Platelet counting method and blood cell analyzer

Abstract

The embodiments of the present application relate to a platelet counting method and a blood cell analyzer. The method comprises: aspirating a blood sample to be tested; mixing one portion of said blood sample with a diluent to prepare a first test sample, and mixing the other portion of said blood sample with a hemolytic agent and fluorescent dye to prepare a second test sample; performing impedance measurement on the first test sample, in order to obtain an electrical signal of the first test sample; performing optical measurement on the second test sample, in order to obtain at least one optical signal from among a scattered light signal and a fluorescent light signal of the second test sample; at least on the basis of the electrical signal of the first test sample, obtaining platelet distribution information of said blood sample; and at least on the basis of the pulse width of an optical signal among the at least one optical signal that corresponds to platelets in the second test sample, and the platelet distribution information, obtaining a platelet count value. Thus, an accurate platelet count can be obtained at a low cost.

Description

Platelet counting methods and blood cell analyzers Technical Field

[0001] This application relates to the field of blood cell detection, and in particular to a platelet counting method and a blood cell analyzer. Background Technology

[0002] Platelet counts are of significant value in the diagnosis and treatment of thrombotic and hemorrhagic diseases. The International Committee for Standardization of Hematology recommends the use of ethylenediaminetetraacetic acid (EDTA) as an anticoagulant for routine blood tests because it has minimal impact on blood cell morphology and platelet count, and is therefore widely used.

[0003] However, EDTA can sometimes cause platelet (PLT) aggregation. Because blood cell analyzers cannot identify aggregated platelets, this results in EDTA-dependent pseudothrombocytopenia (EDTA-PTCP). If this pseudothrombocytopenia is not detected and corrected in time, unnecessary treatments such as bone marrow aspiration, hormone therapy, or splenectomy may be necessary in clinical practice.

[0004] Currently, clinical solutions for EDTA-PTCP include predilution, anticoagulant replacement, drug stabilization, and manual ammonium oxalate method. These methods require highly specialized physicians, expensive testing costs, and advanced laboratory equipment for examination and analysis. Furthermore, the procedures are relatively cumbersome, time-consuming, and costly, and the platelet counting accuracy of some methods remains unsatisfactory.

[0005] Furthermore, for samples that may show platelet aggregation, it is necessary to re-collect the patient's blood for platelet retesting, which not only increases the patient's suffering but also increases the patient's time and cost.

[0006] Therefore, it is especially important to be able to identify EDTA-PTCP patients in a timely manner and accurately perform platelet counts in clinical practice. Summary of the Invention

[0007] In order to at least partially solve the above-mentioned technical problems, the objective of this application is to provide a solution that can obtain platelet counts in a low-cost, rapid and accurate manner to help clinicians make more accurate diagnoses and treatments for patients.

[0008] Based on this, the first aspect of this application provides a platelet counting method, comprising:

[0009] Collect the blood sample to be tested;

[0010] A portion of the blood sample to be tested is mixed with a diluent to prepare a first test sample, and another portion of the blood sample to be tested is mixed with a hemolysin and a fluorescent dye to prepare a second test sample;

[0011] Impedance measurement is performed on the first test sample to obtain the electronic signal of the first test sample;

[0012] Optical measurements are performed on the second test sample to obtain the optical signal of the second test sample, wherein the optical signal includes at least one of the optical signal of scattered light and the optical signal of fluorescence;

[0013] The platelet distribution information of the blood sample to be tested is obtained at least based on the electronic signal of the first test sample; and

[0014] The first platelet count value of the blood sample to be tested is obtained based at least on the pulse width of the light signal corresponding to the platelets of the second test sample and the platelet distribution information.

[0015] A second aspect of this application provides a blood cell analyzer, comprising:

[0016] A sampling device is used to collect blood samples for testing.

[0017] A sample preparation apparatus for mixing a portion of the blood sample to be tested with a diluent to prepare a first test sample, and for mixing another portion of the blood sample to be tested with a hemolysin and a fluorescent dye to prepare a second test sample;

[0018] An impedance detection device includes a counting cell and a detection component. The counting cell is used to allow a first test sample to pass through, and the detection component is used to acquire the electronic signal when the first test sample passes through the counting cell.

[0019] An optical detection device includes a flow chamber, a light source, and a photodetector. The flow chamber is used for a second test sample to pass through. The light source is used to illuminate the second test sample passing through the flow chamber. The photodetector is used to detect the optical signal generated by the second test sample after being illuminated by light when passing through the flow chamber. The optical signal includes at least one of scattered light and fluorescence.

[0020] The processor is configured as follows:

[0021] The platelet distribution information of the blood sample to be tested is obtained at least based on the electronic signal of the first test sample; and

[0022] The first platelet count value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one optical signal and the platelet distribution information.

[0023] In the technical solutions provided in this application, platelet distribution information is obtained through impedance spectroscopy, and then combined with the scattered light pulse width and / or fluorescence pulse width in the optical signal obtained during optical detection, an accurate platelet count result can be obtained, avoiding misleading clinicians in the diagnosis and treatment of patients, and avoiding unnecessary examinations and treatments. Furthermore, the technical solutions provided in this application do not require retesting, resulting in low cost and high efficiency. Attached Figure Description

[0024] Figure 1 shows a schematic diagram of the structure of some embodiments of the blood cell analyzer according to this application.

[0025] Figure 2 shows a schematic diagram of the structure of some embodiments of the optical inspection apparatus according to this application.

[0026] Figure 3 shows a schematic diagram of the structure of some embodiments of the impedance detection device according to this application.

[0027] Figure 4 shows a schematic diagram of an optical pulse signal according to some embodiments of this application.

[0028] Figure 5 shows a scatter plot of abnormal samples according to some embodiments of this application.

[0029] Figure 6 shows a scatter plot of normal samples according to some embodiments of this application.

[0030] Figure 7(a) shows the correlation curve between the reference platelet count and the first platelet count obtained by testing multiple samples according to some embodiments of this application.

[0031] Figure 7(b) shows the absolute deviation between the reference platelet count and the platelet count before correction, corresponding to Figure 7(a).

[0032] Figure 7(c) shows the relative deviation between the reference platelet count value and the platelet count value before correction, corresponding to Figure 7(a).

[0033] Figure 8(a) shows the correlation curve between the reference platelet count and the corrected platelet count obtained by testing multiple samples according to some embodiments of this application.

[0034] Figure 8(b) shows the absolute deviation between the reference platelet count and the corrected platelet count corresponding to Figure 8(a).

[0035] Figure 8(c) shows the relative deviation between the reference platelet count and the corrected platelet count corresponding to Figure 8(a).

[0036] Figure 9(a) shows the correlation curve between the reference platelet count and the corrected platelet count obtained by testing multiple samples according to other embodiments of this application.

[0037] Figure 9(b) shows the absolute deviation between the reference platelet count and the corrected platelet count corresponding to Figure 9(a).

[0038] Figure 9(c) shows the relative deviation between the reference platelet count and the corrected platelet count corresponding to Figure 9(a).

[0039] Figure 10(a) shows the correlation curves between reference platelet counts and corrected platelet counts obtained by testing multiple samples according to some embodiments of this application.

[0040] Figure 10(b) shows the absolute deviation between the reference platelet count and the corrected platelet count corresponding to Figure 10(a).

[0041] Figure 10(c) shows the relative deviation between the reference platelet count and the corrected platelet count corresponding to Figure 10(a).

[0042] Figure 11(a) shows the correlation curve between the reference platelet count and the second platelet count obtained by testing multiple samples according to some other embodiments of this application.

[0043] Figure 11(b) shows the absolute deviation between the reference platelet count and the corrected platelet count corresponding to Figure 11(a).

[0044] Figure 11(c) shows the relative deviation between the reference platelet count and the corrected platelet count corresponding to Figure 11(a).

[0045] Figure 12 shows a schematic flowchart of a platelet counting method according to some embodiments of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0047] To facilitate subsequent explanations, a brief explanation of some terms used in this application is provided below:

[0048] 1) Scatter plot: A two-dimensional or three-dimensional graph generated by a blood cell analyzer, displaying two-dimensional or three-dimensional feature information of multiple particles. The X, Y, and Z axes of a scatter plot each represent a characteristic of each particle. For example, in a scatter plot, the X axis represents the intensity of forward scattered light, the Y axis represents the fluorescence intensity, and the Z axis represents the intensity of side scattered light.

[0049] The term "scatter plot" as used in this application refers not only to a distribution of at least two sets of data in a rectangular coordinate system in the form of data points, but also to data arrays, i.e., regardless of the form in which they are presented graphically.

[0050] 2) Cell population: A group of particles with the same characteristics distributed in a certain area of ​​a scatter plot. It is also called a "particle cluster" or "cell cluster". For example, white blood cell (including all types of white blood cells) population, as well as white blood cell subpopulations, such as neutrophil population, lymphocyte population, monocyte population, eosinophil population or basophil population, etc.

[0051] The blood cell analyzer used in this application classifies and counts particles in blood samples using flow cytometry, which combines laser scattering and fluorescence staining methods. The principle of the blood cell analyzer in detecting blood samples can be illustrated as follows: First, a blood sample is drawn up; then, a portion of the drawn blood sample is mixed with processing reagents to obtain the test sample; next, each particle in the test sample is passed through a flow chamber irradiated by a laser beam. When the laser beam irradiates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, nuclear density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to the particle characteristics. This scattered light is received by a signal detector to obtain information about the particle structure and composition. Specifically, forward scatter (FS) reflects the number and volume of particles, side scatter (SS) reflects the complexity of the internal cell structure (such as intracellular particles or the cell nucleus), and fluorescence (FL) reflects the content of nucleic acid substances in the cell. Using this optical information, particles, or cells, in the blood sample can be classified and counted.

[0052] As mentioned earlier, platelet counting plays a crucial role in the diagnosis and treatment of thrombotic and hemorrhagic diseases. However, platelet aggregation can lead to significant deviations between platelet counts and true values. Clinically used platelet counting methods based on platelet aggregation samples often yield values ​​lower than the actual values ​​(i.e., falsely low platelet counts), misleading clinicians in their diagnosis and treatment. Obtaining true platelet counts often requires repeat blood draws, increasing patient discomfort, time, and costs. Even with retesting using instruments equipped with deaggregation capabilities, additional reagents are needed, further increasing patient costs.

[0053] In view of this, this application proposes a platelet counting scheme that can obtain accurate platelet count values ​​at the lowest possible cost.

[0054] Figure 1 shows a schematic diagram of an embodiment of a blood cell analyzer according to this application. As shown in Figure 1, the blood cell analyzer 100 includes at least a sample aspiration device 110, a sample preparation device 120, a detection device 130, and a processor 140. The blood cell analyzer 100 also has a fluid system (not shown) for connecting the sample aspiration device 110, the sample preparation device 120, the detection device 130, and the processor 140 to facilitate fluid transfer between these devices.

[0055] The sampling device 110 is used to draw up the blood sample to be tested.

[0056] In some embodiments, the sampling device 110 has a sampling needle (not shown) for drawing up a blood sample to be tested. Furthermore, the sampling device 110 may also include, for example, a driving device for driving the sampling needle to quantitatively draw up the blood sample through the tip of the sampling needle. The sampling device 110 can deliver the collected blood sample to the sample preparation device 120.

[0057] The sample preparation apparatus 120 is used to mix a portion of the blood sample to be tested with a processing reagent, such as a diluent, to prepare a first test sample, and to mix another portion of the blood sample to be tested with a hemolysin and a fluorescent dye to prepare a second test sample.

[0058] In some embodiments, the diluent may include, for example, tris(hydroxymethyl)aminomethane buffer. By adjusting the amount of tris(hydroxymethyl)aminomethane buffer, the osmotic pressure of the diluent is adjusted to approximately 200 mOsm / kg (the osmotic pressure of mammalian blood cells is generally around 260–320). When blood cells are mixed with the diluent, red blood cells and white blood cells absorb water in the hypotonic solution, resulting in an increase in volume, while the volume of platelets remains relatively unchanged.

[0059] In the embodiments of this application, the hemolytic agent is used to dissolve red blood cells in the blood, breaking them into fragments, while maintaining the morphology of white blood cells essentially unchanged. For example, the hemolytic agent can be any one or a combination of several of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants; or, for example, the hemolytic agent can include at least one of alkyl glycosides, triterpenoid saponins, and steroidal saponins.

[0060] In some embodiments, the sample preparation apparatus 120 may include at least one reaction cell and a reagent supply device (not shown). The at least one reaction cell is used to receive a blood sample to be tested drawn by the sampling device 110, and the reagent supply device provides processing reagents (e.g., diluent, fluorescent dye, hemolysin, etc.) to the at least one reaction cell, thereby mixing the blood sample to be tested drawn by the sampling device 110 with the processing reagents provided by the reagent supply device in the reaction cell to prepare a test sample (e.g., a first test sample and a second test sample).

[0061] In a specific example, the at least one reaction cell may include a first reaction cell and a second reaction cell, and the reagent supply device may include a first reagent supply unit and a second reagent supply unit. The sampling device 110 is used to partially distribute the aspirated blood sample to be tested into the first reaction cell and the second reaction cell, respectively. The first reagent supply unit is used to provide a diluent to the first reaction cell, so that the portion of the blood sample allocated to the first reaction cell can be mixed with the diluent and reacted to prepare a first test sample. The second reagent supply unit is used to provide a hemolysin and a fluorescent dye to the second reaction cell, so that the portion of the blood sample allocated to the second reaction cell can be mixed with the hemolysin and the fluorescent dye and reacted to prepare a second test sample.

[0062] The detection device 130 may include an optical detection device 131 and an impedance detection device 132. The impedance detection device 132 is used to detect a first test sample prepared by the sample preparation device 120 to obtain the electronic signal of the first test sample (also referred to as the impedance detection channel). The optical detection device 131 is used to detect a second test sample prepared by the sample preparation device 120 to obtain the optical signal of the second test sample (also referred to as the hemolysis optical detection channel).

[0063] The impedance detection device 132 includes a counting cell and a detection component, wherein the counting cell is used for the first test sample to pass through, and the detection component is used to acquire the electronic signal when the first test sample passes through the counting cell.

[0064] In one embodiment of the impedance detection device 132, the impedance detection device 132 can be configured as a sheath current impedance detection device. As shown in FIG2, the sheath current impedance detection device 132 includes a counting cell 1321 having an aperture 1322 with an electrode 1323. The sheath current impedance detection device 132 detects the DC impedance generated when a particle in a first test sample passes through the aperture 1322 and outputs an electronic signal reflecting information about the particle passing through the aperture.

[0065] Specifically, after aspirating the blood sample to be tested, the sampling device 110 is driven by its drive device and moves to the first reaction cell of the sample preparation device 120, injecting a portion of the aspirated blood sample into the first reaction cell. The delivery line 1326 delivers the first test sample, after being treated with diluent in the first reaction cell, to the counting cell 1321. The sheath flow impedance detection device 132 may also be provided with a sheath fluid chamber (not shown) to provide sheath fluid to the counting cell 1321. In the counting cell 1321, the first test sample, enveloped by the sheath fluid, flows through the orifice 1322, causing the first test sample flow to become a thin stream, allowing particles contained in the first test sample to pass through the orifice 1322 one by one. The electrode 1323 is electrically connected to the DC power supply 1324, which provides DC power between the pair of electrodes 1323. During the DC power supply 1324, the impedance between a pair of electrodes 1323 can be detected. The electronic signal representing the impedance change (also known as the resistance signal) is amplified by amplifier 1325 and then sent to processor 140. Since the magnitude of the electronic signal corresponds to the volume (size) of the particles, the platelet distribution information of the blood sample to be tested can be obtained by processing the electronic signal through processor 140.

[0066] The optical detection device 131 includes a flow chamber, a light source, and a photodetector. The flow chamber is used for a second test sample to pass through, the light source is used to illuminate the second test sample passing through the flow chamber, and the photodetector is used to detect the optical signal generated by the second test sample after being illuminated by light as it passes through the flow chamber. Here, the optical signal includes at least a scattered light signal and / or a fluorescence signal, that is, at least one of a scattered light signal and a fluorescence signal.

[0067] In some embodiments, the photodetector in the optical detection device 131 may include a scattering light detector for detecting scattered light signals, such as forward scattered light signals, and a fluorescence detector for detecting fluorescence signals.

[0068] In some embodiments, the optical detection device 131 includes a forward-scattering detector for detecting forward-scattered light signals or a side-scattering detector for detecting side-scattered light signals. The optical detection device 130 preferably includes both a forward-scattering detector and a side-scattering detector.

[0069] In one embodiment of the optical detection device 131, as shown in FIG3, the optical detection device 131 has a light source 1311, a beam shaping assembly 1312, a flow chamber 1313, and a forward scattering detector 1314 arranged sequentially in a straight line. On one side of the flow chamber 1313, a dichroic mirror 1316 is arranged at a 45° angle to the straight line. Part of the side light emitted by the blood cells in the flow chamber 1313 passes through the dichroic mirror 1316 and is captured by a fluorescence detector 1315 arranged at a 45° angle to the dichroic mirror 1316 behind it, while the other part of the side light is reflected by the dichroic mirror 1316 and captured by a side scattering detector 1317 arranged at a 45° angle to the dichroic mirror 1316 in front of it.

[0070] In some embodiments, the light scattering signal detected near the incident light beam is generally referred to as a forward light scattering signal or a small-angle light scattering signal. In some embodiments, the forward light scattering signal can be detected at an angle of about 1° to about 10° with respect to the incident light beam. In other embodiments, the forward light scattering signal can be detected at an angle of about 2° to about 6° with respect to the incident light beam. The light scattering signal detected at about 90° with respect to the incident light beam is generally referred to as a side light scattering signal. In some embodiments, the side light scattering signal can be detected at an angle of about 65° to about 115° with respect to the incident light beam. Typically, the fluorescent signal emitted from blood cells stained with fluorescent dye is also generally detected at about 90° with respect to the incident light beam.

[0071] In some embodiments, as shown in FIG1, the blood cell analyzer 100 may further include a display device 150, a first housing 160, and a second housing 170. The display device 150 is configured to display information related to platelet count, such as platelet count values. A detection device 130 and a processor 140 are disposed, for example, inside the second housing 170. A sample preparation device 120 is disposed, for example, inside the first housing 160, and the display device 150 is disposed, for example, on the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer 100.

[0072] In some embodiments, the processor 140 is used to process and perform calculations on the data to obtain the desired results, such as generating a two-dimensional or three-dimensional scatter plot based on the collected optical information, and performing particle analysis on the scatter plot according to the gating method.

[0073] In some embodiments, the processor 140 may visualize intermediate or final processing results and then display them through the display device 150. For example, the display device 150 may include a user interface, and the processor 140 may output platelet count values ​​and display them on the user interface of the display device 150.

[0074] In some embodiments, the processor 140 includes, but is not limited to, devices such as a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), and a digital signal processor (DSP) used to interpret computer instructions and process data in computer software. For example, the processor 140 is used to execute various computer applications in a computer-readable storage medium, thereby enabling the blood cell analyzer 100 to perform corresponding detection procedures and analyze in real time the optical signals detected by the optical detection device 131 and the electronic signals detected by the impedance detection device 132.

[0075] This application proposes a technical solution for correcting platelet distribution or platelet count based on the pulse width of an optical signal.

[0076] It is understood that the optical signal acquired by the optical detection device 131 can be presented in the form of a light pulse signal. As shown in Figure 4, the peak value or pulse intensity of the light pulse signal can represent the voltage change amplitude when the particle passes through the photodetector, and the pulse width value of the light pulse signal can represent the time taken for the particle to pass through the photodetector. In the embodiments of this application, each light signal obtained by optical measurement can include a light pulse intensity and a light pulse width. For example, each scattered light signal (e.g., a forward scattered light signal) can include a scattered light pulse intensity (e.g., a forward scattered light pulse intensity) and a scattered light pulse width (e.g., a forward scattered light pulse width), and each fluorescence signal can include a fluorescence pulse intensity and a fluorescence pulse width.

[0077] Therefore, according to the embodiments of this application, the processor 140 is configured as follows:

[0078] Platelet distribution information of the blood sample to be tested is obtained based at least on the electronic signal of the first test sample; and

[0079] A first platelet count value (also known as a corrected platelet count value) of the blood sample to be tested is obtained based at least on the pulse width of the light signal corresponding to the platelets of the second test sample in at least one of the light signals (i.e., the light signal of scattered light and / or the light signal of fluorescence) (i.e., at least one of the scattered light pulse width of the scattered light signal of the platelet and the fluorescence pulse width of the fluorescence signal of the platelet) and platelet distribution information.

[0080] In the aforementioned blood cell analyzer 100, platelet distribution information is acquired through an impedance detection channel. This information is then combined with the scattered light pulse width and / or fluorescence pulse width corresponding to the platelets, acquired through a hemolysis optical detection channel, to obtain an accurate platelet count. This allows for accurate platelet counting in a single test, eliminating the need for retesting, thus reducing testing costs and improving efficiency.

[0081] The steps performed by the processor 140 to obtain platelet count values ​​are further described below with reference to some embodiments.

[0082] In some embodiments, the processor 140 is further configured to identify platelets in a second test sample based on optical signals to obtain platelet information of the second test sample, the platelet information including the optical signal of the at least one optical signal corresponding to the platelets of the second test sample.

[0083] In some embodiments, the processor can obtain a second platelet count value (also known as the uncorrected platelet count value) of the blood sample based on the platelet distribution information of the blood sample to be tested.

[0084] In some embodiments, the processor 140 is configured to obtain platelet distribution information of the blood sample to be tested based at least on the electronic signal of the first test sample, which may include: the processor 140 being configured to:

[0085] Platelet distribution information of the blood sample to be tested is obtained solely based on the electronic signal of the first test sample; or

[0086] Platelet distribution information of the blood sample to be tested is obtained based on the electronic signal of the first test sample and the optical signal of the second test sample.

[0087] For example, the second platelet count value, i.e. the platelet count value before correction, can be the platelet count value PLT-I obtained solely from the electronic signal detected by the impedance detection channel, or the first platelet count value can be the platelet count value PLT-H obtained from both the electronic signal detected by the impedance detection channel and the optical signal detected by the hemolysis optical detection channel.

[0088] It should be noted that in this embodiment, the second platelet count value (PLT-H) is not obtained by adding a dedicated optical platelet detection channel, but can be obtained using existing hemolysis optical detection channels, such as white blood cell differential channels or nucleated red blood cell detection channels. Since routine blood tests typically include at least impedance platelet detection and white blood cell differential, obtaining the second platelet count value in this embodiment will not significantly increase the additional testing cost.

[0089] Studies have shown that when platelet aggregation is present in the blood sample being tested, the second platelet count value (PLT-I) is unreliable. Furthermore, since the second platelet count value (PLT-H) is calculated based on the electronic signal of the first test sample and the optical information of the second test sample, it is also affected by platelet aggregation. Therefore, the embodiments of this application can correct the second platelet count value based on the pulse width of the optical signal to improve the accuracy of platelet counting. That is, by correcting the second platelet count value using the scattered light pulse width and / or fluorescence pulse width corresponding to the platelet obtained through the hemolysis optical detection channel, an accurate platelet count result, i.e., the first platelet count value, can be obtained. In this way, platelet count results can be obtained at low cost, quickly, and accurately.

[0090] In some embodiments, the processor 140 may be configured to obtain a first platelet count value of the blood sample to be tested based at least on the scattered light pulse width corresponding to the platelet in the optical information, preferably the forward scattered light pulse width, and the platelet distribution information of the blood sample to be tested.

[0091] In other embodiments, the processor 140 may be configured to obtain a first platelet count value of the blood sample to be tested based at least on the fluorescence pulse width corresponding to the platelet in the optical information and the platelet distribution information of the blood sample to be tested.

[0092] In some other embodiments, the processor 140 may be configured to obtain a first platelet count value of the blood sample to be tested based at least on the optical information of the scattered light pulse width and fluorescence pulse width corresponding to the platelets, and the platelet distribution information of the blood sample to be tested.

[0093] In some embodiments, the optical signal obtained by the hemolysis optical detection channel includes at least a forward-scattered light signal, wherein the at least one optical signal includes a forward-scattered light signal. That is, when the optical detection device 131 is used to perform optical measurement on the second test sample, it can obtain the forward-scattered light signal generated by each particle in the second test sample after being irradiated by light when passing through the flow chamber. Each forward-scattered light signal may include the forward-scattered light pulse intensity and the forward-scattered light pulse width.

[0094] In some embodiments, the processor 140 is configured to obtain a platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the at least one optical signal (i.e., at least one of the scattered light pulse width and fluorescence pulse width in the platelet information) and the platelet distribution information. This may include: the processor 140 being configured to obtain a first platelet count value (also referred to as a corrected platelet count value) of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward scattered light optical signal (i.e., the forward scattered light pulse width in the platelet information) and the platelet distribution information of the blood sample to be tested.

[0095] For example, processor 140 can be configured to correct the second platelet count value of the blood sample to be tested using at least the forward-scattered light pulse width of each platelet in the second test sample to obtain the first platelet count value.

[0096] Further, in some embodiments, the processor 140 is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information) and the platelet distribution information of the blood sample to be tested, which may include:

[0097] The processor 140 is configured to obtain a first platelet count value (also known as a corrected platelet count value) of the blood sample to be tested based on the pulse width and pulse intensity of the optical signal corresponding to the platelets of the second test sample in the optical signal of forward scattered light (i.e., the pulse intensity and pulse width of forward scattered light in the platelet information), and the platelet distribution information of the blood sample to be tested.

[0098] For example, processor 140 can be configured to further correct the second platelet count value of the blood sample to be tested using the forward scattered light pulse intensity and forward scattered light pulse width of each platelet in the second test sample, so as to obtain the first platelet count value.

[0099] The technical solution for correcting the second platelet count value proposed in this application will be further described below with reference to some embodiments.

[0100] In some embodiments, the processor 140 is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light optical signal (i.e., the forward-scattered light pulse width in the platelet information) and the platelet distribution information of the blood sample to be tested, which may include:

[0101] The second platelet count value (i.e., the platelet count value before correction) of the blood sample to be tested is obtained based on the platelet distribution information of the blood sample to be tested.

[0102] The platelet count correction value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the pulse width of the forward-scattered light in the platelet information); and

[0103] The first platelet count (i.e., the corrected platelet count) of the blood sample to be tested is obtained based on the second platelet count value (i.e., the platelet count value before correction) and the platelet count correction value.

[0104] In some embodiments, the processor 140 is configured to obtain a platelet count correction value for the blood sample to be tested based at least on the forward-scattered light pulse width in the platelet information, which may include: the processor 140 being configured to:

[0105] The characteristic parameters characterizing platelet aggregation are obtained based on the pulse width of the optical signal corresponding to the platelets in the second test sample (i.e., the pulse width of the forward-scattered light in the platelet information); and

[0106] The corrected platelet count value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

[0107] In some embodiments, the processor 140 is configured to obtain a platelet count correction value based on characteristic parameters characterizing platelet aggregation, which may include:

[0108] The processor 140 is configured to input characteristic parameters representing platelet aggregation into a preset calculation model to calculate a platelet count correction value.

[0109] In some embodiments, characteristic parameters representing platelet aggregation can be obtained by using a histogram based on the forward-scattered light pulse width. That is, the processor 140 is configured to obtain characteristic parameters representing platelet aggregation based on the pulse width of the light signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information). This configuration may include: the processor 140 being configured to:

[0110] Based on the pulse width of the forward-scattered light signal corresponding to the platelets in the second test sample (i.e., the forward-scattered light pulse width in the platelet information), a platelet distribution histogram of the second test sample is obtained. The horizontal axis of this platelet distribution histogram represents the pulse width of the forward-scattered light signal corresponding to the platelets in the second test sample (i.e., the forward-scattered light pulse width in the platelet information), and the vertical axis represents the number of platelets.

[0111] Characteristic parameters representing platelet aggregation were obtained based on the platelet distribution histogram.

[0112] The inventors of this application discovered through research that there are significant differences in the platelet distribution histograms of normal and abnormal samples in regions with larger forward-scattered light pulse widths. Analysis shows that platelet clusters appear in regions with larger forward-scattered light pulse widths, with different pulse widths representing platelet clusters of different sizes.

[0113] Therefore, preferably, obtaining the characteristic parameters representing platelet aggregation based on the platelet distribution histogram may include: obtaining the distribution information of platelet clusters whose pulse width (i.e., the pulse width of the forward scattered light in the platelet information) is greater than a preset threshold from the forward scattered light signal in the platelet distribution histogram, and obtaining the characteristic parameters representing platelet aggregation based on the distribution information of the platelet clusters, wherein the preset threshold is greater than or equal to the pulse width corresponding to the peak value of the platelet distribution histogram.

[0114] As one implementation method, based on the platelet distribution histogram, the platelet count correction value is obtained according to the following formula 1 (i.e., the preset calculation model):

[0115] Where, N k (i.e., 1…k) represents the number of platelet clusters corresponding to the k-th pulse width, f(FSW) k (i.e., 1…n) k ) represents the correction value of platelet aggregation corresponding to the k-th pulse width, that is, the number of platelets contained in the platelet aggregation corresponding to the k-th pulse width (a characteristic parameter characterizing platelet aggregation).

[0116] Furthermore, expectations can be used Equivalent substitution f(FSW) k That is, the platelet count correction value is obtained according to the following formula 2 (i.e., the preset calculation model):

[0117] In other implementations, relevant data from the entire platelet distribution histogram, or the portion of the platelet distribution histogram where the forward scattered light pulse width is greater than a preset threshold, can be input into a pre-trained machine learning model, such as a neural network model (i.e., a preset computational model), to obtain the output of the machine learning model as the platelet count correction value. In other embodiments, the machine learning model can be a machine learning model based on SVM (Support Vector Machine) and / or LDA (Support Vector Machine).

[0118] As another implementation, the characteristic parameter representing platelet aggregation can be the area under the curve to the right of a preset threshold in the platelet distribution histogram, and the platelet count correction value can be obtained based on this area. For example, the platelet count correction value can be the area under the curve.

[0119] In some embodiments, the processor 140 is configured to obtain a platelet count correction value for the blood sample to be tested based on characteristic parameters characterizing platelet aggregation, which may include:

[0120] The processor 140 is configured to weight the feature parameters using the pulse intensity of the light signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse intensity in the platelet information), and obtain the platelet count correction value of the blood sample to be tested based on the weighted feature parameters.

[0121] In some embodiments, the processor 140 is configured to obtain a platelet count correction value for the blood sample to be tested based at least on the pulse width of the light signal corresponding to the platelets of the second test sample in the forward-scattered light light signal (i.e., the forward-scattered light pulse width in the platelet information), which may include: the processor 140 being configured to:

[0122] Characteristic parameters representing platelet aggregation are obtained based on the pulse width and pulse intensity of the optical signal corresponding to platelets in the forward-scattered light signal (i.e., the forward-scattered light pulse intensity and pulse width in the platelet information); and

[0123] The corrected platelet count value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

[0124] Similarly, processor 140 is configured to obtain a platelet count correction value based on characteristic parameters characterizing platelet aggregation, which may include:

[0125] The processor 140 is configured to input characteristic parameters representing platelet aggregation into a preset calculation model to calculate a platelet count correction value.

[0126] Further, the processor 140 is configured to obtain characteristic parameters characterizing platelet aggregation based on the pulse width and pulse intensity of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light optical signal (i.e., the forward-scattered light pulse intensity and forward-scattered light pulse width in the platelet information), which may include: the processor 140 is configured to:

[0127] The scatter plot of the platelets of the second test sample is obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets of the second test sample in the optical signal of forward scattered light (i.e., the pulse intensity and pulse width of forward scattered light in the platelet information). As shown in Figure 5.

[0128] The platelet aggregation characteristic region R is obtained from the platelet scatter plot, and the scatter points in the platelet aggregation characteristic region represent platelet clusters in the second test sample; and

[0129] Characteristic parameters for platelet aggregation are obtained based on the scatter distribution characteristics in the platelet aggregation feature region R.

[0130] Figure 5 shows a scatter plot of abnormal samples with platelet aggregation, and Figure 6 shows a scatter plot of normal samples without platelet aggregation. The horizontal axis represents the forward scattered light pulse width (FSW), and the vertical axis represents the forward scattered light pulse intensity (FSC). From the FSW-FSC distribution (i.e., the scatter plots) of the platelet aggregation samples and normal samples, it can be seen that a characteristic platelet aggregation region R exists. The scatter plot of the platelet aggregation samples shows more particles with larger forward scattered light pulse widths and higher forward scattered light pulse intensities within this region R compared to normal samples. This is because multiple platelet cells in the platelet aggregation samples adhere together, generating pulse signals with both larger forward scattered light pulse widths and intensities when passing through an optical detector. Therefore, by combining the distribution characteristics of the forward scattered light pulse width in this region R, characteristic parameters characterizing platelet aggregation can be obtained.

[0131] As one implementation method, based on the forward scattered light pulse intensity and forward scattered light pulse width of each platelet in the second test sample, the platelet count correction value is obtained according to the following formula 3:

[0132] Where, N k f(FSW) represents the number of platelet clusters corresponding to the k-th pulse width. k f(FSC) represents the correction value for platelet aggregation corresponding to the k-th pulse width, i.e., the number of platelets contained in the platelet aggregation corresponding to the k-th pulse width. k ) represents the pair f(FSW) obtained based on the intensity of the k-th pulse. k The weighting coefficients of the weighted average.

[0133] Furthermore, expectations can be used Equivalent substitution f(FSW) k That is, the platelet count correction value is obtained according to the following formula 4 (i.e., the preset calculation model):

[0134] In one example, the weighting coefficient f(FSC) can be determined based on the average scattered light pulse intensity of the scatter points in the platelet aggregation feature region R. k ).

[0135] In another example, the weighting coefficients f(FSC) can be determined using the following piecewise linear function (Formula 5). k ):

[0136] Where x represents the intensity of the scattered light pulse, and k1, k2, k3, b1, b2, b3, a1, a2, and a3 are all constants.

[0137] As another implementation, the relevant data of the entire FSW-FSC scatter plot or the platelet aggregation feature region R in the scatter plot can be input into a pre-trained machine learning model, such as a neural network model, to obtain the output of the machine learning model as the platelet count correction value.

[0138] In other embodiments, the forward scattered light pulse width of each particle, especially each platelet, in the second test sample can be directly input into a machine learning model, such as a neural network model, to obtain the output of the machine learning model as a platelet count correction value, without having to calculate the characteristic parameters characterizing platelet aggregation.

[0139] In some embodiments, the processor 140 is configured to obtain a first platelet count value of the blood sample to be tested based on a second platelet count value and a platelet count correction value, which may include:

[0140] The processor 140 is configured to calculate the sum of the second platelet count value (i.e., the platelet count value before correction) and the platelet count correction value, and output it as the first platelet count value (i.e., the corrected platelet count value) of the blood sample to be tested.

[0141] In other embodiments, the product of the second platelet count value and the platelet count correction value may also be calculated and output as the first platelet count value.

[0142] As one implementation method, the second platelet count can be calculated using the following formula 6 or formula 7:

[0143] Wherein, PLT1 is the second platelet count value before correction, and PLT2 is the first platelet count value after correction.

[0144] In some embodiments, the processor 140 is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the at least one optical signal (i.e., at least one of the scattered light pulse width and the fluorescence pulse width in the platelet information) and the platelet distribution information of the blood sample to be tested, which may include:

[0145] The pulse width of the light signal corresponding to the platelets of the second test sample (i.e., at least one of the scattered light pulse width and fluorescence pulse width in the platelet information) is input into the machine learning model to obtain the output of the machine learning model as the first platelet count value of the blood sample to be tested.

[0146] In some embodiments, when the optical detection device 131 is used to perform optical measurements on the second test sample, the photodetector can also be used to detect the side-scattered light signal generated by each particle in the second test sample after being irradiated by light when passing through the flow chamber. That is, the optical signal of the second test sample can also include the light signal of the side-scattered light of the second test sample.

[0147] As some implementations, the optical signal may include a scattered light signal and a fluorescence light signal, wherein the scattered light signal includes a forward-scattered light signal. Accordingly, the processor 140 may also be configured to: obtain platelet information of the second test sample based on the forward-scattered light signal and the fluorescence light signal, that is, obtain the light signal corresponding to the platelets of the second test sample from the at least one light signal.

[0148] For example, the processor 140 can be configured to: generate a first scatter plot based on the pulse intensity of the forward-scattered light signal and the pulse intensity of the fluorescence light signal, identify platelets in the second test sample from the first scatter plot, and obtain platelet information of the second test sample, that is, obtain the light signal corresponding to the platelets in the second test sample from the forward-scattered light signal and / or the fluorescence light signal.

[0149] In other implementations, the optical signal includes a scattered light signal, which includes a forward-scattered light signal and a side-scattered light signal. Accordingly, the processor 140 can also be configured to: obtain platelet information of the second test sample based on the forward-scattered light signal and the side-scattered light signal, that is, obtain the light signal corresponding to the platelets of the second test sample from the at least one of the light signals.

[0150] For example, the processor 140 can be configured to: generate a second scatter plot based on the pulse intensity of the forward-scattered light signal and the pulse intensity of the side-scattered light signal, identify platelets in the second test sample from the second scatter plot, and obtain platelet information of the second test sample, that is, obtain the light signal corresponding to the platelets in the forward-scattered light signal and / or the side-scattered light signal.

[0151] In some other implementations, the optical signals include scattered light signals and fluorescence light signals, with the scattered light signals including forward-scattered light signals and side-scattered light signals. Accordingly, the processor 140 can also be configured to: obtain platelet information of the second test sample based on the forward-scattered light signals, the side-scattered light signals, and the fluorescence light signals, that is, obtain the light signal corresponding to the platelets of the second test sample from the at least one of the light signals.

[0152] The embodiments of this application are verified through specific examples below.

[0153] A total of 213 patients diagnosed with EDTA-dependent pseudothrombocytopenia were collected. Venous blood was collected from these 213 patients using sodium citrate anticoagulant tubes, and routine PLT optical detection was performed on them using a BC-700 blood cell analyzer manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd., to obtain reference platelet count values ​​(i.e., reference values).

[0154] Simultaneously, venous blood was collected from these 213 patients using EDTA anticoagulant tubes, and impedance detection (impedance detection channel) and optical detection (hemolysis optical detection channel) were performed using a Mindray BC-700 blood cell analyzer according to the embodiments of this application to obtain corresponding electronic and optical signals. The optical signals here include forward-scattered light signals, side-scattered light signals, and fluorescence signals. Based on the electronic signals obtained from the impedance detection channel, the pre-correction platelet count value (i.e., the second platelet count value PLT-I) of each sample was obtained, and the correlation and deviation between the reference platelet count value and the second platelet count value PLT-I were then obtained, as shown in Figures 7(a) to 7(c). Figure 7(a) shows the correlation curve between the reference platelet count value and the pre-correction second platelet count value; Figure 7(b) shows the absolute deviation between the reference platelet count value and the pre-correction second platelet count value; and Figure 7(c) shows the relative deviation between the reference platelet count value and the pre-correction second platelet count value.

[0155] As can be seen from Figures 7(a) to 7(c), for these 213 patient samples, the correlation between the uncorrected second platelet count and the reference platelet count was 0.905, and the average deviation was -50.6%.

[0156] In the first example, the platelet information of the second test sample is obtained based on the forward scattered light signal FS and the side scattered light signal SS obtained from the hemolysis optical detection channel, and the corrected first platelet count value of each sample is calculated using the above formula 6 based on the forward scattered light pulse width in the platelet information.

[0157] Figures 8(a) to 8(c) are derived from the reference platelet count and the first platelet count for each sample. Figure 8(a) shows the correlation curve between the reference platelet count and the first platelet count, Figure 8(b) shows the absolute deviation between the reference platelet count and the first platelet count, and Figure 8(c) shows the relative deviation between the reference platelet count and the first platelet count.

[0158] As can be seen from Figures 8(a) to 8(c), the platelet region is obtained by using the forward scattered light signal FS and the side scattered light signal SS obtained through the hemolysis optical detection channel of the blood cell analyzer. The second platelet count value is corrected according to the forward scattered light pulse width FSW of the particles in the platelet region. The correlation between the first platelet count value and the reference platelet count value after correction is improved from 0.905 to 0.909, and the average deviation is improved from -50.6% to -8.3%.

[0159] In the second example, platelet information of the second test sample is obtained based on the forward scattered light signal FS and fluorescence signal FL obtained from the hemolysis optical detection channel. Based on the forward scattered light pulse width in this platelet information, the corrected first platelet count value for each sample is calculated using the above formula 6.

[0160] Figures 9(a) to 9(c) are derived from the reference platelet count and the first platelet count for each sample. Figure 9(a) shows the correlation curve between the reference platelet count and the first platelet count, Figure 9(b) shows the absolute deviation between the reference platelet count and the first platelet count, and Figure 9(c) shows the relative deviation between the reference platelet count and the first platelet count.

[0161] As can be seen from Figures 9(a) to 9(c), the platelet region is obtained by using the forward scattered light signal FS and fluorescence signal FL obtained through the hemolysis optical detection channel of the blood cell analyzer. The second platelet count value is corrected based on the forward scattered light pulse width FSW of the particles in the platelet region. The correlation between the corrected first platelet count value and the reference platelet count value is improved from 0.905 to 0.923, and the average deviation is improved from -50.6% to -8.1%.

[0162] In the third example, the platelet information of the second test sample is obtained based on the forward scattered light signal FS and the side scattered light signal SS obtained from the hemolysis optical detection channel. Based on the forward scattered light pulse width and forward scattered light pulse intensity in the platelet information, the corrected first platelet count value of each sample is calculated using the above formula 7.

[0163] Figures 10(a) to 10(c) are derived from the reference platelet count and the first platelet count for each sample. Figure 10(a) shows the correlation curve between the reference platelet count and the first platelet count, Figure 10(b) shows the absolute deviation between the reference platelet count and the first platelet count, and Figure 10(c) shows the relative deviation between the reference platelet count and the first platelet count.

[0164] As can be seen from Figures 10(a) to 10(c), the platelet region is obtained by using the forward scattered light signal FS and the side scattered light signal SS obtained through the hemolysis optical detection channel of the blood cell analyzer. The second platelet count value is corrected based on the forward scattered light pulse width FSW and the forward scattered light pulse intensity FSC of the particles in the platelet region. The correlation between the first platelet count value and the reference platelet count value after correction is improved from 0.905 to 0.944, and the average deviation is improved from -50.6% to -6.5%.

[0165] In the fourth example, the platelet information of the second test sample is obtained based on the forward scattered light signal FS and the fluorescence signal FL obtained from the hemolysis optical detection channel. Based on the forward scattered light pulse width and forward scattered light pulse intensity in the platelet information, the corrected first platelet count value of each sample is calculated using the above formula 7.

[0166] Figures 11(a) to 11(c) are derived from the reference platelet count and the first platelet count for each sample. Figure 11(a) shows the correlation curve between the reference platelet count and the first platelet count, Figure 11(b) shows the absolute deviation between the reference platelet count and the first platelet count, and Figure 11(c) shows the relative deviation between the reference platelet count and the first platelet count.

[0167] As shown in Figures 11(a) to 11(c), for these 213 patient samples, the platelet region was obtained using the forward scattered light signal FS and fluorescence signal FL obtained through the hemolysis optical detection channel of the blood cell analyzer. The second platelet count value was corrected based on the forward scattered light pulse width FSW and forward scattered light pulse intensity FSC of the particles in the platelet region. The correlation between the corrected first platelet count value and the reference platelet count value improved from 0.905 to 0.961, and the average deviation improved from -50.6% to -5.6%.

[0168] In summary, for samples exhibiting platelet aggregation, especially those with EDTA-dependent pseudothrombocytopenia, the embodiments of this application can yield relatively accurate platelet counts.

[0169] This application embodiment also provides a corresponding platelet counting method, as shown in Figure 12. The platelet counting method 1400 includes:

[0170] Step 1410: Collect the blood sample to be tested;

[0171] Step 1420: Mix a portion of the blood sample to be tested with a diluent to prepare a first test sample, and mix another portion of the blood sample to be tested with a hemolysin and a fluorescent dye to prepare a second test sample;

[0172] Step 1430: The impedance of the first test sample is measured to obtain the electronic signal of the first test sample;

[0173] Step 1440: Perform optical measurement on the second test sample to obtain the optical signal of the second test sample. Here, the optical signal includes at least one of the optical signals of scattered light and fluorescence.

[0174] Step 1450: Obtain platelet distribution information of the blood sample to be tested based at least on the electronic signal of the first test sample;

[0175] Step 1460: Obtain the first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one optical signal and the platelet distribution information.

[0176] In some embodiments, the platelet counting method 1400 may further include identifying platelets in a second test sample based on optical signals to obtain platelet information of the second test sample, the platelet information including the optical signal corresponding to the platelets in the second test sample from the at least one optical signal.

[0177] In some embodiments, step 1450 may include:

[0178] The platelet distribution information is obtained solely based on the electronic signal of the first test sample; or

[0179] The platelet distribution information is obtained based on the electronic signal of the first test sample and the optical signal of the second test sample.

[0180] In some embodiments, in step 1440, the optical signal includes at least a forward-scattered light signal.

[0181] Here, step 1460 may include: obtaining a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light optical signal (i.e., the forward-scattered light pulse width in the platelet information) and the platelet distribution information.

[0182] Further, obtaining the platelet count value of the blood sample to be tested, based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information) and the platelet distribution information, may include:

[0183] The first platelet count value of the blood sample to be tested is obtained based on the pulse width and pulse intensity of the light signal corresponding to the platelets of the second test sample in the forward-scattered light light signal (i.e., the forward-scattered light pulse intensity and forward-scattered light pulse width in the platelet information) and the platelet distribution information.

[0184] In some embodiments, obtaining a first platelet count value of the blood sample to be tested, based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information) and the platelet distribution information, may include:

[0185] The second platelet count value of the blood sample to be tested is obtained based on the platelet distribution information.

[0186] The platelet count correction value of the blood sample to be tested is obtained at least based on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information); and

[0187] The first platelet count value of the blood sample to be tested is obtained based on the second platelet count value and the platelet count correction value.

[0188] In some embodiments, obtaining the platelet count correction value of the blood sample to be tested, at least based on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light optical signal (i.e., the forward-scattered light pulse width in the platelet information), may include:

[0189] The characteristic parameters characterizing platelet aggregation are obtained based on the pulse width of the optical signal corresponding to the platelets in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information); and

[0190] The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

[0191] In some embodiments, the characteristic parameters characterizing platelet aggregation are obtained based on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal (i.e., the forward-scattered light pulse width in the platelet information), which may include:

[0192] A platelet distribution histogram of the second test sample is obtained based on the pulse width of the optical signal corresponding to the platelets in the forward-scattered light signal. The horizontal axis of this platelet distribution histogram represents the pulse width of the optical signal corresponding to the platelets in the second test sample (i.e., the forward-scattered light pulse width), and the vertical axis represents the number of platelets.

[0193] The characteristic parameters representing platelet aggregation are obtained based on the platelet distribution histogram.

[0194] Preferably, obtaining the characteristic parameters representing platelet aggregation based on the platelet distribution histogram may include:

[0195] The distribution information of platelet clusters is obtained from the platelet distribution histogram. The pulse width of the forward-scattered light signal corresponding to the platelet of the second test sample (i.e., the pulse width of the forward-scattered light in the platelet information) is greater than a preset threshold. Based on the distribution information, the characteristic parameters characterizing platelet aggregation are obtained, wherein the preset threshold is greater than or equal to the pulse width corresponding to the peak value of the platelet distribution histogram.

[0196] In some embodiments, obtaining the platelet count correction value of the blood sample to be tested based on the characteristic parameters characterizing platelet aggregation may include:

[0197] The feature parameters are weighted using the pulse intensity of the light signal corresponding to the platelets in the second test sample (i.e., the pulse intensity of the forward-scattered light in the platelet information), and the platelet count correction value of the blood sample to be tested is obtained based on the weighted feature parameters.

[0198] In some embodiments, obtaining the platelet count correction value of the blood sample to be tested, at least based on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light optical signal (i.e., the forward-scattered light pulse width in the platelet information), may include:

[0199] Characteristic parameters representing platelet aggregation are obtained based on the pulse width and pulse intensity of the optical signal corresponding to platelets in the forward-scattered light signal (i.e., the forward-scattered light pulse intensity and pulse width in the platelet information); and

[0200] The platelet count correction value is obtained based on the characteristic parameters representing platelet aggregation.

[0201] In some embodiments, characteristic parameters characterizing platelet aggregation are obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal, which may include:

[0202] The scatter plot of platelets in the second test sample is obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the forward-scattered light signal.

[0203] Platelet aggregation characteristic regions are obtained from the platelet scatter plot, and the scatter points in the platelet aggregation characteristic regions represent platelet clusters in the second test sample; and

[0204] The characteristic parameters characterizing platelet aggregation are obtained based on the scatter distribution characteristics in the platelet aggregation feature region.

[0205] In some embodiments, obtaining the platelet count correction value of the blood sample to be tested based on the characteristic parameters characterizing platelet aggregation may include:

[0206] The characteristic parameters representing platelet aggregation are input into a preset calculation model to calculate the corrected platelet count value of the blood sample to be tested.

[0207] In some embodiments, obtaining a first platelet count value for the blood sample to be tested based on the second platelet count value and the platelet count correction value may include:

[0208] Calculate the sum of the second platelet count value and the platelet count correction value, and output it as the first platelet count value.

[0209] In some embodiments, obtaining the platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one optical signal (i.e., at least one of the scattered light pulse width and fluorescence pulse width in the platelet information) and the platelet distribution information may include:

[0210] The pulse width of the optical signal corresponding to the platelets of the second test sample in the at least one optical signal is input into the machine learning model to obtain the output of the machine learning model as the first platelet count value of the blood sample to be tested.

[0211] In some embodiments, the optical signal may include a scattered light signal and a fluorescence light signal, wherein the scattered light signal includes a forward-scattered light signal. Here, method 1400 may further include: obtaining platelet information of the second test sample based on the forward-scattered light signal and the fluorescence light signal, that is, obtaining the light signal corresponding to the platelets of the second test sample from the at least one light signal.

[0212] For example, method 1400 may include: generating a first scatter plot based on the pulse intensity of the forward-scattered light signal and the pulse intensity of the fluorescence light signal, identifying platelets in the second test sample from the first scatter plot, so as to obtain platelet information of the second test sample, that is, obtaining the light signal corresponding to the platelets in the forward-scattered light signal and / or the fluorescence light signal.

[0213] In other embodiments, the optical signal may include a scattered light signal, which includes a forward-scattered light signal and a side-scattered light signal. Here, method 1400 may further include: obtaining platelet information of the second test sample based on the forward-scattered light signal and the side-scattered light signal, i.e., obtaining the light signal corresponding to the platelets of the second test sample from the at least one of the light signals;

[0214] For example, method 1400 may include: generating a second scatter plot based on the pulse intensity of the forward-scattered light signal and the pulse intensity of the side-scattered light signal, identifying platelets in the second test sample from the second scatter plot, so as to obtain platelet information of the second test sample, that is, obtaining the light signal corresponding to the platelets in the second test sample from the forward-scattered light signal and / or the side-scattered light signal.

[0215] In other embodiments, the optical signal may include a scattered light signal and a fluorescence light signal, wherein the scattered light signal includes a forward-scattered light signal and a side-scattered light signal. Here, method 1400 may further include: obtaining platelet information of the second test sample based on the forward-scattered light signal, the side-scattered light signal, and the fluorescence light signal, that is, obtaining the light signal corresponding to the platelets of the second test sample from the at least one of the light signals.

[0216] For further embodiments of the platelet counting method 1400 provided in this application, please refer to the above description of the blood cell analyzer 100 and its embodiments.

[0217] All features or combinations of features mentioned above in the specification, drawings, and claims may be used in any combination or individually, provided they are meaningful within the scope of this application and do not contradict each other. The advantages and features described in the blood cell analyzer provided in the embodiments of this application are applicable in a corresponding manner to the platelet counting method provided in the embodiments of this application, and vice versa.

[0218] The above description is merely a preferred embodiment of this application and does not limit the patent scope of this application. All equivalent modifications made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A platelet counting method, comprising: Collect the blood sample to be tested; A portion of the blood sample to be tested is mixed with a diluent to prepare a first test sample, and another portion of the blood sample to be tested is mixed with a hemolysin and a fluorescent dye to prepare a second test sample; Impedance measurement is performed on the first test sample to obtain the electronic signal of the first test sample; Optical measurements are performed on the second test sample to obtain the optical signal of the second test sample, the optical signal including at least one of scattered light signal and fluorescence signal; The platelet distribution information of the blood sample to be tested is obtained at least based on the electronic signal of the first test sample; and The first platelet count value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one optical signal and the platelet distribution information.

2. The method according to claim 1, wherein, The optical signal includes the light signal of the scattered light and the light signal of the fluorescence, wherein the light signal of the scattered light includes the light signal of the forward scattered light; the method further includes: obtaining the light signal corresponding to the platelet of the second test sample from the at least one light signal based on the light signal of the forward scattered light and the light signal of the fluorescence; or, The optical signal includes the optical signal of the scattered light, which includes the optical signal of the forward-scattered light and the optical signal of the side-scattered light; the method further includes: obtaining the optical signal corresponding to the platelets of the second test sample from the at least one optical signal based on the optical signal of the forward-scattered light and the optical signal of the side-scattered light; or, The optical signal includes the light signal of the scattered light and the light signal of the fluorescence, wherein the light signal of the scattered light includes the light signal of the forward scattered light and the light signal of the side scattered light; the method further includes: obtaining the light signal corresponding to the platelet of the second test sample from the at least one light signal based on the light signal of the forward scattered light, the light signal of the side scattered light and the light signal of the fluorescence.

3. The method according to claim 1 or 2, further comprising identifying platelets in the second test sample based on the optical signal to obtain an optical signal in the at least one optical signal corresponding to the platelets in the second test sample; Preferably, the optical signal includes the optical signal of the scattered light and the optical signal of the fluorescence, the optical signal of the scattered light includes the optical signal of the forward scattered light, and the platelets in the second test sample are identified based on the optical signal to obtain the optical signal corresponding to the platelets in the second test sample from the at least one optical signal, including: A first scatter plot is generated based on the pulse intensity of the forward-scattered light signal and the pulse intensity of the fluorescence light signal. Platelets in the second test sample are identified from the first scatter plot to obtain the light signal corresponding to the platelets in the second test sample from the forward-scattered light signal and / or the fluorescence light signal. Alternatively, preferably, the optical signal includes the optical signal of the scattered light, which includes the optical signal of the forward scattered light and the optical signal of the side scattered light; Identifying platelets in the second test sample based on the optical signal to obtain the optical signal corresponding to the platelets in the second test sample from the at least one optical signal includes: generating a second scatter plot based on the pulse intensity of the forward-scattered light signal and the pulse intensity of the side-scattered light signal, identifying platelets in the second test sample from the second scatter plot to obtain the optical signal corresponding to the platelets in the second test sample from the forward-scattered light signal and / or the side-scattered light signal.

4. The method according to any one of claims 1 to 3, wherein, The platelet distribution information of the blood sample to be tested is obtained based at least on the electronic signal of the first test sample, including: The platelet distribution information is obtained solely based on the electronic signal of the first test sample; or The platelet distribution information is obtained based on the electronic signal of the first test sample and the optical signal of the second test sample.

5. The method according to any one of claims 1 to 4, wherein, The at least one optical signal includes a forward-scattered optical signal; Obtaining a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one optical signal and the platelet distribution information includes: obtaining a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward scattered light optical signal and the platelet distribution information.

6. The method of claim 5, wherein, The first platelet count value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward-scattered light signal and the platelet distribution information, including: The first platelet count value of the blood sample to be tested is obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets of the second test sample in the forward scattered light optical signal and the platelet distribution information.

7. The method of claim 5 or 6, wherein, The first platelet count value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward scattered light optical signal and the platelet distribution information, including: The second platelet count value of the blood sample to be tested is obtained based on the platelet distribution information. The platelet count correction value of the blood sample to be tested is obtained at least based on the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward-scattered light optical signal; and The first platelet count value of the blood sample to be tested is obtained based on the second platelet count value and the platelet count correction value.

8. The method according to claim 7, wherein, The platelet count correction value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal, including: The characteristic parameters characterizing platelet aggregation are obtained based on the pulse width of the optical signal corresponding to the platelets in the second test sample within the forward-scattered light signal; and The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

9. The method according to claim 8, wherein, Characteristic parameters representing platelet aggregation are obtained based on the pulse width of the optical signal corresponding to the platelets in the second test sample within the forward-scattered light signal, including: Based on the pulse width of the optical signal corresponding to the platelets in the forward-scattered light signal of the second test sample, a platelet distribution histogram of the second test sample is obtained. The horizontal axis of this platelet distribution histogram is the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward-scattered light signal, and the vertical axis is the number of platelets. The characteristic parameters representing platelet aggregation are obtained based on the platelet distribution histogram. Preferably, obtaining the characteristic parameters representing platelet aggregation based on the platelet distribution histogram includes: obtaining the distribution information of platelet clusters in the forward-scattered light signal corresponding to the platelets of the second test sample whose pulse width is greater than a preset threshold from the platelet distribution histogram, and obtaining the characteristic parameters representing platelet aggregation based on the distribution information of the platelet clusters, wherein the preset threshold is greater than or equal to the pulse width corresponding to the peak value of the platelet distribution histogram.

10. The method according to claim 8 or 9, wherein, The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation, including: The characteristic parameters are weighted using the pulse intensity of the light signal corresponding to the platelets of the second test sample in the forward-scattered light signal, and the platelet count correction value of the blood sample to be tested is obtained based on the weighted characteristic parameters.

11. The method according to claim 7, wherein, The platelet count correction value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal, including: Characteristic parameters representing platelet aggregation are obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the second test sample from the forward-scattered light signal; and The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

12. The method according to claim 11, wherein, Characteristic parameters for platelet aggregation are obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the second test sample from the forward-scattered light signal, including: A scatter plot of platelets in the second test sample is obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the forward-scattered light. Platelet aggregation characteristic regions are obtained from the platelet scatter plot, and the scatter points in the platelet aggregation characteristic regions represent platelet clusters in the second test sample; and The characteristic parameters characterizing platelet aggregation are obtained based on the scatter distribution characteristics in the platelet aggregation feature region.

13. The method according to any one of claims 8 to 12, wherein, The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation, including: The characteristic parameters representing platelet aggregation are input into a preset calculation model to calculate the corrected platelet count value of the blood sample to be tested.

14. The method according to any one of claims 7 to 13, wherein, The first platelet count value of the blood sample to be tested is obtained based on the second platelet count value and the platelet count correction value, including: Calculate the sum of the second platelet count value and the platelet count correction value, and output it as the first platelet count value.

15. The method according to any one of claims 1 to 4, wherein, The first platelet count value of the blood sample to be tested is obtained based on at least the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one of the at least one optical signals and the platelet distribution information, including: The pulse width of the light signal corresponding to the platelets of the second test sample in the at least one light signal is input into the machine learning model to obtain the output of the machine learning model as the first platelet count value of the blood sample to be tested.

16. A blood cell analyzer, comprising: A sampling device is used to collect blood samples for testing. A sample preparation apparatus for mixing a portion of the blood sample to be tested with a diluent to prepare a first test sample, and for mixing another portion of the blood sample to be tested with a hemolysin and a fluorescent dye to prepare a second test sample; An impedance detection device includes a counting cell and a detection component. The counting cell is used to allow a first test sample to pass through, and the detection component is used to acquire the electronic signal when the first test sample passes through the counting cell. An optical detection device includes a flow chamber, a light source, and a photodetector. The flow chamber is used for a second test sample to pass through. The light source is used for illuminating the second test sample passing through the flow chamber with light. The photodetector is used for detecting the optical signal generated by the second test sample after being illuminating the light when passing through the flow chamber. The optical signal includes at least one of scattered light signal and fluorescent light signal. The processor is configured as follows: The platelet distribution information of the blood sample to be tested is obtained at least based on the electronic signal of the first test sample; and The first platelet count value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in at least one optical signal and the platelet distribution information.

17. The blood cell analyzer according to claim 16, wherein, The optical signal includes the scattered light signal and the fluorescence light signal, the scattered light signal including the forward scattered light signal; the processor is further configured to: obtain, based on the forward scattered light signal and the fluorescence light signal, the light signal corresponding to the platelet of the second test sample from the at least one light signal; or, The optical signal includes the optical signal of the scattered light, which includes the optical signal of the forward-scattered light and the optical signal of the side-scattered light; the processor is further configured to: obtain, based on the optical signal of the forward-scattered light and the optical signal of the side-scattered light, the optical signal corresponding to the platelet of the second test sample from the at least one optical signal; or, The optical signal includes the light signal of the scattered light and the light signal of the fluorescence, wherein the light signal of the scattered light includes the light signal of the forward scattered light and the light signal of the side scattered light; the processor is further configured to: obtain, based on the light signal of the forward scattered light, the light signal of the side scattered light and the light signal of the fluorescence, the light signal of the at least one light signal corresponding to the platelet of the second test sample.

18. The blood cell analyzer according to claim 16 or 17, wherein, The processor is further configured to identify platelets in the second test sample based on the optical signal, so as to obtain the optical signal in the at least one optical signal corresponding to the platelets in the second test sample; Preferably, the optical signal includes the optical signal of the scattered light and the optical signal of the fluorescence, the optical signal of the scattered light includes the optical signal of the forward scattered light, and the processor is further configured to identify platelets in the second test sample based on the optical signal to obtain the optical signal corresponding to the platelets in the second test sample from the at least one optical signal, including: the processor is further configured to: generate a first scatter plot based on the pulse intensity of the optical signal of the forward scattered light and the pulse intensity of the optical signal of the fluorescence, identify platelets in the second test sample from the first scatter plot to obtain the optical signal corresponding to the platelets in the second test sample from the optical signal of the forward scattered light and / or the optical signal of the fluorescence; Alternatively, preferably, the optical signal includes the optical signal of the scattered light, which includes the optical signal of the forward-scattered light and the optical signal of the side-scattered light; the processor is further configured to identify platelets in the second test sample based on the optical signal to obtain the optical signal corresponding to the platelets in the second test sample from the at least one optical signal, including: the processor is further configured to: generate a second scatter plot based on the pulse intensity of the optical signal of the forward-scattered light and the pulse intensity of the optical signal of the side-scattered light, and identify platelets in the second test sample from the second scatter plot to obtain the optical signal corresponding to the platelets in the second test sample from the optical signal of the forward-scattered light and / or the optical signal of the side-scattered light.

19. The blood cell analyzer according to any one of claims 16 to 18, wherein, The processor is configured to obtain platelet distribution information of the blood sample to be tested based at least on the electronic signal of the first test sample, including: the processor is configured to: The platelet distribution information is obtained solely based on the electronic signal of the first test sample; or The platelet distribution information is obtained based on the electronic signal of the first test sample and the optical signal of the second test sample.

20. The blood cell analyzer according to any one of claims 16 to 19, wherein, The at least one optical signal includes a forward-scattered light optical signal; The processor is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the at least one optical signal and the platelet distribution information, including: the processor is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward scattered light optical signal and the platelet distribution information.

21. The blood cell analyzer according to claim 20, wherein, The processor is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal and the platelet distribution information, including: The processor is configured to obtain a first platelet count value of the blood sample to be tested based on the pulse width and pulse intensity of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light optical signal and the platelet distribution information.

22. The blood cell analyzer according to claim 20 or 21, wherein, The processor is configured to obtain a first platelet count value of the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets in the forward-scattered light and the platelet distribution information, including: the processor is configured to: The second platelet count value of the blood sample to be tested is obtained based on the platelet distribution information. The platelet count correction value of the blood sample to be tested is obtained based at least on the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward-scattered light signal; and The first platelet count value of the blood sample to be tested is obtained based on the second platelet count value and the platelet count correction value.

23. The blood cell analyzer according to claim 22, wherein, The processor is configured to obtain a platelet count correction value for the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal, including: the processor is configured to: The characteristic parameters characterizing platelet aggregation are obtained based on the pulse width of the optical signal corresponding to the platelets in the second test sample within the forward-scattered light signal; and The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

24. The blood cell analyzer according to claim 23, wherein, The processor is configured to obtain characteristic parameters characterizing platelet aggregation based on the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward-scattered light signal, including: the processor is configured to: Based on the pulse width of the optical signal corresponding to the platelets in the forward-scattered light signal of the second test sample, a platelet distribution histogram of the second test sample is obtained. The horizontal axis of this platelet distribution histogram is the pulse width of the optical signal corresponding to the platelets in the second test sample in the forward-scattered light signal, and the vertical axis is the number of platelets. The characteristic parameters representing platelet aggregation are obtained based on the platelet distribution histogram. Preferably, the processor is configured to obtain the characteristic parameters representing platelet aggregation based on the platelet distribution histogram, including: the processor is configured to: obtain from the platelet distribution histogram the distribution information of platelet clusters whose pulse width of the optical signal corresponding to the platelets of the second test sample in the forward scattered light signal is greater than a preset threshold, and obtain the characteristic parameters representing platelet aggregation based on the distribution information of the platelet clusters, wherein the preset threshold is greater than or equal to the pulse width corresponding to the peak value of the platelet distribution histogram.

25. The blood cell analyzer according to claim 23 or 24, wherein, The processor is configured to obtain a platelet count correction value for the blood sample to be tested based on the characteristic parameters characterizing platelet aggregation, including: The processor is configured to weight the feature parameters using the pulse intensity of the light signal in the forward-scattered light that corresponds to the platelets of the second test sample, and to obtain a platelet count correction value for the blood sample based on the weighted feature parameters.

26. The blood cell analyzer according to claim 22, wherein, The processor is configured to obtain a platelet count correction value for the blood sample to be tested based at least on the pulse width of the optical signal corresponding to the platelets of the second test sample in the forward-scattered light signal, including: the processor is configured to: Characteristic parameters representing platelet aggregation are obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the second test sample from the forward-scattered light signal; and The platelet count correction value of the blood sample to be tested is obtained based on the characteristic parameters characterizing platelet aggregation.

27. The blood cell analyzer according to claim 26, wherein, The processor is configured to obtain characteristic parameters characterizing platelet aggregation based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the second test sample from the forward-scattered light signal, including: the processor is configured to: A scatter plot of platelets in the second test sample is obtained based on the pulse width and pulse intensity of the optical signal corresponding to the platelets in the forward-scattered light. Platelet aggregation characteristic regions are obtained from the platelet scatter plot, and the scatter points in the platelet aggregation characteristic regions represent platelet clusters in the second test sample; and The characteristic parameters characterizing platelet aggregation are obtained based on the scatter distribution characteristics in the platelet aggregation feature region.

28. The blood cell analyzer according to any one of claims 23 to 27, wherein, The processor is configured to obtain a platelet count correction value for the blood sample to be tested based on the characteristic parameters characterizing platelet aggregation, including: The processor is configured to input the characteristic parameters representing platelet aggregation into a preset calculation model to calculate the platelet count correction value of the blood sample to be tested.

29. The blood cell analyzer according to any one of claims 22 to 28, wherein, The processor is configured to obtain a first platelet count value of the blood sample to be tested based on the second platelet count value and the platelet count correction value, including: Calculate the sum of the second platelet count value and the platelet count correction value, and output it as the first platelet count value.

30. The blood cell analyzer according to any one of claims 16 to 19, wherein, The processor is configured to obtain a first platelet count value of the blood sample to be tested based on at least a pulse width of the light signal corresponding to the platelets of the second blood sample to be tested in the at least one light signal and the platelet distribution information, comprising: The processor is configured to input the pulse width of the light signal corresponding to the platelets of the second blood sample to be tested in the at least one light signal into a machine learning model to obtain an output of the machine learning model as the first platelet count value of the blood sample to be tested.

Images