined that there are more than the specified number of indicators, the system is set to "abnormal" during the abnormality notification period (S151). Otherwise, the system is set to "no abnormality" during the abnormality notification period (S152).

[0127] Finally, the configuration results are sent via the data output interface 44 to the server user interface 45 or to the user interface 46 via the network, and the user is notified.

[0128] Here, Figure 11 shows a flowchart for determining whether there is an abnormality in the flow path F1 in the first embodiment. In actual operation, for example, by comparing the count with multiple set values, it is possible to identify the abnormality location or site in the flow path F2, such as a bend in the nozzle constituting the flow path F1 or a blockage in the piping, using a three-color display of "red," "yellow," and "green" or more colors.

[0129] In this way, the processor 42 can calculate multiple features from electrical signal data and identify abnormal modes by comparing these multiple features. Specifically, the processor 42 can calculate multiple features from electrical signal data and identify nozzle bending or pipe blockage by comparing these multiple features.

[0130] Furthermore, while Figure 11 shows an anomaly detection system using a combination of two patterns as an example, it is not limited to this. For example, an anomaly detection system could use only pattern 1 or pattern 2, or it could have two or more patterns, such as a combination of pattern 1 and pattern 2.

[0131] Furthermore, the thresholds 1 and 2 in the first embodiment do not necessarily have to be the same value, and it is possible to set different thresholds for different types of electrical signal data (for example, electrical signal data in QC samples or patient samples) or different required sensitivities (for example, high sensitivity or low sensitivity).

[0132] Furthermore, the period covered by anomaly detection is the period during which detection of anomalies in the flow path F1 is required, for example, the past 10 days including the day the anomaly detection system is put into operation. The anomaly notification period is the period during which the results of anomaly detection are notified to the user, for example, the future 8 days including the day the anomaly detection system is put into operation.

[0133] Furthermore, the period for setting the electrical signal data used to calculate features does not necessarily have to be the current day or the previous day. If there is a period of particular interest, it is possible to specify a past day and a further past day as the setting period and perform anomaly detection based on the electrical signal data during that period.

[0134] Furthermore, while we have described an example of an anomaly detection system using electrical signal data from QC samples and patient samples, the settings for this system can also be changed as needed. For example, it is possible to use electrical signal data from one, two, three, or all of the specified samples from patient samples, calibration samples, QC samples, and dummy samples.

[0135] —Example of notification of detection results in an anomaly detection system— Figures 12 and 13 show examples of notification screens for anomaly detection results.

[0136] Figure 12 shows the screen that notifies the user of the abnormality detection result of S151, i.e., "abnormality detected". As an example of the notification content, in addition to the presence of a flow path abnormality, it can also display a solution to that flow path abnormality, such as a recommendation to replace flow path-related parts. There are no particular limitations on the display method; it may be displayed on the entire screen, or it may be displayed in a pop-up format or as a replacement in part of the screen, and there are no particular limitations. The following display screen examples are similar.

[0137] In contrast, Figure 13 shows the screen that notifies the user when the abnormality detection result of S152 is "no abnormalities". As an example of the notification content, in addition to "no abnormalities in the flow path", the status of the flow path abnormality, such as whether the flow path is operating normally, can also be displayed.

[0138] The notification screens illustrated in Figures 12 and 13 can be displayed independently, but it is preferable to integrate them into an anomaly detection system that includes methods for detecting anomalies in multiple components, for example, an anomaly detection system for the entire device, and to notify the user of the result when the flow path anomaly detection result is "abnormal". An example of such an anomaly detection system configuration is shown in Figures 14 to 16.

[0139] Figure 14 shows an example of the display screen configuration for a device status monitoring system. The device components are displayed on the screen, and if an "abnormality" is detected, the user can be notified by a change in display, such as highlighting. Figure 14 shows an example of the display when a "abnormality" is detected in the flow path. In addition, to allow the user to check the detailed abnormality information screen, an "Alarm" button is provided on the right side of the screen in Figure 14, and a function is provided to switch to a separate screen using an input device, for example, by clicking the "Alarm" button with a mouse.

[0140] Figure 15 shows an example of a detailed display screen for abnormal information. When the "Alarm" button is clicked, the screen shown in Figure 15 is displayed to the user. On this screen, among the components displayed on the left side of the screen, the component with the abnormality will be highlighted or otherwise changed in display. On the right side of the screen, alarm information related to the device abnormality is displayed. Details of the alarm information include, for example, the date the alarm occurred, the alarm code assigned in advance depending on the alarm content, and a detailed alarm message. Furthermore, by clicking the component buttons shown on the left side with the mouse, it is possible to display the abnormality detection result notification screen shown in Figure 12 or Figure 13, depending on whether an abnormality was detected or not.

[0141] Figures 12, 13, and 15 show that, in addition to indicating whether or not there are component abnormalities, the system can also display solutions to those abnormalities, such as recommending the replacement of flow path-related components. This information is intended for users with knowledge of equipment maintenance, such as inspection personnel.

[0142] On the other hand, the device status monitoring system shown in Figure 14 is not necessarily used by users with device maintenance knowledge. For example, it may be configured to provide information about the device status to general users who do not have device maintenance knowledge. In that case, the content displayed on the screen will differ, and an example of this is shown in Figure 16.

[0143] The display screen shown in Figure 16 hides the list of parts and displays only the list of alarms. In addition, a "Severity" column has been added to inform the user of the importance of the alarm, indicating the severity using categories such as "Warning" and "Caution." Furthermore, when the user clicks the button for the relevant alarm information with the mouse, a notification of the anomaly detection result is displayed at the bottom of the screen.

[0144] Since this information is intended for general users who do not possess maintenance knowledge, it is configured to display an error identification number and instructions to contact the relevant person, rather than providing solutions to the problem.

[0145] The anomaly detection system illustrated in Figures 12 to 16 can be displayed on the server user interface 45 of the server 40 and notify the user of the anomaly detection result of the flow path F1. Alternatively, it is also possible to configure the system to display the anomaly detection result on the user interface 94 or user interface 46 of the control device 30 of the automatic analyzer 1, or on a computer that can access the server 40, and notify the user of the anomaly detection result.

[0146] Furthermore, notification items other than those exemplified in this embodiment can also be set. Additionally, the anomaly detection system can be shared by all automated analyzers connected to the server 40, or a separate system can be provided for each automated analyzer ID.

[0147] In the notification screens illustrated in Figures 12 and 13, any display format is possible as long as it is possible to determine whether or not there is an abnormality in the flow path. For example, a "red" display could indicate "abnormality present," and a "green" display could indicate "no abnormality."

[0148] This embodiment shows a preferred example of an anomaly detection system, but is not limited to it. Various modifications are included within the scope of the same technical idea. For example, the position of each display content shown in Figures 12 to 16, the method of display, the method of switching between each screen, etc. may be changed. Also, although a mouse click is given as an example of an input device or input method, other input devices or methods such as finger clicks on a tablet device, or user voice commands may also be used.

[0149] Next, the effects of this embodiment will be described.

[0150] The first embodiment of the present invention described above is a flow path abnormality detection system and a flow path abnormality detection method that detects abnormalities based on data obtained from an automatic analyzer 1 comprising: a sensor 11 that applies voltage to a reaction solution obtained by antigen-antibody reaction between a sample and a reagent to cause an electrochemical reaction; a flow path F1 through which the reaction solution and an auxiliary reagent for energization are introduced to the sensor 11; and a photoelectric conversion sensor PT that measures the emission intensity due to the electrochemical reaction. The system comprises a processor 42 that calculates feature quantities from time-series electrical signal data during the period of electrochemical reaction and identifies abnormalities in the flow path F1 from the feature quantities, and a data output interface 44 that outputs a signal related to the result of the abnormality identified by the processor 42. The processor 42 obtains feature quantities from the acquired electrical signal data based on the emission intensity measured by the photoelectric conversion sensor PT.

[0151] This makes it possible to automatically detect abnormalities in the flow path F1 from the measurement data.

[0152] Furthermore, the processor 42 can detect abnormalities in the flow path F1 with greater accuracy by obtaining feature quantities based on the time-dependent current waveform obtained during the electrochemical reaction period from the electrical signal data.

[0153] Furthermore, the processor 42 can perform anomaly detection at any given time by obtaining feature quantities based on the value at a predetermined point in time in the current waveform over time, or the values ​​at multiple points in time in the current waveform over time.

[0154] Furthermore, the processor 42 can perform more accurate anomaly detection by calculating the average or cumulative value of values ​​from multiple time points as a feature.

[0155] Furthermore, the processor 42 calculates multiple feature quantities from the electrical signal data and identifies an abnormal mode by comparing the multiple feature quantities. The data output interface 44 outputs the result of identifying the abnormal mode and also includes a storage device 41 for storing the acquired electrical signal data. The processor 42 calculates feature quantities from the electrical signal data stored in the storage device 41 and identifies an abnormality or abnormal mode in the flow path F1 from the feature quantities, allowing maintenance personnel and users to quickly identify the location of the abnormality.

[0156] Furthermore, the system includes a reaction vessel C2 for containing the reaction solution and an auxiliary reagent container RG for containing auxiliary reagents. The flow path F1 is either a nozzle or piping that transports the reaction solution from the reaction vessel C2 to the sensor 11, or a nozzle or piping that transports the auxiliary reagent from the auxiliary reagent container RG to the sensor 11. This enables abnormality detection in areas where the load fluctuates significantly due to different concentrations for each sample on the automatic analyzer 1 side.

[0157] Furthermore, the processor 42 can calculate multiple features from electrical signal data and, by comparing these features, identify nozzle bending or pipe blockage, thereby enabling the detection of abnormalities in areas prone to malfunction.

[0158] Furthermore, since the reaction solution is a liquid consisting of either a sample, calibration sample, quality control sample, or dummy sample, the values ​​obtained from actual measurements can be used, eliminating the need for measurements for anomaly detection and thus improving the efficiency of the device operation.

[0159] Furthermore, because the electrical signal data is stored on the network, abnormality detection of the automated analyzer 1 operating in various locations can be performed collectively by the server 40, etc., which makes it possible to improve detection accuracy by improving data storage efficiency and reduce the processing burden on the automated analyzer 1.

[0160] <Second Embodiment> A second embodiment of the present invention, comprising a flow path abnormality detection system, an automated analyzer, and a method for detecting abnormalities in a flow path, will be described.

[0161] The difference between this embodiment and the first embodiment is that the abnormality detection system for the flow path F1 is completed within the automatic analyzer 1.

[0162] In this embodiment, the series of processes related to the functions that were handled by the server 40 (Figure 10) in the first embodiment are performed by the control device 30. In this embodiment, the data and processes related to the anomaly detection function that were allocated to the storage device 41 and processor 42 in the first embodiment are allocated to, for example, the storage device 31 and processor 32 of the control device 30. The method for detecting anomalies in the flow path F1 is the same as in the first embodiment, and the detection result is notified to the user, etc., through the user interface 94.

[0163] In the above process, the log file is stored in the storage device 41 during the log file storage process P21. Subsequently, the extracted electrical signal data is stored in the storage device 41 during the electrical signal data storage process P23.

[0164] However, to obtain the same effects as in the first embodiment, it is not necessarily required to provide a storage device 41. For example, it is possible to extract the necessary electrical signal data at the data acquisition interface 43 and transfer it directly to the processor 42 (corresponding to P23) to detect abnormalities in the flow path F1.

[0165] In this embodiment, conditions can be set and results can be notified via the user interface 94 of the control device 30 of the automatic analyzer 1, or via a user interface accessible to the automatic analyzer via a network. The user interface could be, for example, a touch panel held by an operator.

[0166] The other configurations and operations are substantially the same as those of the flow path abnormality detection system, automatic analyzer, and flow path abnormality detection method of the first embodiment described above, and details are omitted.

[0167] In the second embodiment of the present invention, the flow path abnormality detection system, automatic analyzer, and flow path abnormality detection method also provide substantially the same effects as those of the first embodiment described above.

[0168] <Other> It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. The embodiments described above are explained in detail for the purpose of clearly illustrating the present invention, and are not necessarily limited to those having all the configurations described.

[0169] Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations. [Explanation of symbols]

[0170] 1: Automatic analyzer (immunological automatic analyzer) 2: Conveyor line 3: Incubator (reaction disk) 4: First conveying mechanism 5: Tray 6: Sample dispensing nozzle 7: Reagent disk 7a: Disc cover 7b: Reagent aspiration position 8: Reagent dispensing nozzle 9: Second transport mechanism 10: Detection Unit 11: Sensor (container) 12: Liquid Conveying Systems 13: Turntable 15: Potentiostat 18: A / D converter 21: Controller 22: Operating device 30: Control device 31: Storage device 32: Processor 33: Communication Interface 40: Server (Anomaly Detection System) 41:Storage device 42: Processor (arithmetic processing unit) 43: Data Acquisition Interface 44: Data output interface (data output section) 45: Server User Interface 46: User Interface 91: Mechanism department 92: Sample data reading device 93: Reagent data reader 94: User Interface C1: Sample container C2: Reaction vessel (reaction solution container) C3: Reagent container RG: Auxiliary reagent container CL: Detergent container E1: Working electrode (electrode) E2: Counter electrode E3: Reference electrode (electrode) F1: Flow channel (flow channel, nozzle, piping) F2, F3, F4, F5, F6: Flow channels FC: Flow Cell PT: Photoelectric conversion sensor SY: Syringe V1, V2: Valve NW: Network P24: Feature extraction process (arithmetic processing unit) P25: Flow path anomaly detection processing (arithmetic processing unit)

Claims

1. A container that applies voltage to a reaction solution in which a sample and reagent have been reacted with an antigen-antibody, to cause an electrochemical reaction, The container includes a channel for introducing the reaction solution and auxiliary reagents for energization, A flow path abnormality detection system that detects abnormalities based on data obtained from an automated immunoassay analyzer equipped with a photoelectric conversion sensor for measuring the luminescence intensity due to the electrochemical reaction, A calculation processing unit that calculates feature quantities from the time-dependent electrical signal data during the electrochemical reaction period and identifies abnormalities in the flow path from the feature quantities, The system includes a data output unit that outputs a signal relating to the result of the anomaly identified by the calculation processing unit, The processing unit determines the feature quantities from the acquired electrical signal data based on the light emission intensity measured by the photoelectric conversion sensor. Flow path anomaly detection system.

2. In the flow path abnormality detection system according to claim 1, The aforementioned processing unit further determines the feature quantities based on the time-dependent current waveform obtained from the electrical signal data during the electrochemical reaction period. Flow path anomaly detection system.

3. In the flow path abnormality detection system according to claim 2, The calculation processing unit determines the feature quantity based on the value at a predetermined point in time in the current waveform over time, or the values ​​at multiple points in time in the current waveform over time. Flow path anomaly detection system.

4. In the flow path abnormality detection system according to claim 3, The aforementioned processing unit calculates the average or integrated value of values ​​from multiple time points as the feature quantity. Flow path anomaly detection system.

5. In the flow path abnormality detection system according to claim 1, The arithmetic processing unit calculates multiple feature quantities from the electrical signal data, identifies an abnormal mode by comparing the multiple feature quantities, The data output unit outputs the result of identifying the abnormal mode. Flow path anomaly detection system.

6. In the flow path abnormality detection system according to claim 1, The system further comprises a memory device for storing the acquired electrical signal data, The arithmetic processing unit calculates the feature quantities from the electrical signal data stored in the memory device, and identifies an abnormality or abnormal mode in the flow path based on the feature quantities. Flow path anomaly detection system.

7. In the flow path abnormality detection system according to claim 1, A reaction solution container for containing the reaction solution, The system further comprises an auxiliary reagent container for containing the aforementioned auxiliary reagent, The aforementioned flow path is A nozzle or piping for transporting the reaction liquid from the reaction liquid container to the container, A nozzle or piping for transporting the auxiliary reagent from the auxiliary reagent container to the container. It is one of the following Flow path anomaly detection system.

8. In the flow path abnormality detection system according to claim 7, The aforementioned processing unit calculates multiple feature quantities from the electrical signal data and identifies the bending of the nozzle or the blockage of the piping by comparing the multiple feature quantities. Flow path anomaly detection system.

9. In the flow path abnormality detection system according to claim 1, The reaction solution is a liquid consisting of one of the following: a sample, a calibration sample, a quality control sample, or a dummy sample. Flow path anomaly detection system.

10. In the flow path abnormality detection system according to claim 1, The aforementioned electrical signal data is data stored on a network. Flow path anomaly detection system.

11. A container that applies voltage to a reaction solution in which a sample and reagent have been reacted with an antigen-antibody, to cause an electrochemical reaction, The container includes a channel for introducing the reaction solution and auxiliary reagents for energization, A photoelectric conversion sensor for measuring the light emission intensity due to the electrochemical reaction, A calculation processing unit that calculates feature quantities from the time-dependent electrical signal data during the electrochemical reaction period and identifies abnormalities in the flow path from the feature quantities, The system includes a data output unit that outputs a signal relating to the result of the anomaly identified by the calculation processing unit, The processing unit determines the feature quantities from the acquired electrical signal data based on the light emission intensity measured by the photoelectric conversion sensor. Abnormalities are detected based on data obtained from an automated immunoassay analyzer. Automatic analyzer.

12. A container that applies voltage to a reaction solution in which a sample and reagent have been reacted with an antigen-antibody, to cause an electrochemical reaction, The container includes a channel for introducing the reaction solution and auxiliary reagents for energization, A method for detecting abnormalities in a flow channel, comprising detecting an abnormality based on data obtained from an automated immunoassay analyzer equipped with a photoelectric conversion sensor for measuring the luminescence intensity due to the electrochemical reaction, Of the electrical signal data obtained over time during the electrochemical reaction, a feature quantity is calculated based on the luminescence intensity measured by the photoelectric conversion sensor. Based on the aforementioned feature quantities, abnormalities in the flow path are identified. Outputs a signal relating to the result of the identified anomaly. Method for detecting abnormalities in a flow path.

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