Patent Description:
Erythrocyte sedimentation rate (ESR for short) refers to a rate at which red blood cells in anticoagulated blood in vitro naturally sediment under specified conditions. The erythrocyte sedimentation rate is used for the differential diagnosis and observation for some diseases, and it is also a common indicator that reflects aggregation of red blood cells. The ESR is of significance for discerning a quiescent stage of a disease, stability and relapse of conditions, and benignancy and malignancy of tumors.

Currently, the ESR is usually measured by using a Westergren method in the art. That is, a sedimentation rate of red blood cells in a blood sample contained in a Westergren tube or an ESR tube is observed and recorded. Usually, in the Westergren method, after injection of the blood sample into the Westergren tube, timer is started, and a distance from an interface between clustered cells and a plasma to a top liquid level at the top of the tube (a height of the suspending medium) is observed in one hour.

However, measuring the ESR by using the Westergren method (also referred to as a reference method) has the following defects. <NUM>) A measurement speed thereof is slow. Sedimentation time of one hour is required. However, with the increase of the number of patients in a hospital, the testing efficiency of providing measurement results in one hour cannot meet the daily testing needs. <NUM>) More blood volume is needed for this method. Usually, a blood volume of about <NUM> is needed to fill up the ESR tube, which is impossible for patients whose peripheral finger blood is collected to be tested.

In order to overcome the above defects of the Westergren method, a fast ESR measurement method (also referred to as an erythrocyte aggregation method hereinafter) has emerged in the art on the basis of hemorheology research, which is a method for predicting erythrocyte sedimentation rate by measuring an erythrocyte aggregation index. A measured value of the ESR may be derived by measuring a change in a scattering rate/transmissivity of blood cells to light during the formation of rouleaux red blood cells. This measurement process can be completed within a very short time (about <NUM>), and the blood volume used for it is only about <NUM> uL. These methods show the erythrocyte aggregation index is correlated to the erythrocyte sedimentation rate at a certain level.

However, although an ESR value can be rapidly obtained by using the erythrocyte aggregation method, there is still a significant discrepancy between an ESR value measured by using the erythrocyte aggregation method and an ESR value measured by using the Westergren method. Patent application <CIT> provides teachings related to the technical field of the application.

Therefore, an object of the disclosure is to provide an apparatus for measuring ESR, a system for measuring ESR, and a method for measuring ESR, which can rapidly and accurately obtain a corrected ESR measurement result. The ESR measurement result obtained by the disclosure has a good consistency with the ESR measurement result obtained by using the Westergren method.

To achieve the object of the disclosure, a first aspect of the disclosure provides an apparatus for measuring erythrocyte sedimentation rate, including an ESR detection device and a data processing device. The ESR detection device is configured to test a blood sample to be tested, to obtain an erythrocyte aggregation curve of light intensity transmitted through the blood sample to be tested or of light scattered by the blood sample to be tested as a function of time. The data processing device is configured to: acquire the erythrocyte aggregation curve of the blood sample to be tested, acquire a blood cell histogram and/or a blood cell scattergram of the blood sample to be tested that are/is obtained by a blood cell analyzer, and input the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram into a neural network model, to calculate an ESR measurement result by the neural network model.

In the apparatus provided in the first aspect of the disclosure, the erythrocyte aggregation curve of the blood sample to be tested is rapidly obtained by the ESR detection device based on an erythrocyte aggregation method, and then the blood cell histogram and/or the blood cell scattergram of the blood sample to be tested are/is obtained from the blood cell analyzer which is outside the apparatus for measuring the ESR. Next, the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram are input into the neural network model, especially a pre-trained neural network model. In this way, an accurate ESR measurement result can be output.

A second aspect of the disclosure provides a system for measuring erythrocyte sedimentation rate, including a sampling and dispensing device, a blood cell detection device, and the apparatus for measuring ESR in the first aspect. The sampling and dispensing device is configured to collect a blood sample to be tested, and dispense at least portions of the blood sample to be tested into the ESR detection device and the blood cell detection device respectively. The ESR detection device is configured to test the portion of the blood sample dispensed therein, so as to obtain the erythrocyte aggregation curve of the light intensity transmitted through the portion of the blood sample dispensed therein or of light scattered by the portion of the blood sample dispensed therein as a function of time. The blood cell detection device is configured to test the portion of the blood sample dispensed therein to obtain the blood cell histogram and/or the blood cell scattergram of the blood sample to be tested. The blood cell histogram and/or the blood cell scattergram include/includes at least a histogram and/or a scattergram related to red blood cells. The data processing device is configured to acquire the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram, and input the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram into the neural network model, so as to calculate a first ESR measurement result by using the neural network model.

In the system provided in the second aspect of the disclosure, by integrating the ESR detection device and the blood cell detection device, the erythrocyte aggregation curve and the red blood cell histogram and/or the red blood cell scattergram of the blood sample to be tested can be simultaneously acquired rapidly, and the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram are input into the neural network model, especially a pre-trained neural network model, so as to output an accurate ESR measurement result.

A third aspect of the disclosure provides a method for measuring erythrocyte sedimentation rate, including the following operations.

An erythrocyte aggregation curve of a blood sample to be tested is acquired.

A blood cell histogram and/or a blood cell scattergram of the blood sample to be tested are/is acquired, in which the blood cell histogram and/or the blood cell scattergram include/includes at least a histogram and/or a scattergram related to red blood cells.

The erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram are input into a neural network model, to calculate a first ESR measurement result by using the neural network model.

In the method provided in the third aspect of the disclosure, the acquired erythrocyte aggregation curve and the acquired blood cell histogram and/or blood cell scattergram are input into the neural network model, especially a pre-trained neural network model. In this way, an accurate ESR measurement result can be output.

The disclosure will be more clearly elaborated below with reference to the examples and the accompanying drawings. The above and other advantages will become apparent to those of ordinary skill in the art from the detailed description of the examples of the disclosure. The accompanying drawings are only used for illustrating preferred examples and should not be construed as limitations to the disclosure. The same or similar reference numerals represent the same components throughout the accompanying drawings. In the accompanying drawings:.

The examples of the disclosure will be clearly and fully described below with reference to the accompanying drawings. Apparently, the examples to be described are merely some, rather than all, of the examples of the disclosure.

It should be noted that the term "first/second/third" in the examples of the disclosure is only used to distinguish similar objects, and does not represent specific order of the objects. It may be understood that the specific order or sequence of "first/second/third" may be interchanged if applicable.

It can be understood by those skilled in the art that all terms including technical terms and scientific terms used herein have the same meanings as those generally understood by those of ordinary skill in the art to which the present application pertains, unless otherwise specified.

Currently, an ESR value of a blood sample is increasingly used clinically to assist in diagnosis of diseases. The inventors of the disclosure have noticed that consistency between an ESR value obtained by an existing erythrocyte aggregation method and an ESR value obtained by the reference method (that is, the Westergren method) is poor. In other words, the ESR value obtained by the erythrocyte aggregation method cannot accurately reflect a natural sedimentation rate of red blood cells.

In order to rapidly obtain an ESR value that can more accurately reflect the natural sedimentation rate of red blood cells, a technical solution is proposed to correct an erythrocyte aggregation curve by using a blood cell distribution diagram obtained by a blood cell analyzer.

As shown in <FIG>, according to an example of the disclosure, an apparatus <NUM> for measuring ESR is proposed. Herein, the apparatus <NUM> for measuring ESR includes an ESR detection device <NUM> and a data processing device <NUM>.

The ESR detection device <NUM> is configured to test a blood sample to be tested, to obtain an erythrocyte aggregation curve P1 of light intensity transmitted through a dispensed blood sample portion or scattered by the blood sample portion as a function of time. That is, the ESR detection device <NUM> is configured to test the blood sample to be tested based on the erythrocyte aggregation method.

The data processing device <NUM> is configured to process and calculate data to obtain a desired result. In the example of the disclosure, the data processing device <NUM> is configured to: acquire the erythrocyte aggregation curve P1; acquire a blood cell histogram and/or a blood cell scattergram P2 of the blood sample to be tested that are/is obtained by a blood cell analyzer <NUM>; and input the erythrocyte aggregation curve P1 and the blood cell histogram and/or the blood cell scattergram P2 into a neural network model M, so as to calculate an ESR measurement result ESR1 by the neural network model M. Here, the blood cell histogram and/or the blood cell scattergram include/includes at least a histogram and/or a scattergram related to red blood cells.

Here, the neural network model M includes a deep learning algorithm having a neural network structure. That is, the data processing device <NUM> is configured to input the analysis data P1 and P2 into the deep learning algorithm having the neural network structure, and calculate the ESR measurement result ESR1 of the blood sample to be tested by means of the deep learning algorithm.

In the example shown in <FIG>, the blood cell analyzer <NUM> is arranged outside the apparatus <NUM> for measuring ESR and is connected to the data processing device <NUM> in communication so that the data processing device <NUM> acquires the blood cell histogram and/or the blood cell scattergram obtained by the blood cell analyzer <NUM>.

In an example of the ESR detection device <NUM>, as shown in <FIG>, the ESR detection device <NUM> includes a testing pipeline <NUM>, a power assembly <NUM>, and an optical detection assembly with a light emitter <NUM> and a light receiver <NUM>. The power assembly <NUM> (e.g., a syringe here) is configured to transfer, for example, aspirate, the blood sample to be tested into a testing region <NUM> of the testing pipeline <NUM>. For example, the power assembly is configured to aspirate the blood sample to be tested from a test tube <NUM> to the testing region <NUM> of the testing pipeline <NUM> by a sampling needle <NUM>. The light emitter <NUM> and the light receiver <NUM> are respectively located on either side of the testing region <NUM> of the testing pipeline <NUM>. The light emitter <NUM> is configured to irradiate the blood sample in the testing region <NUM>. The light receiver <NUM> is configured to detect a change in the amount of light emitted by the light emitter <NUM> after the light passes through the blood sample (for example, to receive light transmitted through and/or scattered by the blood sample), and detect a degree to which the blood sample in the testing region <NUM> absorbs or scatters light by detecting the amount of the received light. Since the scatterance or transmittance of light passing through the blood sample may change during aggregation of red blood cells in the blood sample (to form rouleaux), it is possible to detect the degree to which the blood sample absorbs or scatters light and thus to measure the erythrocyte sedimentation rate, by detecting the amount of light transmitted through or scattered by the blood sample. Here, the optical detection assembly tests the blood sample, in particular by using transmission turbidimetry.

When the ESR detection device <NUM> is activated for testing, the power assembly <NUM> drives the blood sample to be tested to flow into the testing pipeline <NUM>, allows the blood sample to be tested to stop flowing when it flows to the testing region <NUM>, and then keeps the blood sample to be tested still. The light emitter <NUM> irradiates the blood sample in the testing region, and the light receiver <NUM> detects the scatterance or transmittance of the light emitted by the light emitter <NUM> after it passes through the blood sample in the testing region <NUM>, to detect an erythrocyte aggregation degree, such as an erythrocyte aggregation rate, in the blood sample to be tested.

In some examples, the testing pipeline <NUM> is made of a flexible tube, and the testing region <NUM> of the testing pipeline <NUM> is made of a transparent material. Therefore, the testing pipeline <NUM> may be arranged arbitrarily and flexibly. For example, it may be arranged vertically, horizontally, or obliquely, or may be arranged in a curved manner, which is not limited herein. Preferably, the testing pipeline <NUM> is configured as a capillary tube.

In some examples, the ESR detection device <NUM> may be further configured to: before obtaining the erythrocyte aggregation curve P1 by means of testing, de-aggregate red blood cells in the blood sample to be tested by allowing the blood sample to be tested to flow back and forth in the testing region <NUM> of the ESR detection device <NUM>. As a result, the red blood cells in the blood sample in the testing region <NUM> are dispersed as much as possible before the optical detection assembly with the light emitter <NUM> and the light receiver <NUM> detects the erythrocyte aggregation degree, so that the erythrocyte aggregation degree can be measured more accurately.

To this end, in the example shown in <FIG>, the power assembly <NUM> can be used to drive the blood sample in the testing region <NUM> of the testing pipeline <NUM> to flow back and forth. Especially, when the power assembly <NUM> is configured as a syringe, the red blood cells in blood sample can be flexibly de-aggregated, and a blood volume can be saved, since a movement speed and a movement direction of the syringe can be flexibly set.

After the power assembly <NUM> drives the blood sample in the testing region <NUM> to flow back and forth a predetermined number of cycles, the power assembly <NUM> immediately stops driving, so that the blood sample in the testing region <NUM> stops flowing, and red blood cells in the blood sample in the testing region <NUM> aggregate, resulting in the change in transmittance.

In addition, in the example shown in <FIG>, the ESR detection device <NUM> may further include a temperature sensor <NUM> and a heater <NUM> arranged near the testing region <NUM> of the testing pipeline <NUM>. The temperature sensor <NUM> is configured to detect a temperature value of the testing region <NUM> or of the blood sample in the testing region. The heater <NUM> is configured to heat the testing region <NUM> when the temperature value is less than a predetermined temperature to maintain the temperature of the testing region <NUM> constant.

In some examples, the data processing device <NUM> may include a processor. The processor may include, but is not limited to, a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), a digital signal processor (DSP) and other devices for interpreting computer instructions and processing data in computer software.

<FIG> is a schematic block diagram of an example of a neural network model M according to the disclosure. The neural network model M has a convolutional neural network structure. The convolutional neural network structure includes a data input layer, a convolutional layer, a pooling layer, and a fully connected layer. In the example shown in <FIG>, the neural network model M includes four convolutional layers, two pooling layers, and three fully connected layers. In other examples, the neural network model M may alternatively have fewer or more convolutional layers, pooling layers, and fully connected layers, which are not specifically limited in the disclosure.

Here, preferably, the neural network model M is pre-trained and stored in the data processing device <NUM>. For example, the neural network model M is pre-trained with testing data of a large number of clinical samples.

As shown in <FIG> and <FIG>, according to another example of the disclosure, a system <NUM> for measuring ESR is proposed, in which the system integrates an apparatus for measuring ESR and a hematology analyzer. The system <NUM> includes a sampling and dispensing device <NUM>, an ESR detection device <NUM>, a blood cell detection device (the hematology analyzer) <NUM>, and a data processing device <NUM>.

The sampling and dispensing device <NUM> is configured to collect a blood sample to be tested, and dispense at least portions of the blood sample to be tested into the ESR detection device <NUM> and the blood cell detection device <NUM>.

The ESR detection device <NUM> is configured to test the portion of the blood sample dispensed therein, so as to obtain an erythrocyte aggregation curve P1 of light intensity transmitted through the blood sample to be tested or light scattered by the blood sample to be tested as a function of time. For a structure of the ESR detection device <NUM>, reference may be made to the above description of the ESR detection device <NUM> shown in <FIG>, and details will not be described herein again.

The blood cell detection device <NUM> is configured to test the portion of the blood sample dispensed therein, so as to obtain a blood cell histogram and/or a blood cell scattergram of the blood sample to be tested. Herein, the blood cell histogram and/or the blood cell scattergram include/includes at least a histogram and/or a scattergram related to red blood cells.

The data processing device <NUM> is configured to acquire the erythrocyte aggregation curve P1 and the blood cell histogram and/or the blood cell scattergram P2, and input the erythrocyte aggregation curve P1 and the blood cell histogram and/or the blood cell scattergram P2 into a neural network model M, to calculate a first ESR measurement result ESR1 by using the neural network model M, that is, an ESR measurement result corrected by using the blood cell histogram and/or the blood cell scattergram. For a structure of the data processing device <NUM>, reference may be made to the above description of the data processing device <NUM> shown in <FIG>, and details will not be described herein again.

As shown in <FIG>, in some examples, the sampling and dispensing device <NUM> may include a sampling needle <NUM> and a drive assembly (not shown in the figure). The drive assembly is configured to drive the sampling needle <NUM> to move, so that the sampling needle <NUM> collects the blood sample and dispenses a portion of the blood sample into a reaction cell <NUM> for routine blood test of the blood cell detection device <NUM>. In addition, the sampling and dispensing device <NUM> may include a power assembly <NUM>, such as a syringe, for providing driving force to aspirate the blood sample from a test tube to a sampling needle.

In some preferred examples, the power assembly of the ESR detection device and the power assembly <NUM> of the sampling and dispensing device <NUM> are the same assembly. That is, the power assembly <NUM> of the sampling and dispensing device <NUM> can also be used as the power assembly of the ESR detection device.

In some examples, the blood cell detection device <NUM> includes a reaction cell <NUM> and a detection assembly (not shown in <FIG>). The reaction cell <NUM> is configured to provide a place for the dispensed blood sample to mix with a reagent. The detection assembly is configured to perform a routine blood test on a sample solution to be tested obtained by mixing the blood sample with the reagent in the reaction cell <NUM>.

Those skilled in the art can understand that the detection assembly of the blood cell detection device <NUM> may include at least one of an optical detection unit, an impedance detection unit, and a hemoglobin detection unit. Accordingly, the reaction cell may include at least one of an optical detection reaction cell, an impedance detection reaction cell, and a hemoglobin detection reaction cell. When the blood cell detection device <NUM> performs a routine blood test on the blood sample, the blood sample and a corresponding reagent (such as a diluent and/or a hemolyzing agent and/or a staining agent) can be added to the reaction cell <NUM>. The detection assembly can detect the blood sample in the reaction cell <NUM> to obtain at least one blood cell parameter. Herein, the blood cell parameter may include at least one of or a combination of a white blood cell (WBC) classification parameter, a WBC count and morphological parameter, a hemoglobin (HGB) parameter, a red blood cell (RBC) count and morphological parameter, and a blood platelet (PLT) count and morphological parameter.

In an example of an impedance detection unit <NUM> of the blood cell detection device <NUM>, as shown in <FIG>, the impedance detection unit <NUM> includes a detection aperture <NUM> and a detection circuit. The detection circuit is configured to detect a signal of impedance change when cells in the portion of the blood sample dispensed into the impedance detection unit <NUM> pass through the detection aperture <NUM>, to obtain a red blood cell parameter and/or a platelet parameter and/or a white blood cell parameter. The impedance detection unit <NUM> is configured to operate based on a Coulter principle (also known as an electrical impedance method), and in particular, is configured as a sheath flow impedance detection unit. Specifically, the impedance detection unit <NUM> includes a flow cell having a detection aperture <NUM> and includes a detection circuit. The detection circuit includes a pair of electrodes <NUM>, a direct current (DC) power supply <NUM> electrically connected to the electrodes <NUM>, and an amplifier <NUM>. The impedance detection unit <NUM> detects DC impedance generated by particles in the blood sample passing through the detection aperture <NUM>, and outputs an electrical signal representing information of the particles passing through the aperture. The impedance detection unit <NUM> may be further provided with a sheath fluid cell (shown in the figure) for supplying a sheath fluid to the flow cell. In the flow cell, the blood sample to be tested flows with being wrapped by the sheath fluid, and particles contained in the blood sample to be tested pass through the detection aperture <NUM> one by one. The DC power supply <NUM> provides DC power to the pair of electrodes <NUM>. During the DC power supply provides DC power, impedance between the pair of electrodes <NUM> can be detected. A resistance signal representing a change in impedance is amplified by the amplifier <NUM> and then transferred to the data processing device <NUM>, to obtain a red blood cell parameter and/or a platelet parameter and/or a white blood cell parameter.

In an example of an optical detection unit <NUM> of the blood cell detection device <NUM>, as shown in <FIG>, the optical detection unit <NUM> is configured to operate based on a principle of flow cytometry, and it has a light source <NUM>, a flow cell <NUM>, and light detectors <NUM>, <NUM>, and <NUM>. The light source <NUM> is configured to emit a light beam to irradiate a testing region of the flow cell <NUM>, and the light detectors are configured to detect optical signals generated when the cells in the portion of the blood sample dispensed into the optical detection unit <NUM> pass through the testing region and are irradiated.

Herein, the flow cell <NUM> refers to a chamber of a focused flow, which is suitable for detecting a scattered light signal and a fluorescence signal. When a particle, such as a blood cell, passes through the detection aperture of the flow cell, the particle scatters an incident light beam to various directions, in which the incident light beam is from the light source and directed to the detection aperture. Light detectors, which may be provided at one or more different angles relative to the incident light beam, may detect light scattered by the particle to obtain a scattered light signal. For example, at least one of a forward-scattered light detector for detecting forward-scattered light (FSC), a side-scattered light detector for detecting side-scattered light (SSC), and a fluorescence detector for detecting fluorescence (SFL) may be provided. The forward-scattered light represents a volume of the cell, the side-scattered light represents internal complexity of the cell, and the fluorescence represents a content of nucleic acid in the cell.

In the example shown in <FIG>, the optical detection unit <NUM> has the light source <NUM>, a beam shaping assembly <NUM>, a flow cell <NUM>, and the forward-scattered light detector <NUM> sequentially arranged in a straight line. On one side of the flow cell <NUM>, a dichroscope <NUM> is arranged at an angle of <NUM>° relative to the straight line. Part of lateral light caused by particles in the flow cell <NUM> is transmitted through the dichroscope <NUM> and is captured by the fluorescence detector <NUM> arranged behind the dichroscope <NUM> at an angle of <NUM>° relative to the dichroscope <NUM>; and the other part of the lateral light is reflected by the dichroscope <NUM> and is captured by the side-scattered light detector <NUM> arranged in front of the dichroscope <NUM> at an angle of <NUM>° relative to the dichroscope <NUM>.

In some examples, the erythrocyte aggregation curve P1 is obtained by transmission turbidimetry.

<FIG> shows the erythrocyte aggregation curve P1 obtained by the ESR detection devices <NUM> and <NUM> according to the disclosure, where transmittance (also referred to as transmissivity) means a relative light intensity, which is equal to a ratio of intensity of transmitted light to intensity of the background light.

In some examples, the data processing devices <NUM> and <NUM> may be further configured to: calculate an Area Under Curve (AUC), which refers to an area enclosed by the erythrocyte aggregation curve and a time axis within a time period between a measurement start time point T1 and a measurement end time point T2; and calculate a second ESR measurement result ESR2 (that is, an uncorrected ESR measurement result) based on the AUC and a pre-stored calibration curve.

In some examples, the pre-stored calibration curve is stored, for example, in the data processing devices <NUM> and <NUM>.

<FIG> shows a calibration curve (also referred to as a standard curve) for calculating the second ESR measurement result ESR2, where the abscissa thereof represents the AUC, and the ordinate thereof represents the second ESR measurement result ESR2. By researching, it is found that there is a correlation between the AUC of the erythrocyte aggregation curve and the ESR obtained by the Westergren method of the blood sample. Therefore, the calibration curve may be obtained by statistical fitting of the AUC of the erythrocyte aggregation curve and the ESR obtained by the Westergren method of a large number of blood samples. That is, a large number of blood samples are tested by using both the apparatus or system according to the disclosure and the measurement apparatus (including the ESR tube) for the Westergren method, to obtain the AUC of the erythrocyte aggregation curve and the ESR according to the Westergren method of these blood samples, and then a calibration curve may be obtained based on the data.

In some examples, the calibration curve is stored in the data processing devices <NUM> and <NUM> in the form of a fitting function or in the form of a series of discrete data. For example, the calibration curve is stored in the data processing devices <NUM> and <NUM> in the form of a lookup table, and the second ESR measurement result ESR2, which approximates to the ESR according to the Westergren method, may be obtained through the AUC by means of table lookup and interpolation.

The second ESR measurement result ESR2 obtained according to the example of the disclosure has a good correlation with a reference value for the erythrocyte aggregation method which is obtained by the instrument from ALIFAX company in the prior art, as shown in <FIG>.

In some examples, the data processing devices <NUM> and <NUM> may be further configured to output at least one of the first ESR measurement result ESR1 and the second ESR measurement result ESR2.

In some examples, the blood cell histogram may include one or more of a red blood cell volume distribution histogram, a platelet volume distribution histogram, and a white blood cell volume distribution histogram that are obtained based on a signal of impedance change detected by the impedance detection unit <NUM>, as shown in <FIG>. In a specific example, the erythrocyte aggregation curve may be corrected by using the red blood cell volume distribution histogram to obtain the ESR1. That is, the erythrocyte aggregation curve and the red blood cell volume distribution histogram of the blood sample to be tested are input into the pre-trained neural network model M to obtain an output of the neural network model as the ESR1.

In an example, the erythrocyte aggregation curve, a red blood cell volume distribution histogram obtained by the electrical impedance method, and a measurement result obtained by the Westergren method of a large number of blood samples are collected. Training is performed based on the neural network model M shown in <FIG> to obtain parameters of the neural network model. Next, the blood sample to be tested is detected by using the system <NUM> for measuring ESR according to the disclosure and the Westergren method, to obtain the corresponding first ESR measurement result ESR1 and the measurement result according to the Westergren method. The correlation shown in <FIG> is obtained based on the first ESR measurement result and the measurement result according to the Westergren method of the blood sample to be tested. It can be seen from <FIG> that the correlation between the first ESR measurement result ESR1 and the measurement result according to the Westergren method reaches <NUM>, indicating that the first ESR measurement result ESR1 has a good consistency with the measurement result according to the Westergren method.

In some other examples, the blood cell scattergram may include one or more of a red blood cell scattergram, a platelet scattergram, and a white blood cell scattergram that are obtained based on a signal of impedance change detected by the impedance detection unit <NUM>. For example, data for generating the red blood cell scattergram include volume signals of red blood cells, that is, signals of impedance change, and other signals related to the red blood cell passing through the detection aperture <NUM>, such as a time when the red blood cell passes through the detection aperture <NUM> or a time when the red blood cell arrives at the center of the detection aperture <NUM>. As shown in <FIG>, in the red blood cell scattergram shown therein, the abscissa thereof represents the time when the red blood cell passes through the detection aperture <NUM>, and the ordinate thereof represents an impedance signal.

In a specific example, the erythrocyte aggregation curve, a red blood cell scattergram obtained by the electrical impedance method, and a measurement result obtained by the Westergren method of a large number of blood samples are collected. Training is performed based on the neural network model M shown in <FIG> to obtain parameters of the neural network model. Next, the blood sample to be tested is detected by using the system <NUM> for measuring ESR according to the disclosure and the Westergren method to obtain the corresponding first ESR measurement result ESR1 and the measurement result according to the Westergren method. The correlation shown in <FIG> is obtained based on the first ESR measurement result and the measurement result according to the Westergren method of the blood sample to be tested. It can be seen from <FIG> that the correlation between the first ESR measurement result ESR1 and the measurement result according to the Westergren method reaches <NUM>, indicating that the first ESR measurement result ESR1 has a good consistency with the measurement result according to the Westergren method.

Herein, it is advantageous to use a red blood cell volume distribution histogram, a platelet volume distribution histogram, a white blood cell volume distribution histogram, a red blood cell scattergram, a platelet scattergram, and/or a white blood cell scattergram that may be obtained based on the electrical impedance method, as the inputs to the neural network model, because in practice it is usually required for patients to detect a red blood cell parameter (red blood cell count) as well as a platelet parameter (platelet count) according to the electrical impedance method.

Alternatively, in still some other examples, the blood cell scattergram may include at least one of a red blood cell scattergram, a white blood cell scattergram, and a platelet scattergram that are obtained based on an optical signal detected by the optical detection unit <NUM>. Preferably, the red blood cell scattergram obtained by using flow cytometry includes at least a forward-scattered light (FSC) signal of a red blood cell, and other signals forming the red blood cell scattergram may be a side-scattered light (SSC) signal and/or a side fluorescence (SFL) signal of a red blood cell. As shown in <FIG>, in the red blood cell scattergram shown therein, the abscissa thereof represents the side fluorescence (SFL) signal of a red blood cell, and the ordinate thereof represents the forward-scattered light (FSC) signal of a red blood cell. For example, the erythrocyte aggregation curve of the blood sample to be tested and the red blood cell scattergram shown in <FIG> may be input into the pre-trained neural network model M to obtain an output of the neural network model as the ESR1.

In some examples, a blood cell histogram or a blood cell scattergram is used as an input parameter of the neural network model M. In some other examples, the blood cell histogram and the blood cell scattergram obtained in one test may be used as input parameters of the neural network model M.

As shown in <FIG>, according to an example of the disclosure, a method <NUM> for measuring ESR is proposed, and the method <NUM> includes the following operations.

S310: an erythrocyte aggregation curve P1 of a blood sample to be tested is acquired.

S320: a blood cell histogram and/or a blood cell scattergram P2 of the blood sample to be tested are/is acquired, in which the blood cell histogram and/or the blood cell scattergram include/includes at least a histogram and/or a scattergram related to red blood cells.

S330: the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram are input into a neural network model M, to calculate a first ESR measurement result ESR1 by using the neural network model M.

Further, the method may further include the following operations.

An AUC enclosed by the erythrocyte aggregation curve P1 and a time axis within a time period between a measurement start time point T1 and a measurement end time point T2 is calculated.

A second ESR measurement result ESR2 is calculated based on the AUC and a pre-stored calibration curve.

At least one of the first ESR measurement result ESR1 and the second ESR measurement result ESR2 is output.

In some examples, the blood cell histogram may include one or more of a red blood cell volume distribution histogram, a platelet volume distribution histogram, and a white blood cell volume distribution histogram obtained by using a Coulter method.

Additionally or alternatively, the blood cell scattergram may include a scattergram related to red blood cell volume measurement using the Coulter method and/or a scattergram related to red blood cell measurement using flow cytometry; and/or the blood cell scattergram may include a scattergram related to platelet volume measurement using the Coulter method and/or a scattergram related to platelet measurement using flow cytometry; and/or the blood cell scattergram may include a scattergram related to white blood cell measurement using flow cytometry.

In some examples, the erythrocyte aggregation curve may be obtained by means of transmission turbidimetry.

As shown in <FIG>, according to another example of the disclosure, a method <NUM> for measuring ESR is proposed, and the method <NUM> includes the following operations.

S410: a portion of a blood sample to be tested is transferred to an optical testing pipeline <NUM>, and red blood cells in the portion of the blood sample to be tested are de-aggregated by allowing the portion of the blood sample to be tested to flow back and forth in the optical testing pipeline <NUM>.

S420: after de-aggregating the red blood cells, the portion of the blood sample to be tested is kept still in the optical testing pipeline <NUM>, and is irradiated with light, to obtain an erythrocyte aggregation curve C of light intensity transmitted through the portion of the blood sample to be tested as a function of time.

S430: analysis data are input into a deep learning algorithm having a neural network structure, in which the analysis data include the erythrocyte aggregation curve and a blood cell histogram and/or a blood cell scattergram of the blood sample to be tested, and the blood cell histogram and/or the blood cell scattergram include/includes at least a histogram and/or a scattergram related to red blood cells.

S440: an ESR measurement result of the blood sample to be tested is calculated by means of the deep learning algorithm.

For example, the sampling needle of the system for measuring ESR aspirates the blood sample to be tested. After mixing homogenously, red blood cells in the blood sample to be tested are fully de-aggregated. A curve of light intensity transmitted through the de-aggregated blood sample as a function of time is measured, that is, the erythrocyte aggregation curve is measured.

For other examples and advantages of the method <NUM> and the method <NUM> for measuring ESR according to the disclosure, reference may be made to the above description of the apparatus <NUM> and the system <NUM> for measuring ESR according to the disclosure, and details will not be described herein again.

Further, an example not being part of the disclosure provides a computer-readable storage medium including computer program instructions. When the computer program instructions are executed by a processor, the operations of the above method <NUM> and of its various examples may be performed.

The features or combinations thereof mentioned in the above description, accompanying drawings, and claims can be used alone or in combination with each other arbitrarily, as long as it is within the scope of the appended claims and there is no conflict with each other. The advantages and features described for the apparatus and the system for measuring ESR provided in the disclosure are applicable in a corresponding manner to the method for measuring ESR provided in the disclosure, and vice versa.

Claim 1:
An apparatus (<NUM>) for measuring erythrocyte sedimentation rate, ESR, comprising: an ESR detection device (<NUM>) and a data processing device (<NUM>),
wherein the ESR detection device (<NUM>) is configured to test a blood sample to be tested, so as to obtain an erythrocyte aggregation curve of light intensity transmitted through or scattered by the blood sample to be tested as a function of time; and
the data processing device (<NUM>) is configured to acquire the erythrocyte aggregation curve of the blood sample to be tested;
characterized in that the data processing device (<NUM>) is further configured to: acquire a blood cell histogram and/or a blood cell scattergram of the blood sample to be tested which are/is obtained by a blood cell analyzer; and input the erythrocyte aggregation curve and the blood cell histogram and/or the blood cell scattergram into a neural network model (M), so as to calculate an ESR measurement result by using the neural network model (M).