Patent Publication Number: US-2021164883-A1

Title: Cell analysis method, cell analysis device, and cell analysis system

Description:
RELATED APPLICATIONS 
     This application claims priority to Japanese Patent Application No. 2019-217162, filed on November 29, 2019, the entire content of which is incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to a cell analysis method, cell analysis device, cell analysis system, and cell analysis program. 
     2. Description of the Related Art 
     WIPO Patent Publication No. 2015/065697 discloses a method of applying a filtered microscope image to a trained machine learning model to determine centers and boundaries of cells of a specific type, count the determined cells, and output an image of the cells. 
     SUMMARY OF THE INVENTION 
     In an examination, a plurality of analysis items are often analyzed. However, as the number of analysis items increases, it becomes necessary to increase the number of trainings of the machine learning model and the types of parameters to be input to the machine learning model in order to reduce determination errors, and the training requires extensive time, and the machine learning model becomes large as necessary. 
     The present invention provides a cell analysis method, a cell analysis device, a cell analysis system, and a cell analysis program that facilitate analysis of a plurality of analysis items. 
     One embodiment of the present invention relates to a cell analysis method for analyzing cells. The cell analysis method generates data for analysis of cells ( 82 ,  87 ,  585 ) contained in a sample, selects an artificial intelligence algorithm ( 60 ,  63 ,  563 ) from a plurality of artificial intelligence algorithms ( 60 ,  63 ,  563 ), inputs the generated data for analysis ( 82 ,  87 ,  585 ) to the selected artificial intelligence algorithm ( 60 ,  63 ,  563 ), and generates, by the selected artificial intelligence algorithm ( 60 ,  63 ,  563 ), data indicating properties of the cells ( 84 ,  88 ,  582 ) based on the generated data for analysis ( 82 ,  87 ,  585 ). 
     One embodiment of the present invention relates to a cell analysis device ( 400 A,  200 T) for analyzing cells. The cell analysis device ( 400 A,  200 T) is provided with a control unit ( 40 A,  20 T) configured to select an artificial intelligence algorithm ( 60 ,  63 ,  560 ) from a plurality of artificial intelligence algorithms ( 60 ,  63 ,  560 ); input data for analysis of cells contained in a sample ( 82 ,  87 ,  585 ) to the selected artificial intelligence algorithm ( 60 ,  63 ,  560 ); and generate, by the selected artificial intelligence algorithm ( 60 ,  63 ,  560 ), data indicating properties of the cells ( 84 ,  88 ,  582 ) based on the data for analysis ( 82 ,  87 ,  585 ). 
     One embodiment of the present invention relates to a cell analysis system ( 1000 ). Cell analysis system ( 1000 ) includes a flow cell ( 110 ) having a flow path ( 111 ) through which a sample containing cells flows, a light source ( 120 ,  121 ,  122 ,  123 ) for irradiating light on the sample flowing through the flow path ( 111 ), an imaging unit ( 160 ) for imaging cells in the sample irradiated with the light; and a control unit ( 40 A), wherein the control unit ( 40 A) is configured to generate data for analysis of cells contained in the sample ( 82 ,  87 ) flowing through the flow path ( 111 ) and imaged by the imaging unit ( 160 ), select an artificial intelligence algorithm ( 60 ,  63 ) from a plurality of artificial intelligence algorithms ( 60 ,  63 ), input the generated data for analysis ( 82 ,  87 ) to the selected artificial intelligence algorithm ( 60 ,  63 ), and generate, by the selected artificial intelligence algorithm ( 60 ,  63 ), data indicating properties of the cells ( 80 ,  85 ) based on the data for analysis. 
     In one embodiment of the present invention, a cell analysis system ( 5000 ) includes a flow cell ( 4113 ) having a flow path ( 4113   a ) through which a sample containing cells flows, a signal acquiring unit ( 610 ) that acquires signals from cells in a sample flowing through the flow path, and a control unit ( 20 T), wherein the control unit ( 20 T) is configured to acquire signal strengths for individual cells passing through the flow path ( 4113   a ), generate analysis data ( 585 ) from the acquired signal strengths, select an artificial intelligence algorithm ( 560 ,  563 ) from a plurality of artificial intelligence algorithms ( 560 ,  563 ), and generate, by the selected artificial intelligence algorithm ( 560 ,  563 ), data indicating properties of the cells ( 582 ) based on the data for analysis. 
     One embodiment of the present invention relates to a cell analysis system ( 1000 ). The cell analysis system ( 1000 ) includes a microscope ( 700 ) with a stage for placing a slide smeared with a sample containing cells, an imaging unit ( 710   d ) that images the cells in the sample magnified by the microscope, and a control unit ( 40 A), wherein the control unit ( 40 A) is configured to generate data for analysis ( 82 ,  87 ) of cells contained in the sample smeared on the slide and imaged by the imaging unit, select an artificial intelligence algorithm ( 60 ,  63 ) from a plurality of artificial intelligence algorithms ( 60 ,  63 ), input the generated data for analysis to the selected artificial intelligence algorithm, and generate, by the selected artificial intelligence algorithm, data indicating properties of the cells ( 80 ,  85 ) based on the data for analysis. 
     One embodiment of the present invention relates to a cell analysis program for analyzing cells. The cell analysis program uses a computer execute processes including a step of selecting an artificial intelligence algorithm ( 60 ,  63 ,  560 ) as an input destination of the analysis data ( 82 ,  87 ,  5845 ) of the cells contained in the sample from among a plurality of artificial intelligence algorithms ( 60 ,  63 ,  560 ), and a step of generating data ( 84 ,  88 ,  582 ) indicating the properties of the cells based on the analysis data ( 82 ,  87 ,  585 ) via the selected artificial intelligence algorithm ( 60 ,  63 ,  560 ). 
     According to the present invention, analysis of a plurality of analysis items is facilitated in cell analysis. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. 
         FIG. 1  shows an outline of the present invention; 
         FIGS. 2A and 2B  show a method of generating training data for training a first artificial intelligence algorithm  50  for analyzing chromosomal abnormalities;  FIG. 2A  shows a method for generating positive training data;  FIG. 2B  shows a method for generating negative training data; 
         FIG. 3  shows a method of generating training data for training a first artificial intelligence algorithm  50  for analyzing chromosomal abnormalities; 
         FIG. 4  shows a method of generating analysis data for analyzing chromosomal abnormalities, and a method of analyzing cells by a trained first artificial intelligence algorithm  60 ; 
         FIGS. 5A and 5B  show a staining pattern of PML-RARA chimeric gene-positive cells by an imaging flow cytometer; The left side of  FIG. 5A  shows the image of channel 2, and the right side shows the image of channel 2;  FIG. 5B  is a cell different from  FIG. 5A , the left shows the image of channel 2, and the right shows the image of channel 2; 
         FIG. 6  shows an example of a fluorescent labeling pattern; 
         FIG. 7  shows an example of a fluorescent labeling pattern; 
         FIG. 8  shows an example of a fluorescent labeling pattern; 
         FIG. 9A (a),  FIG. 9A (b),  FIG. 9A (c),  FIG. 9A (d),  FIG. 9B (a),  FIG. 9B (b),  FIG. 9B (c), and  FIG. 9B (d) show a method of generating training data for training a second artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells; 
         FIGS. 10A and 10B  shows a method of generating training data for training a second artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells;  FIG. 10A  shows a method for generating positive training data;  FIG. 10B  shows a method for generating negative training data; 
         FIG. 11  shows a method of generating training data for training a second artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells; 
         FIG. 12  shows a method of generating analysis data for analyzing peripheral circulating tumor cells, and a method of analyzing cells by a trained second artificial intelligence algorithm  63 ; 
         FIG. 13  shows the hardware structure of the cell analysis system  1000 ; 
         FIG. 14  shows the hardware structure of the training device  200 A; 
         FIG. 15  shows a function block of the training device  200 A; 
         FIG. 16  shows a flowchart of a training process of the first artificial intelligence algorithm; 
         FIG. 17  shows a hardware structure of a cell imaging device  100 A and a cell analysis device  400 A; 
         FIG. 18  shows a function block of the cell analysis device  400 A; 
         FIG. 19  shows a flowchart of a cell analysis process; 
         FIG. 20  shows an example of an algorithm database storing an artificial intelligence algorithm according to the first embodiment; 
         FIG. 21  shows a hardware structure when the cell imaging device is a microscope; 
         FIG. 22  shows the structure of the optical system of a microscope; 
         FIG. 23  shows an example of a method of generating training data; 
         FIG. 24  shows an example of a label value; 
         FIG. 25  shows an example of a method of generating analysis data; 
         FIG. 26  shows an example of the appearance of the cell analysis system  5000 ; 
         FIG. 27  shows an example of a function structure of the measurement unit  600 ; 
         FIG. 28  shows a schematic example of the optical system of the nucleated cell detection unit  611  of the measurement unit  600 ; 
         FIG. 29  shows a schematic example of the sample preparation unit  640  of the measurement unit  600 ; 
         FIG. 30  shows a structural example of hardware of the training device; 
         FIG. 31  shows a structural example of hardware of a cell analysis device; 
         FIG. 32  shows an example of a function structure of a training device; 
         FIG. 33  shows an example of a function structure of a cell analysis device; 
         FIG. 34  shows a flowchart of a training process of the third and fourth artificial intelligence algorithms; 
         FIG. 35  shows a flowchart of a cell analysis process; 
         FIG. 36  shows a flowchart of a cell analysis process; and 
         FIG. 37  shows an example of an analysis mode reception screen. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     I. Summary of the Embodiment of the Invention 
     A summary of an embodiment of the present invention will be described with reference to  FIG. 1 . 
     An embodiment of the present invention relates to a cell analysis method for analyzing cells. As shown in  FIG. 1 , the cell analysis method characteristically generates analysis data  82 ,  87 , or  585  of the cells contained in the sample, and selects an artificial intelligence algorithm as an input destination of the generated analysis data from among a plurality of artificial intelligence algorithms  405 ( a ),  405 ( b ), T 205 ( a ) and T 205 ( b ) depending on the exam item. The selected artificial intelligence algorithm generates data indicating the properties of the cells to be analyzed from the input analysis data. As shown in  FIG. 1 , when analyzing cells, a flow cytometer or a microscope can be used to obtain an image of the cells and waveform data based on the signal intensity from the cells. The kind of data collected from cells is predetermined according to exam items or analysis items such as chromosomal aberration test, peripheral circulating tumor cell test, peripheral blood test, urinalysis. Therefore, in the present invention, an artificial intelligence algorithm suitable for each analysis is selected according to the exam item or the analysis item. 
     II. First Embodiment 
     The first embodiment relates to a method of analyzing a cell from an image of the cell using an artificial intelligence algorithm. 
     1. Summary of Cell Analysis Method 
     The present embodiment relates to a cell analysis method for analyzing cells using an artificial intelligence algorithm. In the cell analysis method, an analysis target image obtained by capturing an image of an analysis target cell is acquired by causing a sample containing cells to flow in a flow path and imaging the cells passing through the flow path. The analysis data to be input to the artificial intelligence algorithm are generated from the acquired analysis target image. When the analysis data are input to the artificial intelligence algorithm, the artificial intelligence algorithm generates data indicating the properties of the cells included in the analysis target image. The analysis target image is preferably an image of individual cells passing through the flow path. 
     In the present embodiment, the sample may be a sample prepared from a specimen collected from a subject. The sample may include, for example, blood samples such as peripheral blood, venous blood, arterial blood, urine samples, and body fluid samples other than blood and urine. Body fluids other than blood and urine may include bone marrow, ascites, pleural effusion, spinal fluid and the like. Body fluids other than blood and urine may be simply referred to as “body fluid”. The blood is preferably peripheral blood. For example, the blood may be peripheral blood collected by using an anticoagulant such as ethylenediaminetetraacetate sodium salt or potassium salt) and heparin sodium. 
     The sample can be prepared from the specimen according to a known method. For example, an examiner collects nucleated cells by subjecting a blood sample collected from a subject to centrifugation or the like using a cell separation medium such as Ficoll. In recovering the nucleated cells, the nucleated cells may be left by hemolyzing red blood cells and the like using a hemolytic agent instead of recovering the nucleated cells by centrifugation. The target site of the recovered nucleated cells is labeled with at least one selected from the Fluorescence In Situ Hybridization (FISH) method, immunostaining method, intracellular organelle staining method and the like described below, and preferably by performing fluorescent labeling; then the suspension liquid of the labeled cells is used as a sample supplied to, for example, in an imaging flow cytometer to image the analysis target cells. 
     The sample can include multiple cells. Although the number of cells contained in the sample is not particularly limited, the sample should contain at least 10 2  or more, preferably 10 3  or more, more preferably 10 4  or more, more preferably 10 5  or more, and still even more preferably 10 6  or more. Also, the plurality of cells may include different types of cells. 
     In the present embodiment, cells that can be analyzed are also referred to as analysis target cells. The analysis target cell may be a cell contained in a sample collected from a subject. Preferably, the cells may be nucleated cells. The cells can include normal cells and abnormal cells. 
     Normal cell means a cell that should be originally contained in the sample depending on the body portion where the sample is collected. Abnormal cell mean cells other than normal cells. Abnormal cells can include cells with chromosomal abnormalities and/or tumor cells. Here, the tumor cells are preferably peripheral circulating tumor cells. More preferably, the peripheral circulating tumor cells are not intended to be hematopoietic tumor cells in which tumor cells are present in the blood in a normal pathological state, rather tumor cells originating from a cell lineage other than a hematopoietic cell line are intended to be in circulation. In the present specification, tumor cells circulating peripherally are also referred to as circulating tumor cells (CTC). 
     When detecting a chromosomal abnormality, the target site is the nucleus of the cell to be analyzed. Examples of chromosomal abnormalities include chromosomal translocations, deletions, inversions, duplications, and the like. Examples of cells having such chromosomal abnormalities include myelodysplastic syndrome, acute myeloblastic leukemia, acute myeloblastic leukemia, acute promyelocytic leukemia, acute myelomonocytic leukemia, and acute monocytic leukemia, erythroleukemia, acute megakaryoblastic leukemia, acute myelogenous leukemia, acute lymphocytic leukemia, lymphoblastic leukemia, chronic myelogenous leukemia, chronic leukemia such as leukemia, Hodgkin lymphoma, non-Hodgkin lymphoma, malignant lymphoma and multiple myeloma. 
     Chromosomal abnormalities can be detected by known methods such as the FISH method. In general, test items for detecting chromosomal abnormalities are set according to the type of abnormal cells to be detected. The gene or locus to be analyzed is set as an analysis item depending on what kind of test item is to be performed on the sample. In the detection of chromosomal abnormalities by the FISH method, abnormal chromosome position or abnormal number can be detected by hybridizing a probe that specifically binds to the locus or gene present in the nucleus of the cell to be analyzed. The probe is labeled with a labeling substance. The labeling substance is preferably a fluorescent dye. Depending on the probe, when the labeling substance is a fluorescent dye, the labeling substance combines with fluorescent dyes having different fluorescence wavelength regions, and it is possible to detect multiple genes or loci in one cell. 
     The abnormal cell is a cell that appears when suffering from a predetermined disease, and may include, for example, a tumor cell such as a cancer cell or a leukemia cell. In the case of hematopoietic organs, the predetermined diseases can be selected from a group consisting of myeloid dysplasia syndrome, acute myeloid leukemia, acute myeloid leukemia, acute premyelocytic leukemia, acute myeloid monocytic leukemia, acute monocytic leukemia, leukemia such as red leukemia, acute meganuclear blast leukemia, acute myeloid leukemia, acute lymphocytic leukemia, lymphoblastic leukemia, chronic myeloid leukemia, or chronic lymphocytic leukemia, Hodgkin lymphoma, non-Hodgkin lymphoma, malignant lymphoma and multiple myeloid leukemia. In the case of organs other than hematopoietic organs, the predetermined diseases may be gastrointestinal malignant tumors originating from the rectum or anal region, upper pharynx, esophagus, stomach, duodenum, jejunum, ileum, cecum, worm, ascending colon, transverse colon, descending colon, S-shaped colon; liver cancer; cholangiocarcinoma; pancreatic cancer; pancreatic cancer; urinary malignancies originating from the bladder, ureter or kidney; female reproductive system malignancies originating from the ovaries, Fallopian tubes, uterus; breast cancer; pre-stage cancer; skin cancer; endocrine malignancies such as the hypothalamus, pituitary gland, thyroid gland, parathyroid gland, adrenal gland, and pancreas; central nervous system malignancies; and solid tumors such as a malignant tumor that develops from bone and soft tissue. 
     Abnormal cells can be detected using at least one selected from bright-field images, immunostaining images for various antigens, and organelle-stained images that specifically stain organelles. 
     A bright-field image can be obtained by irradiating a cell with light and imaging the transmitted light from the cell or the reflected light from the cell. Preferably, the bright-field image is an image obtained by capturing the phase difference of cells using transmitted light. 
     Immunostained images can be obtained by imaging immunostained cells by labeling with a labeling substance using an antibody capable of binding to an antigen present at at least one intracellular or cell target site selected from the nucleus, cytoplasm, and cell surface. As the labeling substance, it is preferable to use a fluorescent dye as in the FISH method. Depending on the antigen, when the labeling substance is a fluorescent dye, the labeling substance combines with fluorescent dyes having different fluorescence wavelength regions, and it is possible to detect multiple antigens in one cell. 
     Organelle-stained images can be obtained by imaging stained cells using dyes that can selectively bind to proteins, sugar chains, lipids, nucleic acids and the like present in at least one cell or cell membrane target site selected from the nucleus, cytoplasm, and cell membrane. Examples of nuclear-specific stains include Hoechst™ 33342, Hoechst™ 33258, 4′, 6-diamidino-2-phenylindole (DAPI), Propidium Iodide (PI), DNA-binding dyes such as ReadyProbes™ nuclear staining reagents, and Histone protein binding reagents such as Cell Light™ reagent. Examples of the nucleolus and RNA-specific staining reagent include SYTO™ RNA Select™, which specifically binds to RNA. Examples of the cytoskeleton-specific staining reagent include fluorescently labeled phalloidin. The CytoPainter series from Abcam plc (Cambridge, UK) can be used as dye to stain other organelles, such as lysosomes, endoplasmic reticulum, Golgi apparatus, mitochondria and the like. These staining dyes or staining reagents are fluorescent dyes or reagents containing fluorescent dyes, and different fluorescence wavelength regions can be selected depending on the wavelength range of the fluorescence of the organelles and the fluorescent dyes used as another stain applied jointly to one cell. 
     When detecting abnormal cells, inspection items are set according to what kind of abnormal cells are detected. The inspection items may include analysis items necessary for detecting abnormal cells. The analysis items may be set corresponding to the above-mentioned bright-field image, each antigen, and each organelle. Fluorescent dyes having different wavelength regions of fluorescence correspond to each analysis item except for the bright field, and different analysis items can be detected in one cell. 
     The analysis data to be input to the artificial intelligence algorithm is acquired by a method described later. The data indicating the properties of the cells included in the analysis target image generated by the artificial intelligence algorithm are, for example, data indicating whether the analysis target cells are normal or abnormal. More specifically, the data indicating the properties of the cells included in the analysis target image are data indicating whether the analysis target cell is a cell having a chromosomal abnormality or a peripheral circulating tumor cell. 
     For convenience of description in the present specification, “analysis target image” may be referred to as “analysis image”, “data to be analyzed” may be referred to as “analysis data”, “image for training” may be referred to as “training image”, and “data for training” may be referred to as “training data”. The “fluorescent image” is intended to be a training image obtained by imaging a fluorescent label or an analysis image obtained by imaging a fluorescent label. 
     2. Cell Analysis Method Using a First Artificial Intelligence Algorithm 
     The training method of the first artificial intelligence algorithm  50  and the second artificial intelligence algorithm  53 , and the cell analysis method using the trained first artificial intelligence algorithm  60  and the trained second artificial intelligence algorithm  63  will be described using  FIGS. 2A, 2B to 12 . The first and second artificial intelligence algorithms  60  and  63  can be deep learning algorithms having a neural network structure. The neural network structure can be selected from a fully connected deep neural network (FC-DNN), a convolutional neural network (CNN), an autoregressive neural network (RNN), and a combination thereof. A convolutional neural network is preferred. 
     As the artificial intelligence algorithm, for example, the artificial intelligence algorithm provided by Python can be used. 
     2-1. Artificial Intelligence Algorithm for Detecting Chromosomal Abnormality 
     This embodiment is related to a training method using a first artificial intelligence algorithm  50  for detecting a chromosomal abnormality, and a cell analysis method using the trained first artificial intelligence algorithm  60  for detecting a chromosomal abnormality. Here, the term “train” or “training” may be used in place of the term “generate” or “generating”. 
     (1) Training Data Generation 
     A training method of the first artificial intelligence algorithm  50  for detecting a chromosomal abnormality will be described with reference to  FIGS. 2A, 2B and 3 . In  FIGS. 2A and 2B , an example using an image of FISH staining of the PML-RARA chimeric gene formed by translocation of a transcriptional regulator lodged on the long arm of chromosome 15 (15q24.1), and the retinoic acid receptor α (RARA) gene located on the long arm of chromosome 17 (17q21.2). 
     As shown in  FIGS. 2A and 2B , labeled positive integrated training data  73 P and labeled negative integrated training data  73 N are generated from a positive training image  70 P obtained by imaging a cell positive for a chromosomal abnormality (hereinafter referred to as “first positive control cell”) and a negative training image  70 N obtained by imaging a cell negative for chromosomal abnormality (hereinafter referred to as “first negative control cell”), respectively. The positive training image  70 P and the negative training image  70 N may be collectively referred to as a training images  70 . Further, the labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N may be collectively referred to as training data  73 . 
     Here, the case of detecting the PML-RARA chimeric gene will be exemplified. The example shows a probe for detecting the PML locus is bound to a first fluorescent dye that fluoresces in the green wavelength region, and a probe for detecting the RARA locus is bound to a second fluorescent dye that fluoresces in the red wavelength region different from that of the first fluorescent dye. The nucleus of the first positive control cell and the nucleus of the first negative control cell can be labeled with the first fluorescent dye and the second fluorescent dye, respectively, by the FISH method using the probe bound with the first fluorescent dye and the probe bound with the second fluorescent dye. The label with the first fluorescent dye at the target site may be referred to as the first fluorescent label, and the label with the second fluorescent dye at the target site may be referred to as the second fluorescent label. 
     A sample containing cells having the first fluorescent label and the second fluorescent label can be subjected to analysis in a cell imaging device such as an imaging flow cytometer to capture an image of the cells. An image taken of a cell may include multiple images for the same field of view of the same cell. Since the first fluorescent label and the second fluorescent label have different fluorescence wavelength regions of the respective fluorescent dyes, a first filter for transmitting light emitted from the first fluorescent dye and a second filter for transmitting the light emitted from the second fluorescent dye differ. Therefore, the light transmitted through the first filter and the light transmitted through the second filter are taken into the imaging unit  160  described later via a corresponding first channel and a second channel, respectively, to capture as separate images of the same cell in the same field of view. That is, in the imaging unit  160 , a plurality of images corresponding to the number of labeling substances labeling the cell are acquired for the same field of view of the same cell. 
     Therefore, in the example of  FIGS. 2A and 2B , as shown in  FIG. 2A , the positive training image  70 P includes a first positive training image  70 PA in which a green first fluorescent label is imaged via a first channel and a second positive training image  70 PB in which a red second fluorescent label is imaged via a second channel for the first positive control cell. The first positive training image  70 PA and the second positive training image  70 PB are associated with each other as images of the same field of view of the same cell. The first positive training image  70 PA and the second positive training image  70 PB are then converted to the first positive numerical training data  71 PA and the second positive numerical training data  71 PB, which numerically indicate the brightness of the captured light at each pixel in the image. 
     A method of generating the first positive numerical training data  71 PA will be described using the first positive training image  70 PA. Each image captured by the imaging unit  160  is trimmed to, for example, 100 pixels in length×100 pixels in width to generate a training image  70 . At this time, trimming is performed so that the images acquired from each channel for one cell have the same field of view. The first positive training image  70 PA is represented, for example, as a 16-bit grayscale image. Therefore, in each pixel, the brightness of the pixel can be indicated by a numerical value of the brightness of 65,536 gradations from 1 to 65,536. As shown in  FIG. 2A , the value indicating the gradation of brightness in each pixel of the first positive training image  70 PA is the first positive numerical training data  71 PA, which expresses a matrix of numbers corresponding to each pixel. 
     Similar to the first positive numerical training data  71 PA, the second positive numerical training data  71 PB indicating the brightness of the imaged light at each pixel in the image can be generated from the second positive training image  70 PB. 
     Next, the first positive numerical training data  71 PA and the second positive numerical training data  71 PB are integrated for each pixel to generate positive integrated training data  72 P. As shown in  FIG. 2A , the positive integrated training data  72 P are matrix data in which the numerical value in each pixel of the first positive numerical training data  71 PA is shown side by side with the value in each pixel of the second positive numerical training data  71 PB. 
     Next, the positive integrated training data  72 P are labeled with a label value  74 P indicating that the positive integrated training data  72 P are derived from the first positive control cell, and the labeled positive integrated training data  73 P are generated. The numeral “2” is attached in  FIG. 2A  as a label indicating that it is the first positive control cell. 
     From the negative training image  70 N, the labeled negative integrated training data  73 N are generated in the same manner as in the case of generating the labeled positive integrated training data  73 P. 
     As shown in  FIG. 2B , the negative training image  70 N includes a first negative training image  70 NA obtained by imaging a green first fluorescent label through a first channel and a second negative training image  70 NB obtained by imaging a blue second fluorescent label through a second channel with regard to a first negative control cell. Imaging and trimming, and quantification of the brightness of light in each pixel are the same as in the case of acquiring the first positive numerical training data  71 PA from the first positive training image  70 PA. It is possible to generate the first negative numerical training data  71 NA which numerically indicates the brightness of the captured light in each pixel in the image from the first negative training image  70 N by the same method as the first positive numerical training data  71 PA. 
     Similarly, from the second negative training image  70 NB, it is possible to generate the second negative numerical training data  71 NB that numerically indicates the brightness of the captured light at each pixel in the image. 
     As shown in  FIG. 2B , the first negative numerical training data  71 NA and the second negative numerical training data  71 NB are integrated for each pixel according to the method of generating the positive integrated training data  72 P, and the negative integrated training data  72 N are generated. As shown in  FIG. 2B , the negative integrated training data  72 N become matrix data in which the numerical value in each pixel of the first negative numerical training data  71 NA is shown side by side with the value in each pixel of the second negative numerical training data  71 NB. 
     Next, the negative integrated training data  72 N is labeled with a label value  74 N indicating that the negative integrated training data  72 N is derived from the first negative control cell, and labeled negative integrated training data  73 N are generated. A “1” is attached in  FIG. 2B  as a label indicating that it is the first negative control cell. 
       FIG. 3  shows a method of inputting the labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N generated in the first artificial intelligence algorithm  50 . The number of nodes in the input layer  50   a  in the first artificial intelligence algorithm  50  having a neural network structure corresponds to the product of the number of pixels of the training image  70  (100×100=10,000 in the above example) and the number of channels for one cell (two channels of a green channel and a red channel in the above example). Data corresponding to the positive integrated training data  72 P of the labeled positive integrated training data  73 P are input to the input layer  50   a  of the neural network. A label value  74 P corresponding to the data input to the input layer  50   a  is input to the output layer  50   b  of the neural network. Further, data corresponding to the negative integrated training data  72 N of the labeled negative integrated training data  73 N are input to the input layer  50   a  of the neural network. A label value  74 N corresponding to the data input to the input layer  50   a  is input to the output layer  50   b  of the neural network. With these inputs, each weight in the intermediate layer  50   c  of the neural network is calculated, the first artificial intelligence algorithm  50  is trained, and the trained first artificial intelligence algorithm  60  is generated. 
     (2) Analysis Data Generation and Cell Analysis 
     The method of generating the integrated analysis data  72  and the cell analysis method using the trained first artificial intelligence algorithm  63  will be described from the analysis image  80  with reference to  FIG. 4 . The analysis image  80  can be imaged in the same manner as the method in which the training image  70  is imaged. 
     As shown in  FIG. 4 , the analysis image  80  includes a first analysis image  80 A in which a green first fluorescent label is imaged via a first channel and a second analysis image  80 B in which a red second fluorescent label is imaged via a second channel for the cells to be analyzed. Imaging and trimming, and quantification of the brightness of light in each pixel are the same as in the case of acquiring the first positive numerical training data  71 PA from the first positive training image  70 PA. Using the same method as the first positive numerical training data  71 PA, the first numerical analysis data  81 A which numerically indicate the brightness of the captured light at each pixel in the image can be generated from the first analysis image  80 A. 
     Similarly, from the second analysis image  80 B, it is possible to generate the second numerical analysis data  81 B which numerically indicates the brightness of the captured light in each pixel in the image. 
     As shown in  FIG. 4 , the first numerical analysis data  81 A and the second numerical analysis data  81 B are integrated for each pixel to generate the integrated analysis data  82  according to the method of generating the positive integrated training data  72 P. As shown in  FIG. 4 , the integrated analysis data  82  become matrix data in which the numerical value in each pixel of the first numerical analysis data  81 A is shown side by side with the value in each corresponding pixel of the second numerical analysis data  81 B. 
     As shown in  FIG. 4 , the generated integrated analysis data  82  are input to the input layer  60   a  of the neural network in the trained first artificial intelligence algorithm  60 . The value included in the input integrated analysis data  82  outputs a label value  84  indicating whether the analysis target cell has a chromosomal abnormality from the output layer  60   b  of the neural network via the intermediate layer  60   c  of the neural network. In the example shown in  FIG. 4 , when it is determined that the cell to be analyzed does not have a chromosomal abnormality, “1” is output as a label value, and when it is determined that the cell has a chromosomal abnormality, “2” is output as a label value. Instead of the label value, labels such as “none”, “yes”, “normal”, and “abnormal” also may be output. 
     (3) Other Configurations 
     i. In the present embodiment, the imaging flow cytometer uses an Extended Depth of Field (EDF) filter for expanding the depth of field when imaging cells, such that the cell image provided to the examiner restores the focal depth of the image after imaging. However, the training image  70  and the analysis image  80  used in the present embodiment are preferably images that have not been restored with respect to the images captured by using the EDF filter. An example of an image that has not been restored is shown in  FIGS. 5A and 5B .  FIGS. 5A and 5B  show cells positive for the PML-RARA chimeric gene. A and B are images of different cells. The images on the left side of  FIGS. 5A and 5B  show images of the first fluorescent label. The images on the right side of  FIGS. 5A and 5B  show images of the same cells as the cells on the left side, and the image of the second fluorescent label imaged in the same field of view as the image on the left side. 
     ii. Out-of-focus images can be excluded from the training image  70  and the analysis image  80  during imaging. Whether the image is in focus can be determined because if the difference in brightness between each pixel and the adjacent pixel does not include a portion where the gradient of the difference changes drastically in the entire image, it can be determined that the image is out of focus. 
     iii. The training image  70  and the analysis image  80  used in the present embodiment are typically trimmed so that the number of pixels is 100 pixels in the vertical direction and 100 pixels in the horizontal direction, but the size of the image is not limited to this. The number of pixels can be appropriately set between 50 to 500 pixels in the vertical direction and 50 to 500 pixels in the horizontal direction. The number of pixels in the vertical direction and the number of pixels in the horizontal direction of the image do not necessarily have to be the same. However, a training image  70  for training the first artificial intelligence algorithm  50  and an analysis image  80  for generating integrated analysis data  82  to be input in the first artificial intelligence algorithm  60  trained using the training image  70  have the same number of pixels and preferably the same number of pixels in the vertical direction and the horizontal direction. 
     iv. In this embodiment, the training image  70  and the analysis image  80  use a 16-bit grayscale image. However, the gradation of brightness may be 8 bits, 32 bits, or the like in addition to 16 bits. Although, the numerical value for brightness expressed in 16 bits (65, 536 gradations) is used directly in the present embodiment, these numerical values also may be subjected to a low-dimensional processing for summarizing them with gradations having a constant width, and these low-dimensional numerical values may be used as the numerical training data  71 PA,  71 PB,  71 NA,  71 NB. In this case, it is preferable to perform the same processing on the training image  70  and the analysis image  80 . 
     v. The chromosomal abnormalities that can be detected in this embodiment are not limited to the PML-RARA chimeric gene. For example, BCR/ABL fusion gene, AML1/ETO (MTG8) fusion gene (t (8; 21)), PML/RARα fusion gene (t (15; 17)), AML1 (21q22) translocation, MLL (11q23) translocation, TEL (12p13) translocation, TEL/AML1 fusion gene (t (12; 21)), IgH (14q32) translocation, CCND1 (BCL1)/IgH fusion gene (t (11; 14)), BCL2 (18q21) translocation, IgH/MAF fusion gene (t (14; 16)), IgH/BCL2 fusion gene (t (14; 18)), c-myc/IgH fusion gene (t (8; 14)), FGFR3/IgH fusion gene (t (4; 14)), BCL6 (3q27) translocation, c-myc (8q24) translocation, MALT1 (18q21) translocation, API2/MALT1 fusion gene (t (11; 18) translocation), TCF3/PBX1 fusion gene (t (1; 19) translocation), EWSR1 (22q12) translocation, PDGFRI3 (5q32) translocation, IGH-CCND1 gene [(IGH-BCL1) (t (11; 14) translocation) (chromosomal)], IGH-FGFR3 gene (t (4; 14) translocation), IgH-MAF gene (t (14; 16) translocation) and the like can be detected. 
     Also, translocations can include various variations.  FIGS. 6 and 7  show examples of fluorescent labeling of a typical positive pattern (major pattern) of the BCR/ABL fusion gene. In the state in which the first fluorescent label image and the second fluorescent label image are superimposed and the ES probe is used, negative cases have two first fluorescent labels and two second fluorescent labels, and the number of fusion fluorescent labeled images is zero. In the typical positive pattern using the ES probe, the number of the first fluorescent labels is 1, the number of the second fluorescent labels is 2, and the number of fusion fluorescent labels is 1. When the DF probe is used and the first fluorescent label image and the second fluorescent label image are superimposed, the negative pattern has two first fluorescent labels and two second fluorescent labels, and the number of fusion fluorescent labels is zero. In the typical positive pattern example using the DF probe, the number of the first fluorescent labels is 1, the number of the second fluorescent labels is 1, and the number of fusion fluorescent labels is 2. 
       FIG. 7  is an example of a fluorescent labeling of an atypical positive pattern of the BCR/ABL fusion gene. One example of the atypical positive pattern is the minor BCR/ABL pattern, in which the cut point of the BCR gene is relatively upstream of the BCR gene, so that the ES probe also detects three first fluorescent labels. Another example of the atypical positive pattern is the deletion of a portion of the binding region of the probe targeting the ABL gene on chromosome 9, and dependent on this, only one fusion fluorescent label is detected whereas two should be detected when the DF probe is used. In another example of the atypical positive pattern, a portion of the binding region of the probe targeting the ABL gene on chromosome 9 and a portion of the binding region of the probe targeting the BCR gene on chromosome 22 are both deleted. Dependent on this, only one fusion fluorescent label is detected whereas two should be detected when the DF probe is used. 
       FIG. 8  shows an example of a negative pattern and a reference pattern of a positive pattern when detecting a chromosomal abnormality related to the ALK locus is detected. In the negative pattern, the ALK gene is not cleaved, so there are two fusion fluorescent labels. On the other hand, in the positive pattern, since the ALK gene is cleaved, only one fusion fluorescent label is present (when only one of the alleles is cleaved), or the fusion fluorescent label is not recognized (both alleles). If is disconnected). The negative pattern and the positive pattern are the same for the ROS1 gene and the RET gene as well as the ALK gene. 
       FIG. 8  shows an example of a reference pattern of a chromosomal abnormality in which the long arm (5q) of chromosome 5 is deleted. For example, the first fluorescently labeled probe is designed to bind to the long arm of chromosome 5, and the second fluorescently labeled probe is designed to bind to the centromere of chromosome 5. In the negative pattern, the number of centromeres on chromosome 5 and the number of long arms on chromosome 5 are the same, so the first fluorescent label and the second fluorescent label reflect the number of homologous chromosomes, that is, two each. In the positive pattern, deletion of the long arm occurs on one or both of chromosome 5, and the number of the first fluorescent labels is only 1 or 0. This negative and positive pattern is the same for deletions of the short or long arms of other chromosomes. Examples of long-arm deletions of other chromosomes include long-arm deletions of chromosomes 7 and 20. Further examples showing similar positive and negative patterns include 7q31 (deletion), p16 (9p21 deletion analysis), IRF-1 (5q31) deletion, D20S108 (20q12) deletion, D13S319 (13q14) deletion, 4q12 deletion, ATM (11q22.3) deletion, p53 (17p13.1) deletion and the like. 
       FIG. 8  also shows an example of chromosome 8 trisomy. The first fluorescently labeled probe binds, for example, to the centromere on chromosome 8. The positive pattern has three first fluorescent labels. The negative pattern has two first fluorescent labels. Such a fluorescent labeling pattern is the same in trisomy 12 of chromosome. In the chromosome 7 monosomy, for example, when a first fluorescently labeled probe that binds to the centromere of chromosome 7 is used, the positive pattern is one first fluorescent label. The negative pattern has two first fluorescent labels. 
     2-2. Artificial Intelligence Algorithm for Detecting Peripheral Circulating Tumor Cells 
     The present embodiment relates to a method for training a second artificial intelligence algorithm  53  for detecting peripheral circulating tumor cells and a method for analyzing cells using the second artificial intelligence algorithm  63  for detecting peripheral circulating tumor cells. Here, the term “train” or “training” may be used in place of the term “generate” or “generating”. 
     (1) Training Data Generation 
     The training method of the second artificial intelligence algorithm  53  for detecting peripheral circulating tumor cells will be described with reference to  FIG. 9A (a),  FIG. 9A (b),  FIG. 9A (c),  FIG. 9A (d),  FIG. 9B (a),  FIG. 9B (b),  FIG. 9B (c), and  FIG. 9B (d) to  11 . 
       FIG. 9A (a),  FIG. 9A (b),  FIG. 9A (c),  FIG. 9A (d),  FIG. 9B (a),  FIG. 9B (b),  FIG. 9B (c), and  FIG. 9B (d) show a preprocessing method for an image captured by the imaging unit  160 .  FIG. 9A (a),  FIG. 9A (b),  FIG. 9A (c), and  FIG. 9A (d) show a captured image before pretreatment. The preprocessing is a trimming process for making the training image  75  and the analysis image  85  the same size, and can be performed on all the images used as the training image  75  or the analysis image  85 .  FIG. 9A (a) and  FIG. 9A (b) are images of the same cell, but the channels at the time of imaging are different.  FIG. 9A (c) and  FIG. 9A (a) are images of different cells. Although  FIG. 9A (c) and  FIG. 9A (d) are images of the same cell, the channels when imaging are different. As shown in  FIG. 9A (a) and  FIG. 9A (c), the size of the image when the cells are imaged may be different. In addition, the size of the cell itself also may differ depending on the cell. Therefore, it is preferable to trim the acquired image so as to reflect the size of the cells and have the same image size. In the example shown in  FIG. 9A (a),  FIG. 9A (b),  FIG. 9A (c),  FIG. 9A (d),  FIG. 9B (a),  FIG. 9B (b),  FIG. 9B (c), and  FIG. 9B (d), a position separated by  16  pixels in the vertical direction and the horizontal direction from the center is set as a trimming position with the center of gravity of the nucleus of the cell in the image as the center. The image cut out for trimming is shown in  FIG. 9B (a),  FIG. 9B (b),  FIG. 9B (c), and  FIG. 9B (d).  FIG. 9B (a) is an image extracted from  FIG. 9A (a),  FIG. 9B  (b) is an image extracted from  FIG. 9A (b),  FIG. 9B (c) is an image extracted from  FIG. 9A (c), and  FIG. 9B  (d) is an image extracted from  FIG. 9A (d). Each image in  FIG. 9B (a),  FIG. 9B (b),  FIG. 9B (c), and  FIG. 9B (d) has a length of 32 pixels and a width of 32 pixels. The center of gravity of the nucleus can be determined, for example, by using the analysis software (IDEAS) attached to an imaging flow cytometer (ImageStream MarkII, Luminex). 
       FIGS. 10A, 10B and 11  show a training method for the second artificial intelligence algorithm  53 . 
     As shown in  FIGS. 10A and 10B , positive integrated training data  78 P and the negative integrated training data  78 N are generated from a positive training image  75 P obtained by imaging peripheral circulating tumor cells (hereinafter, referred to as “second positive control cell”) and negative training image  75 N obtained from cells other than peripheral circulating tumor (hereinafter, “second negative control cell”). The positive training image  75 P and the negative training image  75 N may be collectively referred to as a training image  75 . The positive integrated training data  78 P and the negative integrated training data  78 N also may be collectively referred to as training data  78 . 
     When detecting peripheral circulating tumor cells, the image captured by the imaging unit  160  may include a bright field image and a fluorescence image. The brightfield image can be an image of the phase difference of the cells. This image can be captured, for example, on a first channel. The fluorescent image is an image of a fluorescent label labeled at a target site in the cell by immunostaining or intracellular organelle staining. Fluorescent labeling is performed with fluorescent dyes that have different fluorescence wavelength regions for each antigen and/or each organelle. 
     For example, when the first fluorescent dye that emits fluorescence in the first green wavelength region is bound to the first antigen, the first antigen can be labeled with the first fluorescent dye by binding the first fluorescent dye to an antibody that directly or indirectly binds to the first antigen. 
     When a second fluorescent dye that emits fluorescence in a red wavelength region different from that of the first fluorescent dye is bound to an antibody that binds to the second antigen, the second antigen can be labeled with the second fluorescent dye by binding the second fluorescent dye to an antibody that directly or indirectly binds to the second antigen. 
     When the antibody that binds to the third antigen is bound to the first fluorescent dye and the third fluorescent dye that emits fluorescence in a yellow wavelength region different from that of the second fluorescent dye, the third antigen can be labeled with a third fluorescent dye by binding the third fluorescent dye to an antibody that directly or indirectly binds to the third antigen. 
     In this way, fluorescent dyes with different fluorescence wavelength regions can be labeled from the first fluorescence label to the Xth fluorescence label. 
     A sample containing cells having the first fluorescent label to the Xth fluorescent label can be subjected to imaging with a cell imaging device such as an imaging flow cytometer, and an image of the cells can be obtained. An image taken of a cell may include multiple images for the same field of view of the same cell. Since the first fluorescent label to the Xth fluorescent label have different fluorescence wavelength regions of each fluorescent dye, the filter for transmitting the light emitted from each fluorescent dye is different for each fluorescent dye. The bright field image requires the use of a filter different from the filter that transmits light from the fluorescent dye. Therefore, the light transmitted through each filter is taken into the imaging unit  160  (described later) via each corresponding channel, and is captured as another image of the same cell in the same field of view. That is, in the imaging unit  160 , for the same visual field of the same cell, a plurality of images corresponding to the number obtained by adding the number of bright-field images to the number of labeling substances labeling the cells are acquired. 
     In the example shown in  FIGS. 10A and 10B , the first channel (Ch1) indicates a bright-field image in  FIGS. 10A and 10B . In  FIGS. 10A and 10B , the second channel (Ch2), the third channel (Ch3), . . . the Xth channel (ChX) refer to each channel in which a plurality of different labeling substances are imaged. 
     In the example of  FIGS. 10A and 10B , as shown in  FIG. 10A , the positive training image  75 P includes the first positive training image  75 P 1  imaged through the first channel of the second positive control cell, a second positive training image  75 P 2  in which the first fluorescent label is imaged via the second channel, a third positive training image  75 P 3  in which the second fluorescent label is imaged via the third channel, and the like on up to an Xth positive training image  75 Px in which each fluorescent label is imaged on up to the Xth channel. Images from the first positive training image  75 P 1  to the Xth positive training image  75 Px are associated as images of the same visual field of the same cell. Images from the first positive training image  75 P 1  to the Xth positive training image  75 P are converted to the first positive numerical training data  76 P 1  to the Xth positive training data  76 Px which numerically indicate the brightness of the imaged light in each pixel in the image. 
     A method of generating the first positive numerical training data  76 P 1  will be described with reference to the first positive training image  75 P 1 . Each image captured by the imaging unit  160  is trimmed, for example, to 32 pixels in length×32 pixels in width by the above-mentioned preprocessing to obtain a training image  75 . The first positive training image  75 P 1  is represented, for example, as a 16-bit grayscale image. Therefore, in each pixel, the brightness of the pixel can be indicated by a numerical value of the brightness of 65,536 gradations from 1 to 65,536. As shown in  FIG. 10A , the values indicating the gradation of brightness in each pixel of the first positive training image  75 P 1  are the first positive numerical training data  76 P 1 , which is a matrix of numbers corresponding to each pixel. 
     Similar to the first positive numerical training data  76 P 1 , the Xth positive numerical training data  76 Px can be generated from the second positive numerical training data  76 P 2  which numerically indicate the brightness of the imaged light for each pixel in the image from the second positive training image  75 P 2  to the Xth positive training image  75 Px. 
     Next, the first positive numerical training data  76 P 1  to the Xth positive numerical training data  76 Px are integrated for each pixel to generate positive integrated training data  77 P. As shown in  FIG. 10A , in the positive integrated training data  77 P become matrix data in which the numerical values in each pixel of the first positive numerical training data  76 PA are shown side by side with the values of each pixel corresponding to the second positive numerical training data  76 P 2  to the X positive numerical training data  76 Px. 
     Next, the positive integrated training data  77 P is labeled with a label value  79 P indicating that the positive integrated training data  77 P is derived from the second positive control cell, then labeled positive integrated training data  78 P are generated. The numeral “2” is attached in  FIG. 10A  as a label indicating that it is the first positive control cell. 
     From the negative training image  75 N, the labeled negative integrated training data  78 N are generated in the same manner as in the case of generating the labeled positive integrated training data  78 P. 
     As shown in  FIG. 10B , the negative training image  75 N includes from the first negative training image  75 N 1  to the Xth negative training image  75 Nx obtained from the first channel via the Xth image for the second negative control cell, similarly to the positive training image  75 P. The quantification of the brightness of light in each pixel is identical to the case when the first positive numerical training data  76 P 1  to Xth positive numerical training data  76 Px are acquired from the first positive training image  75 PA to the Xth positive training image  75 P. First negative numerical training data  76 N 1  indicating the brightness of the imaged light numerically can be generated for each pixel in the image from the first negative training image  75 N 1  by the same method as the first positive numerical training data  76 P 1 . 
     Similarly, from the second negative numerical training data  76 N 2  to the Xth negative numerical training data  76 Nx indicating the brightness of the imaged light numerically can be generated for each pixel in the image from the second negative training image  75 N 2  to the Xth second training image  75 Nx. 
     As shown in  FIG. 10B , the first negative numerical training data  76 N 1  to the Xth negative numerical training data  76 Nx are integrated for each pixel according to the method of generating the positive integrated training data  77 P to generate the negative integrated training data  77 N. As shown in  FIG. 10B , in the negative integrated training data  77 N become matrix data in which the numerical values in each pixel the first negative numerical training data  76 N 1  are shown side by side with the values of each pixel corresponding to the second negative numerical training data  76 N 2  to the Xth negative numerical training data  76 Nx. 
     Next, the negative integrated training data  77 N is labeled with a label value  79 N indicating that the negative integrated training data  77 N is derived from the second negative control cell, and labeled negative integrated training data  78 N are generated. A “1” is attached in  FIG. 10B  as a label indicating that it is the second negative control cell. 
       FIG. 11  shows a method of inputting the labeled positive integrated training data  78 P generated in the second artificial intelligence algorithm  53  and the labeled negative integrated training data  78 N. The number of nodes of the input layer  53   a  in the first artificial intelligence algorithm  53  having a neural network structure corresponds to the sum of the number of pixels of the training image  75  (32×32=1024 in the above example) and the number of channels for one cell (X channels from 1 to X in the above example). Data equivalent to the positive integrated training data  77 P of the labeled positive integrated training data  78 P are input to the input layer  53   a  of the neural network. A label value  79 P corresponding to the data input to the input layer  53   a  is input to the output layer  53   b  of the neural network. Data corresponding to the negative integrated training data  77 N of the labeled negative integrated training data  78 N are input to the input layer  53   a  of the neural network. A label value  79 N corresponding to the data input to the input layer  53   a  is input to the output layer  53   b  of the neural network. With these inputs, each weight in the intermediate layer  53   c  of the neural network is calculated, the second artificial intelligence algorithm  53  is trained, and the trained second artificial intelligence algorithm  63  is generated. 
     (2) Analysis Data Generation 
     The method of generating the integrated analysis data  72  and the cell analysis method using the trained first artificial intelligence algorithm  63  will be described from the analysis image  85  with reference to  FIG. 12 . The analysis image  85  can be captured and reprocessed in the same manner as the training image  75  was captured. 
     As shown in  FIG. 12 , the analysis image  85  includes a first analysis image  85 T 1 , that is, a bright-field image of the cells to be analyzed taken through the first channel, and the Xth analysis image  85 Tx obtained from the second analysis image  85 T 2  of the imaged Xth fluorescence label taken through the Xth channel from the second channel. Imaging and preprocessing, and quantification of the brightness of light in each pixel are the same as in the case of acquiring the first positive numerical training data  76 P 1  from the first positive training image  75 P 1 . Using the same method as the first positive numerical training data  76 P 1 , the first numerical analysis data  86 T 1  which numerically indicates the brightness of the captured light at each pixel in the image is generated from the first analysis image  85 T 1 . 
     Similarly, from the second analysis image  85 T 2  to the Xth analysis image  85 Tx, the Xth numerical analysis data  86 Tx can be generated from the second numerical analysis data  86 T 2  numerically indicating the brightness of the captured light in each pixel in the image. 
     As shown in  FIG. 12 , the first numerical analysis data  86 T 1  to the Xth numerical analysis data  86 Tx are integrated for each pixel to generate the integrated analysis data  87  according to the method of generating the positive integrated training data  77 P. As shown in  FIG. 12 , the integrated analysis data  87  become matrix data in which the numerical value in each pixel of the first numerical analysis data  86 T 1  are shown side by side with the value in each pixel corresponding to the second numerical analysis data  86 T 2  to the Xth numerical analysis data  86 Tx. 
     As shown in  FIG. 12 , the generated integrated analysis data  87  are input to the input layer  63   a  of the neural network in the trained second artificial intelligence algorithm  63 . The value included in the input integrated analysis data  87  outputs a label value  89  indicating whether the analysis target cell is a peripheral circulating tumor cell from the output layer  63   b  of the neural network via the intermediate layer  63   c  of the neural network. In the example shown in  FIG. 12 , “1” is output as a label value when it is determined that the cell to be analyzed is not a peripheral circulating tumor cell, and “2” is output as a label value when it is determined to be a peripheral circulating tumor cell. Instead of the label value, labels such as “none”, “yes”, “normal”, and “abnormal” also may be output. 
     (3) Other Configurations 
     i. The training image  75  and the analysis image  85  used in the present embodiment are preferably images that have not been restored with respect to the images captured by using the EDF filter. 
     ii. Out-of-focus images can be excluded from the training image  75  and the analysis image  85  during imaging. 
     iii. Although the training image  75  and the analysis image  85  used in the present embodiment are typically trimmed so that the number of pixels is 32 pixels in the vertical direction and 32 pixels in the horizontal direction, the size of the image is not limited insofar as the entire cell is contained in the image. The number of pixels can be appropriately set between 30 to 50 pixels in the vertical direction and 30 to 50 pixels in the horizontal direction. The number of pixels in the vertical direction and the number of pixels in the horizontal direction of the image do not necessarily have to be the same. However, a training image  75  for training the first artificial intelligence algorithm  53  and an analysis image  85  for generating integrated analysis data  87  to be input to the first artificial intelligence algorithm  63  trained using the training image  75  preferably have the same number of pixels in the vertical direction and the horizontal direction. 
     iv. In this embodiment, the training image  70  and the analysis image  80  use a 16-bit grayscale image. However, the gradation of brightness may be 8 bits, 32 bits, or the like in addition to 16 bits. Although, for each numerical training data  76 P 1  to numerical training data  76 Px and numerical training data  76 N 1  to numerical training data  76 Nx, the numerical values of the brightness represented by 16 bits (65,536 gradations) are used directly in the present embodiment, these numerical values are subjected to a low-dimensional processing that summarizes them with a gradation of a certain width, and the numerical values after the low dimensional processing also may be used as the numerical training data  76 Px from each numerical training data  76 P 1  and the numerical training data  76 Nx from the numerical training data  76 N 1 . In this case, it is preferable to perform the same processing on the training image  70  and the analysis image  80 . 
     3. Cell Analysis System 
     Hereinafter, the cell analysis systems  1000 ,  2000 , and  3000  according to the first to third embodiments will be described with reference to  FIGS. 13 to 21 . 
     3-1. First Embodiment of a Cell Analysis System 
       FIG. 13  shows the hardware structure of the cell analysis system  1000  according to the first embodiment. The cell analysis system  1000  may include a training device  200 A for training the artificial intelligence algorithm, a cell imaging device  100 A, and a cell analysis device  400 A. The cell imaging device  100 A and the cell analysis device  400 A are communicably connected. The training device  200 A and the cell analysis device  400 A also can be connected by a wired or wireless network. 
     3-1-1. Training Device 
     (1) Hardware Structure 
     The hardware structure of the training device  200 A will be described with reference to  FIG. 14 . The training device  200 A includes a control unit  20 A, an input unit  26 , and an output unit  27 . The training device  200 A can be connected to the network  99 . 
     The control unit  20 A includes a CPU (Central Processing Unit)  21  that performs data processing described later, a memory  22  used as a work area for data processing, a storage unit  23  that records a program and processing data described later, a bus  24  for transmitting data among each of the units, an interface (I/F) unit  25  for inputting/outputting data to/from an external device, and a GPU (Graphics Processing Unit)  29 . The input unit  26  and the output unit  27  are connected to the control unit  20 A via the I/F unit  25 . Illustratively, the input unit  26  is an input device such as a keyboard or a mouse, and the output unit  27  is a display device such as a liquid crystal display. The GPU  29  functions as an accelerator for assisting arithmetic processing (for example, parallel arithmetic processing) performed by the CPU  21 . In the following description, the processing performed by the CPU  21  means that the processing performed by the CPU  21  using the GPU  29  as an accelerator is also included. Here, instead of GPU  29 , a chip which is preferable for the calculation of the neural network may be provided. Examples of such a chip include FPGA (Field-Programmable Gate Array), ASIC (Application Specific Integrated Circuit), Myriad X (Intel), and the like. 
     The control unit  20 A sends a training program for training the artificial intelligence algorithm and an artificial intelligence algorithm in advance and in an executable format to the storage unit  23 , for example, in order to perform the processing of each step described with reference to  FIG. 16 . The execution form is, for example, a form generated by being converted from a programming language by a compiler. The control unit  20 A makes the operating system and the training program recorded in the storage unit  23  cooperate with each other to perform training processing of the artificial intelligence algorithm prior to the training. 
     In the following description, unless otherwise specified, the processing performed by the control unit  20 A means the processing performed by the CPU  21  or the CPU  21  and the GPU  29  based on the program and the artificial intelligence algorithm stored in the storage unit  23  or the memory  22 . The CPU  21  temporarily stores necessary data (intermediate data during processing and the like) using the memory  22  as a work area, and appropriately records data to be stored for a long period of time, such as a calculation result, in the storage unit  23 . 
     (2) Function Structure of Training Device 
       FIG. 17  shows the function structure of the training device  200 A. The training device  200 A includes a training data generation unit  201 , a training data input unit  202 , an algorithm update unit  203 , a training data database (DB)  204 , and algorithm databases (DB)  205 ( a ) and  205 ( b ). Step S 11  shown in  FIG. 26  corresponds to the analysis data generation unit  201 . Step S 112  shown in step S 12  in  FIG. 16  corresponds to the training data input unit  202 . Step S 14  shown in  FIG. 16  corresponds to the algorithm update unit  203 . 
     The training images  70 PA,  70 PB,  70 NA,  70 NB,  75 P 1  to  75 Px,  75 N 1  to  75 Nx are acquired in advance from the cell imaging device  100 A by the cell analysis device  400 A, and are stored in advance in the storage unit  23  or the memory  22  of the control unit  20 A of the training device  200 A. The training device  200 A also may acquire the training images  70 PA,  70 PB,  70 NA,  70 NB,  75 P 1  to  75 Px and  75 N 1 , to  75 Nx from the cell analysis device  400 A via the network, or may be acquired via the media drive D 98 . The training data database (DB)  204  stores the generated training data  73  and  78 . The artificial intelligence algorithm prior to training is stored in advance in the algorithm databases  205 ( a ) and  205 ( b ). The trained first artificial intelligence algorithm  60  is recorded in the algorithm database  205 ( a ) in association with the exam items and analysis items for inspecting chromosomal abnormalities. The trained second artificial intelligence algorithm  63  is recorded in the algorithm database  205 ( b ) in association with the exam items and analysis items for testing peripheral circulating tumor cells. 
     (3) Training Process 
     The control unit  20 A of the training device  200 A performs the training process shown in  FIG. 16 . 
     First, in response to a request from the user to start processing, the CPU  21  of the control unit  20 A acquires the training images  70 PA,  70 PB,  70 NA,  70 NB and the training images  75 P 1  to  75 Px and  75 N 1  to  75 Nx stored in the storage unit  23  or the memory  22 . The training images  70 PA,  70 PB,  70 NA,  70 NB can be used to train the first artificial intelligence algorithm  50 , and the training images  75 P 1  to  75 Px, and  75 N 1  to  75 Nx can be used to train the second artificial intelligence algorithm  53 , respectively. 
     i. First Artificial Intelligence Algorithm  50  Training Process 
     In step S 11  of  FIG. 16 , the control unit  20 A generates positive integrated training data  72 P from the positive training images  70 PA and  70 PB, and generates negative integrated training data  72 N from the negative training images  70 NA and  70 NB. The control unit  20 A assigns a label value  74 P or a label value  74 N corresponding to each of the positive integrated training data  72 P and the negative integrated training data  72 N, and generates a labeled positive integrated training data  73 P or a labeled negative integrated training data  73 N. The labeled positive integrated training data  73 P or the labeled negative integrated training data  73 N are recorded in the storage unit  23  as training data  73 . The method for generating the labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N is described in section 2-1 above. 
     Next, the control unit  20 A inputs the generated labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N into the first artificial intelligence algorithm  50  in step S 12  of  FIG. 16 , and trains the first artificial intelligence algorithm  50 . The training result of the first artificial intelligence algorithm  50  is aggregated each time the training is performed using the plurality of labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N. 
     Subsequently, in step S 13  of  FIG. 16 , the control unit  20 A determines whether the training results for a predetermined number of trials have been accumulated. When the training results are accumulated for a predetermined number of trials (when “YES”), the control unit  20 A proceeds to the process of step S 14 , and when the training results are not accumulated for a predetermined number of trials (“NO”), the control unit  20 A proceeds to the process of step S 15 . 
     When the training results are accumulated for a predetermined number of trials, in step S 14 , the control unit  20 A updates the weighting (w) (coupling weight) of the first artificial intelligence algorithm  50  using the training results accumulated in step S 12 . 
     Next, in step S 15 , the control unit  20 A determines whether the first artificial intelligence algorithm  50  has been trained with a predetermined number of labeled positive integrated training data  73 P and labeled negative integrated training data  73 N. When the training is performed with the specified number of labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N (in the case of “YES”), the training process is terminated. The control unit  20 A stores the trained first artificial intelligence algorithm  60  in the storage unit  23 . 
     When the first artificial intelligence algorithm  50  is not trained with the specified number of labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N (in the case of “NO”), the control unit  20 A advances from step S 15  to step S 16  and the processes from step S 11  to step S 15  are performed on the next positive training images  70 PA and  70 PB and the negative training images  70 NA and  70 NB. 
     ii. Second Artificial Intelligence Algorithm  53  Training Process 
     In step S 11  of  FIG. 16 , the control unit  20 A generates positive integrated training data  77 P from positive training images  75 P 1  to  75 Px, and generates negative integrated training data  77 N from negative training images  75 N 1  to  75 Nx. The control unit  20 A assigns a label value  79 P or a label value  79 N corresponding to each of the positive integrated training data  77 P and the negative integrated training data  77 N, and generates labeled positive integrated training data  78 P or labeled negative integrated training data  78 N. The labeled positive integrated training data  78 P or the labeled negative integrated training data  78 N are recorded in the storage unit  23  as training data  78 . The method for generating the labeled positive integrated training data  78 P and the labeled negative integrated training data  78 N is as described in 2-2 above. 
     Next, the control unit  20 A inputs the generated labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N into the second artificial intelligence algorithm  53  in step S 12  of  FIG. 16 , and trains the second artificial intelligence algorithm  53 . The training result of the second artificial intelligence algorithm  53  is accumulated every time the training is performed using the plurality of labeled positive integrated training data  78 P and the labeled negative integrated training data  78 N. 
     Subsequently, in step S 13  of  FIG. 16 , the control unit  20 A determines whether the training results for a predetermined number of trials have been accumulated. When the training results are accumulated for a predetermined number of trials (when “YES”), the control unit  20 A proceeds to the process of step S 14 , and when the training results are not accumulated for a predetermined number of trials (“NO”), the control unit  20 A proceeds to the process of step S 15 . 
     When the training results are accumulated for a predetermined number of trials, in step S 14 , the control unit  20 A uses the training results accumulated in step S 12  to update the weight w (coupling weight) of the second artificial intelligence algorithm  53 . 
     Next, in step S 15 , the control unit  20 A determines whether the second artificial intelligence algorithm  53  has been trained with a predetermined number of labeled positive integrated training data  78 P and labeled negative integrated training data  78 N. When training is performed with the specified number of labeled positive integrated training data  78 P and labeled negative integrated training data  78 N (in the case of “YES”), the training process is completed. The control unit  20 A stores the trained second artificial intelligence algorithm  63  in the storage unit  23 . 
     When the second artificial intelligence algorithm  53  is not trained with the specified number of labeled positive integrated training data  78 P and the labeled negative integrated training data  78 N (in the case of “NO”), the control unit  20 A advances from step S 15  to step S 16 , and the processes from step S 11  to step S 15  are performed on the next positive training images  75 P 1  to  75 Px and the negative training images  75 N 1  to  75 Nx. 
     (4) Training Program 
     The present embodiment includes a computer program for training an artificial intelligence algorithm that causes a computer to execute the processes of steps S 11  to S 16 . 
     An implementation of the present embodiment relates to a program product such as a storage medium that stores the computer program. That is, the computer program can be stored on a hard disk, a semiconductor memory element such as a flash memory, or a storage medium such as an optical disk. The recording format of the program on the storage medium is not limited insofar as the training device  200 A can read the program. Recording on the storage medium is preferably non-volatile. 
     Here, the “program” is a concept including not only a program that can be directly executed by the CPU, but also a source format program, a compressed program, an encrypted program, and the like. 
     3-1-2. Cell Imaging Device 
       FIG. 17  shows the structure of the cell imaging device  100 A that captures the training images  70 ,  75  and/or the analysis images  80  and  85 . The cell imaging device  100 A shown in  FIG. 17  is exemplified by an imaging flow cytometer. The operation of the cell imaging device  100 A as an imaging device is controlled by the cell analysis device  400 A. 
     As described above, chromosomal abnormalities or peripheral circulating tumor cells use one or more fluorescent dyes to detect the target site. Preferably, the FISH method uses two or more fluorescent dyes to detect a target site on the first chromosome and a target site on the second chromosome (the “first” and “second” that modify the “chromosome” is a comprehensive concept of numbers that do not mean chromosome numbers). For example, a probe that hybridizes with the PML locus is labeled by a first fluorescent dye in which a nucleic acid having a sequence complementary to the base sequence of the PML locus is irradiated with light of wavelength λ11 to generate first fluorescence of wavelength λ21. With this probe, the PML locus is labeled with the first fluorescent dye. In the probe that hybridizes with the RARA locus, a nucleic acid having a sequence complementary to the base sequence of the RARA locus is labeled with a second fluorescent dye that produces a second fluorescence of a wavelength λ22 when irradiated with light of a wavelength λ12. Using this probe, the RARA locus is labeled with a second fluorescent dye. The nucleus is stained with a dye for nuclear staining that produces a third fluorescence of wavelength λ23 when irradiated with light of wavelength λ13. The wavelength λ11, the wavelength λ12, and the wavelength λ13 are so-called excitation lights. The wavelength λ114 is light emitted from a halogen lamp or the like for bright field observation. 
     The cell imaging device  100 A includes a flow cell  110 , a light source  120  to  123 , a condenser lens  130  to  133 , a dichroic mirror  140  to  141 , a condenser lens  150 , an optical unit  151 , a condenser lens  152 , and an imaging unit  160 . The sample  10  is flowed through the flow path  111  of the flow cell  110 . 
     The light sources  120  to  123  irradiate light on the sample  10  flowing from the bottom to the top of the flow cell  110 . The light sources  120  to  123  are configured of, for example, a semiconductor laser light source. Lights having wavelengths λ11 to λ14 are emitted from the light sources  120  to  123 , respectively. 
     The condenser lenses  130  to  133  collect light having wavelengths λ11 to λ14 emitted from light sources  120  to  123 , respectively. The dichroic mirror  140  transmits light having a wavelength of λ11 and refracts light having a wavelength of λ12. The dichroic mirror  141  transmits light having wavelengths λ11 and λ12 and refracts light having wavelength λ13. In this way, light having wavelengths λ11 to λ14 is applied to the sample  10  flowing through the flow path  111  of the flow cell  110 . The number of semiconductor laser light sources included in the cell imaging device  100 A is not limited insofar as it is 1 or more. The number of semiconductor laser light sources can be selected from, for example, 1, 2, 3, 4, 5 or 6. 
     When the sample  10  flowing through the flow cell  110  is irradiated with light having wavelengths λ11 to λ13, fluorescence is generated from the fluorescent dye labeled on the cells flowing through the flow path  111 . Specifically, when the light of the wavelength λ11 is irradiated on the first fluorescent dye that labels the PML locus, a first fluorescence of the wavelength λ21 is generated from the first fluorescent dye. When light of wavelength λ12 is irradiated on the second fluorescent dye that labels the RARA locus, the second fluorescent dye produces a second fluorescence of wavelength λ22. When light of wavelength λ13 is irradiated on the dye for nuclear dyeing that stains the nucleus, the dye for nuclear dyeing produces a third fluorescence of wavelength λ23. 
     When the sample  10  flowing through the flow cell  110  is irradiated with light having a wavelength of λ14, this light passes through the cells. The transmitted light of wavelength λ14 transmitted through the cells is used to generate a brightfield image. For example, in the embodiment, the first fluorescence is the wavelength region of green light, the second fluorescence is the wavelength region of red light, and the third fluorescence is the wavelength region of blue light. 
     The condenser lens  150  collects the first fluorescence to the third fluorescence generated from the sample  10  flowing through the flow path  111  of the flow cell  110  and the transmitted light transmitted through the sample  10  flowing through the flow path  111  of the flow cell  110 . The optical unit  151  has a configuration in which four dichroic mirrors are combined. The four dichroic mirrors of the optical unit  151  reflect the first fluorescence to the third fluorescence and the transmitted light at slightly different angles, and separate them on the light receiving surface of the imaging unit  160 . The condenser lens  152  collects the first fluorescence to the third fluorescence and the transmitted light. 
     The imaging unit  160  is configured by a TDI (Time Delivery Integration) camera. The imaging unit  160  captures the first fluorescence to the third fluorescence and the transmitted light to obtain a fluorescence image corresponding to the first fluorescence to the third fluorescence and a bright field image corresponding to the transmitted light, which are output as imaging signals to the cell analysis device  400 A. The image to be captured may be a color image or a grayscale image. 
     The cell imaging device  100 A also may be provided with a pretreatment device  300  as necessary. The pretreatment device  300  samples a portion of the sample and performs FISH, immunostaining, intracellular organelle staining, or the like on the cells contained in the sample to prepare the sample  10 . 
     3-1-3. Cell Analysis Device 
     (1) Hardware Structure 
     The hardware structure of the cell analyzer  400 A will be described with reference to  FIG. 17 . The cell analysis device  400 A is communicably connected to the cell imaging device  100 A. The cell analysis device  400 A includes a control unit  40 A, an input unit  46 , and an output unit  47 , and a media drive  98 . The cell analysis device  400 A can be connected to the network  99 . 
     The structure of the control unit  40 A is the same as the structure of the control unit  20 A of the training device  200 A. Here, the CPU  21 , the memory  22 , the storage unit  23 , the bus  24 , the I/F unit  25 , and the GPU  29  in the control unit  20 A of the training device  200 A are replaced with the CPU  41 , the memory  42 , the storage unit  43 , the bus  44 , and the I/F unit  45 , and GPU  49 , respectively. However, the storage unit  43  stores the trained artificial intelligence algorithms  60  and  63  generated by the training device  200 A and acquired by the CPU  41  from the I/F unit  45  via the network  99  or the media drive  98 . 
     The analysis images  80  and  85  can be acquired by the cell imaging device  100 A and stored in the storage unit  43  or the memory  42  of the control unit  40 A of the cell analysis device  400 A. 
     (2) Function Structure of Cell Analysis Device 
       FIG. 18  shows the function structure of the cell analysis device  400 A. The cell analysis device  400 A includes an analysis data generation unit  401 , an analysis data input unit  402 , an analysis unit  403 , an analysis data database (DB)  404 , and an algorithm databases (DB)  405 ( a ) and  405 ( b ). Step S 21  shown in  FIG. 19  corresponds to the analysis data generation unit  401 . Step S 22  shown in  FIG. 19  corresponds to the analysis data input unit  402 . Step S 23  shown in  FIG. 19  corresponds to the analysis unit  403 . The analysis data database  404  stores analysis data  82 ,  88 . 
     The trained first artificial intelligence algorithm  60  is recorded in the algorithm database  405 ( a ) in association with the exam items and analysis items for inspecting chromosomal abnormalities. The trained second artificial intelligence algorithm  63  is recorded in the algorithm database  405 ( b ) in association with the exam items and analysis items for testing peripheral circulating tumor cells. 
     (3) Cell Analysis Process 
     The control unit  40 A of the cell analysis device  400 A performs the cell analysis process shown in  FIG. 19 . This embodiment facilitates high-precision and high-speed analysis. 
     The CPU  41  of the control unit  40 A starts the cell analysis process according to a request from the user to start the process or when the cell imaging device  100 A starts the analysis. 
     The control unit  40 A receives an exam item by the input unit  46  in step S 20  shown in  FIG. 19 . Specifically, the exam item is accepted by reading the information about the exam item from the barcode attached to each sample by the barcode reader which is an example of the input unit  46 . In step S 21 , the control unit  40 A selects a channel to be used for generating analysis data from the cell image of each channel output from the cell imaging device  100 A according to the exam item received in step S 20 , selects the channel to be used to generate the analysis data, acquires the cell image corresponding to the selected channel, and generates the u=integrated analysis data  82  or integrated analysis data  87 . The method of generating the integrated analysis data  82  is as described in section 2-1 above. The method of generating the integrated analysis data  87  is as described in section 2-2 above. The control unit  40 A stores the generated integrated analysis data  82  or integrated analysis data  87  in the storage unit  43  or the memory  42 . 
     In step S 22 , the control unit  40 A selects the first artificial intelligence algorithm  60  or the second artificial intelligence algorithm  63  according to the exam item received in step S 20 . The received exam item and the artificial intelligence algorithm are linked by the exam item/algorithm table shown in  FIG. 20 . The exam item/algorithm table is stored in the storage unit  43 . The exam item/algorithm table shown in  FIG. 20  exemplifies “chromosomal abnormalities” and “peripheral circulating tumor cells” as “exam items”. The exam item “chromosomal abnormality” corresponds to “BCR-ABL”, “PML-RARA”, “IGH-CCND1, IGH-FGFR3, IGH-MAF” as analysis items, and the exam item “peripheral circulating tumor cells” corresponds to “CTC” as an analysis item. In addition, each exam item has labels of “green”, “yellow”, “blue”, and “red” indicating the wavelength region of fluorescence labeled on the target site, and “bright” indicating brightfield imaging is labeled “Field of View”. The wavelength range of each fluorescence label of “ch1”, “ch2”, “ch3”, “ch4”, and “brightfield”, which are labels of the names of imaging channels corresponding to region and bright field imaging, also are attached. Then, the trained first artificial intelligence algorithm 60 is associated with the analysis items “BCR-ABL”, “PML-RARA”, and “IGH-CCND1, IGH-FGFR3, IGH-MAF”. A trained second artificial intelligence algorithm  63  is associated with the analysis item “CTC”. 
     In step S 22 , when the exam item is “chromosomal abnormality”, the control unit  40 A selects the first artificial intelligence algorithm  60  according to the label of the analysis item, whereas when the second artificial intelligence algorithm  63  is selected according to the label of the analysis item when the exam item is “peripheral circulating tumor cells”. 
     In step S 23 , the control unit  40 A uses the selected first artificial intelligence algorithm  60  or the second artificial intelligence algorithm  63  to determine the properties of the analysis target cells in the analysis images  80 A and  80 B, and stores the label value  84  of the determination result in the storage unit  43  or the memory  42 . The determination method is as described in sections 2-1 and 2-2 above. 
     The control unit  40 A determines whether all the analysis images  80 A and  80 B have been determined in step S 24 , and when all the analysis images  80 A and  80 B have been determined (in the case of “YES”), proceeds to step S 25 , and the determination result corresponding to the label value  84  of the determination result is stored in the storage unit  43 , and the determination result is output to the output unit. When all the analysis images  80 A and  80 B are not determined in step S 24  (in the case of “NO”), the control unit  40 A updates the analysis images  80 A and  80 B in step S 26 , and step S 21  to step S 24  are repeated until the determinations are made for all the analysis images  80 A and  80 B. Although the determination result may be the label value itself, the determination result also may be a label such as “yes”, “no” or “abnormal”, “normal” corresponding to each label value. 
     (4) Cell Analysis Program 
     The present embodiment includes a computer program for performing cell analysis that causes a computer to perform the processes of steps S 20  to S 26  and steps S 221  to S 222 . 
     An implementation of the present embodiment relates to a program product such as a storage medium that stores the computer program. That is, the computer program can be stored on a hard disk, a semiconductor memory element such as a flash memory, or a storage medium such as an optical disk. The recording format of the program on the storage medium is not limited insofar as the training device  200 A can read the program. Recording on the storage medium is preferably non-volatile. 
     Here, the “program” is a concept including not only a program that can be directly executed by the CPU, but also a source format program, a compressed program, an encrypted program, and the like. 
     4. Other Embodiments 
     (1) Modification Example of Analysis Device 
     In the first embodiment, the control unit  40 A is described in terms of an example of selecting an artificial intelligence algorithm based on the exam items received in S 20 . However, instead of the exam item, it may be based on the analysis mode shown in  FIGS. 36 and 37 . 
     The control unit  40 A accepts the analysis mode by the input unit  46  in step S 200  shown in  FIG. 36 . Specifically, the analysis mode reception screen shown in  FIG. 37  is displayed on the touch panel display having the functions of the input unit  46  and the output unit  47 , and the analysis is performed by accepting the chromosomal abnormality determination mode button  801  or the CTC determination mode  802 . n step S 201 , the control unit  40 A selects a channel to be used for generating analysis data from the cell image of each channel output from the cell imaging device  100 A according to the exam item received in step S 200 , selects the channel to be used to generate the analysis data, acquires the cell image corresponding to the selected channel, and generates the integrated analysis data  82  or integrated analysis data  87 . The method of generating the integrated analysis data  82  is as described in section 2-1 above. The method of generating the integrated analysis data  87  is as described in section 2-2 above. The control unit  40 A stores the generated integrated analysis data  82  or integrated analysis data  87  in the storage unit  43  or the memory  42 . 
     In step S 202 , the control unit  40 A selects the first artificial intelligence algorithm  60  when the received analysis mode is the “chromosomal aberration determination mode”, and selects the second artificial intelligence algorithm  63  when the received analysis mode is the “CTC determination mode”. 
     In step S 23 , the control unit  40 A uses the selected first artificial intelligence algorithm  60  or the second artificial intelligence algorithm  63  to determine the properties of the analysis target cells in the analysis images  80 A and  80 B, and stores the label value  84  of the determination result in the storage unit  43  or the memory  42 . The determination method is as described in sections 2-1 and 2-2 above. 
     Since the processing of steps S 204  to S 206  by the control unit  40 A is the same as that of steps S 24  to S 26  described with reference to  FIG. 19 , the description thereof will be omitted. 
     (2) Modification Example of the Imaging Unit 
     In the first embodiment, the above sections 1 to 4 have described an example in which the imaging unit  160  is provided in the imaging flow cytometer. However, the microscope  700  shown in  FIGS. 21 and 22  also may be used instead of the imaging flow cytometer. Here, the microscope shown in  FIG. 21  is disclosed in US Pat. No. 2018-0074308 and is incorporated herein by reference. 
     As shown in  FIG. 21 , the microscope device  700  includes a housing unit  710  and a moving unit  720 . The microscope device  700  includes an imaging unit  710   d  and a slide installation unit  711 . The imaging unit  710   d  includes an objective lens  712 , a light source  713 , and an imaging element  714 . The slide installation unit  711  is provided on the upper surface (the surface on the Z1 direction side) of the housing unit  710 . The objective lens  712 , the light source  713 , and the image sensor  714  are provided inside the housing unit  710 . The microscope device  700  includes a display unit  721 . The display unit  721  is provided on the front surface (the surface on the Y1 direction side) of the moving unit  720 . The display surface  721   a  of the display unit  721  is arranged on the front side of the moving unit  720 . The microscope device  700  includes a drive unit  710   a  that moves the moving unit  720  relative to the housing unit  710 . 
     The slide installation unit  711  includes a stage  711   a.  The stage  711   a  can move in the horizontal direction (X direction and Y direction) and in the vertical direction (Z direction). The stage  711   a  can move independently of each other in the X direction, the Y direction, and the Z direction. In this way the slide can be moved relative to the objective lens  712 , so that the desired position of the slide can be magnified and viewed. 
     The objective lens  712  is arranged close to the stage  711   a  of the slide installation unit  711 . The objective lens  712  is arranged close to the lower side (Z2 direction) of the stage  711   a  of the slide installation unit  711 . The objective lens  712  is provided so as to face the slide installation unit  711  in the vertical direction (Z direction). The objective lens  712  is arranged so that the optical axis is substantially perpendicular to the slide installation surface on which the slide of the slide installation unit  711  is installed. The objective lens  712  is arranged so as to face upward. The objective lens  712  can move relative to the slide installation unit  711  in the vertical direction (Z direction). The objective lens  712  is arranged so as to have a longitudinal direction in the vertical direction. That is, the objective lens  712  is arranged so as to have an optical axis in the substantially vertical direction. The objective lens  712  includes a plurality of lenses. 
     The light source  713  can irradiate the slide coated with the sample with light. The light source  713  irradiates the slide with light via the objective lens  712 . The light source  713  irradiates the slide with light from the same side as the image sensor  714 . The light source  713  can output light having a predetermined wavelength. The light source  713  can output light having a plurality of different wavelengths. That is, the light source  713  can output different types of light. The light source  713  includes a light emitting element. The light emitting element includes, for example, an LED element, a laser element, and the like. 
       FIG. 22  shows a structural example of the optical system of the microscope device  700 . The microscope device  700  includes an objective lens  712 , a light source  713 , an image sensor  714 , a first optical element  715 , a filter  716   a,  a second optical elements  716   b,    716   c,    716   f,  and  716   g,  lenses  716   d,    716   e  and  716   h,  reflective parts  717   a,    717   b  and  717   d,  and a lens  717   c.  The objective lens  712 , the light source  713 , the image sensor  714 , and the first optical element  715 , the filter  716   a,  the second optical elements  716   b,    716   c,    716   f  and  716   g,  the lenses  716   d,    716   e  and  716   h,  the reflecting parts  717   a,    717   b  and  717   d,  and the lens  717   c  are located within the housing unit  710 . 
     The first optical element  715  is configured to reflect the light emitted from the light source  713  in the optical axis direction of the objective lens  712 , and transmit the light from the slide. The first optical element  715  includes, for example, a dichroic mirror. That is, the first optical element  715  is configured to reflect light having a wavelength emitted from the light source  713  and transmit the wavelength of light generated from the slide. 
     The filter  716   a  is configured to transmit light of a predetermined wavelength to block light of other wavelengths, or to block light of a predetermined wavelength and transmit light of other wavelengths. That is, light of a desired wavelength is transmitted by the filter  716   a  and reaches the image sensor  714 . 
     The second optical elements  716   b,    716   c,    716   f  and  716   g  are configured to reflect the light from the slide toward the image sensor  714 . The second optical elements  716   b,    716   c,    716   f  and  716   g  include a reflecting part. The second optical elements  716   b,    716   c,    716   f  and  716   g  include, for example, a mirror. 
     The reflecting parts  717   a,    717   b  and  717   d  are configured to reflect the light from the light source  713  toward the objective lens  712 . Reflecting parts  717   a,    717   b  and  717   d  include, for example, mirrors. 
     The light emitted from the light source  713  is reflected by the reflecting portion  717   a  and incident on the reflecting portion  17   b.  The light incident on the reflecting portion  717   b  is reflected and incident on the reflecting portion  717   d  via the lens  717   c.  The light incident on the reflecting portion  717   d  is reflected and incident on the first optical element  715 . The light incident on the first optical element  715  is reflected and reaches the slide installation portion  11  via the objective lens  712 , and is irradiated to the slide. 
     The light emitted from the slide based on the light of the light source  713  is incident on the first optical element  715  via the objective lens  712 . The light incident on the first optical element  715  is transmitted and enters the second optical element  716   b  through the filter  716   a.  The light incident on the second optical element  716   b  is reflected and incident on the second optical element  716   c . The light incident on the second optical element  716   c  is reflected and incident on the second optical element  716   f  via the lenses  716   d  and  716   e.  The light incident on the second optical element  716   f  is reflected and incident on the second optical element  716   g.  The light incident on the second optical element  716   g  is reflected and reaches the image pickup element  714  via the lens  16   h.  The image sensor  714  captures an enlarged image of the slide based on the light that reaches it. 
     The captured image is transmitted from the microscope  700  to the computer  800  shown in  FIG. 21 . The computer  800  corresponds to a generator ( 200 A) and/or a cell analyzer ( 400 A). 
     III. Second Embodiment 
     A second embodiment relates to a method of analyzing a cell using an artificial intelligence algorithm from waveform data based on signal strength from the cell. 
     1. Cell Analysis Method 
     The present embodiment relates to a cell analysis method for analyzing cells contained in a biological sample. In the analysis method, numerical data corresponding to the signal strength for each cell is input to the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563  having a neural network structure. Then, based on the result output from the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563 , the type of cell for which the signal strength has been acquired is determined for each cell. 
     The type of cell to be determined in a certain form of the present embodiment is based on the type of cell based on the morphological classification, and differs depending on the type of the biological sample. When the biological sample is blood and the blood is collected from a healthy person, the types of cells to be determined in this embodiment include erythrocytes, nucleated cells such as leukocytes, and platelets, included. Nucleated cells include neutrophils, lymphocytes, monocytes, eosinophils and basophils. Neutrophils include lobulated neutrophils and rod-shaped neutrophils. On the other hand, when the blood is collected from an unhealthy person, the nucleated cells may contain at least one selected from the group consisting of immature granulocytes and abnormal cells. Such cells are also included in the type of cells to be determined in this embodiment. Immature granulocytes can include cells such as metamyelocytes, myelocytes, promyelocytes, and myeloblasts. 
     A method of generating training data  575  and a method of analyzing waveform data will be described with reference to the examples shown in  FIGS. 23 to 25 . Here, the term “train” or “training” may be used in place of the term “generate” or “generating”. For convenience of description, “analysis target image” may be referred to as “analysis image”, “data to be analyzed” may be referred to as “analysis data”, “image for training” may be referred to as “training image”, and “data for training” may be referred to as “training data”. The “fluorescent image” is intended to be a training image obtained by imaging a fluorescent label or an analysis image obtained by imaging a fluorescent label. 
     (1) Training Data Generation 
     The example shown in  FIG. 23  is an example of a method for generating training waveform data used for training a third artificial intelligence algorithm for determining the types of leukocytes, immature granulocytes, and abnormal cells. The waveform data for forward scattered light  570   a,  the waveform data  570   b  for lateral scattered light, and the waveform data  570   c  for lateral fluorescence, which are the waveform data for training, are associated with the cells to be trained. Waveform data of forward scattered light  570   a,  waveform data of side scattered light  570   b,  and waveform data  570   c  of lateral fluorescence also are referred to as training waveform data  570 . The training waveform data  570   a,    570   b,  and  570   c  obtained from the training target cells may be waveform data obtained by flow cytometry of cells whose cell types are known based on morphological classification. Alternatively, waveform data of cells whose cell type has already been determined from a scattergram of a healthy person may be used. Further, as the waveform data in which the cell type of a healthy person is determined, a pool of waveform data of cells acquired from a plurality of people may be used. The sample for acquiring the training waveform data  570   a,    570   b , and  570   c  is preferably processed from a sample containing cells of the same type as the cells to be trained by the same sample processing method as the sample containing the cells to be trained. It is preferable that the training waveform data  570   a,    570   b,  and  570   c  are acquired under the same conditions as those for the cells to be analyzed. The training waveform data  570   a,    570   b,  and  570   c  can be obtained in advance for each cell by, for example, a known flow cytometry or sheath flow electrical resistance method. Here, when the cells to be trained are erythrocytes or platelets, the training data may be waveform data acquired by the sheath flow electric resistance method, and the waveform data may be a type obtained from the electrical signal strength. 
     In the example shown in  FIG. 23 , training waveform data  570   a,    570   b , and  570   c  acquired by flow cytometry using Sysmex XN-1000 are used. For the training waveform data  570   a,    570   b,  and  570   c,  for example, after the forward scattered light reaches a predetermined threshold, acquisition of the forward scattered light signal intensity, the side scattered light signal intensity, and the side fluorescence signal intensity is started, and in this example, waveform data are acquired at a plurality of time points at regular intervals for one training target cell until the acquisition is completed after a predetermined time. Examples of acquiring waveform data at a plurality of time points at regular intervals include 1024 points at 10 nanosecond intervals, 128 points at 80 nanosecond intervals, 64 points at 160 nanosecond intervals, and the like. For waveform data, the cells contained in the biological sample are flowed through the flow path for cell detection in the measurement unit, which is equipped with a flow cytometer, a sheath flow electric resistance type measuring device, or the like which can detect cells individually and are acquired for individual cells passing through the flow path. Specifically, a data group having a value indicating the time at which the signal strength was acquired and a value indicating the signal strength at that time as elements is acquired for each signal at multiple time points during the passage of one trained cell through a predetermined position in the flow path, and is used as training waveform data  570   a,    570   b,  and  570   c.  The information about the time point is not limited insofar as it can be stored so that the control units  10 T and  20 T, which will be described later, can determine how much time has passed since the acquisition of the signal strength was started. For example, the information at the time point may be the time from the start of measurement, or may be the number of points. It is preferable that the signal strength is stored in the storage units  13 ,  23  or the memories  12 ,  22  described later together with the information at the time when the signal strength is acquired. 
     The training waveform data  570   a,    570   b,  and  570   c  in  FIG. 23  are represented by raw data values, such as forward scattered light sequence data  572   a,  side scattered light sequence data  572   b,  and side fluorescence sequence data  572   c.  The sequence data  572   a,    572   b,  and  572   c  are synchronized at the time when the signal intensity is acquired for each training target cell, and the sequence data  576   a  of the forward scattered light, the sequence data  576   b  of the side scattered light, and the sequence data  576   c  of the lateral fluorescence are synchronized. That is, the second numerical value from the left of  576   a  is the signal strength at the time t=0 when the measurement is started, which is 10. Similarly, the second numerical value from the left of  576   b  and  576   c  is the signal strength at the time t=0 when the measurement is started, and is 50 and 100, respectively. Adjacent cells inside the respective sequence data  576   a,    576   b,  and  576   c  store signal intensities at intervals of 10 nanoseconds. The sequence data  576   a,    576   b,  and  576   c  are combined with a label value of  577  indicating the type of training target cell, and the three signal strengths at the same point (forward scattered light signal strength, side scattered light signal strength, and side) become a set of training data  575  that is input to the third artificial intelligence algorithm  550 . For example, when the target training cell is a neutrophil, the sequence data  576   a,    576   b,  and  576   c  are given “1” as a label value  577  indicating that the cell is a neutrophil, and training data  575  are generated.  FIG. 24  shows an example of the label value  577 . Since the training data  575  are generated for each cell type, the label value is given a different label value of  577  depending on the cell type. Here, the synchronization at the time when the signal intensity is acquired is, for example, combined at the same time in the sequence data  572   a  of the forward scattered light, the sequence data  572   b  of the side scattered light, and the sequence data  572   c  of the side fluorescence for the time from the start of measurement to match the measurement points. In other words, the sequence data  572   a  of the forward scattered light, the sequence data  572   b  of the laterally scattered light, and the sequence data  572   c  of the lateral fluorescence each have the signal strength acquired at the same time in one cell passing through the flow cell. Although the measurement start time may be a time when the signal intensity of the forward scattered light exceeds a predetermined threshold value such as a threshold value, another scattered light or fluorescence signal intensity threshold value may be used. A threshold value also may be set for each sequence data. 
     Although the acquired signal strength value may be used as is for the sequence data  576   a,    576   b,  and  576   c,  processing such as noise removal, baseline correction, and normalization also may be performed as necessary. In the present specification, the “numerical data corresponding to the signal strength” may include the acquired signal strength value itself and a value subjected to noise removal, baseline correction, and alization as necessary. 
     Taking  FIG. 23  as an example, the outline of training of the third artificial intelligence algorithm  550  and the fourth artificial intelligence algorithm  553  having a neural network structure will be described. The third artificial intelligence algorithm  550  is an algorithm for classifying neutrophils, lymphocytes, monocytes, eosinophils, basophils, and immature granulocytes, and the fourth artificial intelligence algorithm  553  is an algorithm for classifying abnormal cells. The third artificial intelligence algorithm  550  and the fourth artificial intelligence algorithm  553  are preferably convolutional neural networks. The number of nodes of the input layer  550   a  in the third artificial intelligence algorithm  550  corresponds to the number of sequences included in the waveform data of the input training data  575 . The training data  575  is combined so that the time points when the sequence data  576   a,    576   b,  and  576   c  acquire the signal strength are the same points, and the training data is input to the input layer  550   a  of the third artificial intelligence algorithm  550  as the first training data. The label value  577  of each waveform data of the training data  575  is input to the output layer  550   b  of the third artificial intelligence algorithm  550  as the second training data, and used to train a third artificial intelligence algorithm  550 . Reference numeral  550   c  in  FIG. 23  indicates an intermediate layer. The fourth artificial intelligence algorithm  553  has a similar configuration. 
     (2) Analysis Data Generation and Cell Analysis Method 
       FIG. 25  shows an example of a method for analyzing waveform data of cells to be analyzed. In the cell analysis method using the waveform data, the analysis data  585  are generated from the waveform data  580   a  of the forward scattered light, the waveform data  580   b  of the lateral scattered light, and the waveform data  580   c  of the lateral fluorescence acquired from the analysis target cells. The waveform data  580   a,  the waveform data  580   b,  and the waveform data  580   c  are collectively referred to as the waveform data  580  for analysis. The waveform data for analysis  580   a,    580   b,  and  580   c  can be acquired by using, for example, known flow cytometry. In the example shown in  FIG. 25 , the analysis waveform data  580   a,    580   b,  and  580   c  are acquired by using Sysmex XN-1000 in the same manner as the training waveform data  570   a,    570   b,  and  570   c.  The analysis waveform data  580   a,    580   b,  and  580   c  are represented by raw data values, for example, forward scattered light sequence data  582   a,  side scattered light sequence data  582   b,  and side fluorescence sequence data  582   c.    
     Regarding the generation of the analysis data  585  and the training data  575 , it is preferable that at least the acquisition conditions and the conditions for generating the data to be input to the neural network from each waveform data and the like are the same. The sequence data  582   a,    582   b,  and  582   c  are synchronized at the time when the signal strength is acquired for each cell to be trained, and become the sequence data is  586   a  (forward scattered light), the sequence data is  586   b  (side scattered light), and the sequence data is  586   c  (side fluorescence). The sequence data  586   a,    586   b,  and  586   c  are combined so that the three signal intensities at the same point (the signal intensity of the forward scattered light, the signal intensity of the side scattered light, and the signal intensity of the side fluorescence) are combined so as to become a set, and the analysis data  585  are input to the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563 . 
     When the analysis data  585  are input to the input layer  560   a  configuring the trained third artificial intelligence algorithm  560  or the input layer  563   a  configuring the fourth artificial intelligence algorithm  563 , the probability that the analysis target cell for which the analysis data  585  have been acquired belongs to each of the cell types input as training data is output from the output layer  560   b  or the input layer  563   a.  Reference numerals  560   c  and  563   c  in  FIG. 25  indicate an intermediate layer. Further, it may be determined that the analysis target cell for which the analysis data  585  have been acquired belongs to the classification having the highest value in this probability, and the label value  582  or the like associated with the cell type may be output. The output analysis result  583  regarding the cell may be data in which the label value itself is replaced with information (for example, terminology or the like) indicating the cell type.  FIG. 25  shows an example based on the analysis data  585 , wherein the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563  outputs the label value “1” having the highest probability that the analysis target cell for which the analysis data  585  have been acquired, and the character data “neutrophil” corresponding to this label value is output as the analysis result  583  for the cell. Although the output of the label value may be performed by the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563 , but other computer programs may be used, another computer program may output the most preferable label value based on the probability calculated by the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563 . 
     2. Cell Analysis System 
     The waveform data of this embodiment can be acquired by the cell analysis system  5000 .  FIG. 26  shows the appearance of the cell analysis system  5000 . The cell analysis system  5000  includes a measurement unit (also referred to as a measurement unit)  600 , processing units  100 T and  200 T in the measurement unit  600  for controlling setting and measurement of sample measurement conditions in the measurement unit  600  which can be connected by wire or wirelessly so that they can communicate with each other. The configuration example of the measurement unit  600  will be shown below, but the mode of the present embodiment is not to be construed as being limited to the following examples. The processing unit  100 T or the processing unit  200 T can be shared with the training device  100 T or the cell analysis device  200 T, which will be described later. Here, an example in which the training device  100 T or the cell analysis device  200 T is used as the processing unit  100 T or the processing unit  200 T will be described. 
     2-1. First Cell Analysis System  5000   
     (1) Structure of First Measurement Unit 
     A structural example will be described with reference to  FIGS. 26 to 28  when the measurement unit  600  is a flow cytometer for detecting nucleated cells in a blood sample. 
       FIG. 27  shows an example of a function structure of the measurement unit  600 ; As shown in this figure, the measurement unit  600  includes a detection unit  610  for detecting blood cells, an analog processing unit  620  for the output of the detection unit  610 , a measurement unit control unit  680 , a display/operation unit  650 , a sample preparation unit  640 , and a device mechanism unit  630 . The analog processing unit  620  performs processing including noise removal on the electric signal as an analog signal input from the detection unit, and outputs the processed result to the A/D conversion unit  682  as an electric signal. 
     The detection unit  610  functions as a signal acquisition unit, and has a nucleated cell detection unit  611  that detects at least nucleated cells such as white blood cells, an erythrocyte/platelet detection unit  612  that measures the number of erythrocytes and platelets, and if necessary, and a hemoglobin detection unit  613  for measuring the amount of blood pigment in the blood cells. Note that the nucleated cell detection unit  611  is composed of an optical detection unit, and more specifically, has a configuration for performing detection by a flow cytometry method. 
     As shown in  FIG. 27 , the measurement unit control unit  680  includes an A/D conversion unit  682 , a digital value calculation unit  683 , and an interface unit  689  connected to the training device  100 T or the cell analysis device  200 T. The measurement unit control unit  680  includes an interface unit  486  interposed between the display/operation unit  650  and an interface unit  688  interposed between the device mechanism unit  630 . 
     Note that the digital value calculation unit  683  is connected to the interface unit  689  via the interface unit  684  and the bus  685 . The interface unit  689  is connected to the display/operation unit  650  via the bus  685  and the interface unit  486 , and is connected to the detection unit  610 , the device mechanism unit  630 , and the sample preparation unit  640  via the bus  685  and the interface unit  688 . 
     The A/D conversion unit  682  converts the received light signal, which is an analog signal output from the analog processing unit  620 , into a digital signal and outputs the converted signal to the digital value calculation unit  683 . The digital value calculation unit  683  performs a predetermined calculation process on the digital signal output from the A/D conversion unit  682 . A predetermined arithmetic processing includes, for example, during the period after the forward scattered light reaches a predetermined threshold value while acquisition of the signal intensity of the forward scattered light, the signal intensity of the side scattered light, and the signal intensity of the side fluorescence is started and before acquisition ends after a predetermined time, a process of acquiring each waveform data at a plurality of time points at a fixed interval for one training target cell and a process of extracting the peak value of the waveform data, although the invention is not limited to this. Then, the digital value calculation unit  683  outputs the calculation result (measurement result) to the training device  100 T or the cell analysis device  200 T via the interface unit  684 , the bus  685 , and the interface unit  689 . 
     The training device  100 T or the cell analysis device  200 T is connected to the digital value calculation unit  683  via the interface unit  684 , the bus  685 , and the interface unit  689 , such that the training device  100 T or the cell analysis device  200 T can receive the calculation result output from the digital value calculation unit  683 . The training device  100 T or the cell analysis device  200 T controls the device mechanism unit  630  including a sampler (not shown) for automatically supplying the sample container, a fluid system for preparing and measuring the sample, and other controls. 
     The nucleated cell detection unit  611  flows a measurement sample containing cells through a flow path for cell detection, irradiates light on the cells flowing through the flow path for cell detection, and measures scattered light and fluorescence generated from the cells. The erythrocyte/platelet detection unit  612  flows a measurement sample containing cells through a cell detection channel, measures the electrical resistance of the cells flowing through the cell detection channel, and detects the cell volume. 
     In the present embodiment, the measurement unit  600  preferably includes a flow cytometer and/or a sheath flow electric resistance type detection unit. In  FIG. 27 , the nucleated cell detection unit  611  can be a flow cytometer. In  FIG. 27 , the erythrocyte/platelet detection unit  612  may be a sheath flow electrical resistance type detection unit. Here, nucleated cells may be measured by the erythrocyte/platelet detection unit  612 , and erythrocytes and platelets may be measured by the nucleated cell detection unit  611 . 
     (2) Flow Cytometer 
     In the measurement by the flow cytometer shown in  FIG. 28 , when the cells contained in the measurement sample pass through the flow cell (sheath flow cell)  4113  provided in the flow cytometer, a light source  4111  irradiates the flow cell  4113  with light, and the scattered light and fluorescence emitted via this irradiation light from the cells in the flow cell  4113  are detected. 
     In the present embodiment, the scattered light is not particularly limited as long as it is scattered light that can be measured by a flow cytometer that is generally distributed. For example, scattered light includes forward scattered light (for example, a light receiving angle of around 0 to 20 degrees) and side scattered light (light receiving angle of around 90 degrees). It is known that side scattered light reflects internal information of cells such as cell nuclei and granules, and forward scattered light reflects information on cell size. In the present embodiment, it is preferable to measure the forward scattered light intensity and the side scattered light intensity as the scattered light intensity. 
     Fluorescence is the light emitted from the fluorescent dye when energized by the excitation light of an appropriate wavelength with respect to the fluorescent dye bound to the nucleic acid in the cell. The excitation light wavelength and the received light wavelength depend on the type of fluorescent dye used. 
       FIG. 28  shows a structural example of the optical system of the nucleated cell detection unit  611 . In this drawing, the light emitted from the laser diode, which is the light source  4111 , irradiates the cells passing through the flow cell  6113  via the irradiation lens system  4112 . 
     In the present embodiment, the light source  4111  of the flow cytometer is not specifically limited, and a light source  4111  having a wavelength suitable for exciting the fluorescent dye is selected. As such a light source  4111 , for example, a semiconductor laser including a red semiconductor laser and/or a blue semiconductor laser, a gas laser such as an argon laser and a helium-neon laser, a mercury arc lamp, and the like may be used. In particular, semiconductor lasers are suitable because they are much cheaper than gas lasers. 
     As shown in  FIG. 28 , the forward scattered light emitted from the particles passing through the flow cell  4113  is received by the forward scattered light receiving element  4116  via the condenser lens  4114  and the pinhole portion  4115 . The forward scattered light receiving element  4116  may be a photodiode or the like. The side scattered light is received by the side scattered light receiving element  4121  via the condenser lens  4117 , the dichroic mirror  4118 , the bandpass filter  4119 , and the pinhole portion  4120 . The side scattered light receiving element  4121  can be a photodiode, a photomultiplier, or the like. The side fluorescence is received by the side fluorescence light receiving element  4122  via the condenser lens  4117  and the dichroic mirror  4118 . The side fluorescence light receiving element  4122  may be an avalanche photodiode, a photomultiplier, or the like. 
     The received light signals output from the light receiving elements  4116 ,  4121  and  4122  are subjected to analog processing such as amplification and waveform processing by the analog processing unit  620  shown in  FIG. 27  having amplifiers  4151 ,  4152  and  4153 , respectively, and sent to the control unit  680  of the measurement unit. 
     Returning to  FIG. 27 , the measurement unit  600  may include a sample preparation unit  640  for preparing a measurement sample. The sample preparation unit  640  is controlled by the information control unit  481  of the measurement unit via the interface unit  688  and the bus  685 . In  FIG. 29 , a sample preparation unit  640  provided in the measurement unit  600  prepares a measurement sample by mixing a blood sample, a staining reagent, and a hemolytic reagent, and the obtained measurement sample is measured by a nucleated cell detection unit. 
     In  FIG. 29 , the blood sample in the sample container  00   a  is suctioned from the suction pipette  601 . The blood sample quantified by the suction pipette  601  is mixed with a predetermined amount of diluting liquid and transported to the reaction chamber  602 . A predetermined amount of hemolytic reagent is added to the reaction chamber  602 . A predetermined amount of staining reagent is supplied to the reaction chamber  602  and mixed with the above mixture. By reacting a mixture of a blood sample with a staining reagent and a hemolytic reagent in a reaction chamber  602  for a predetermined time, red blood cells in the blood sample are hemolyzed, and a measurement sample in which nucleated cells are stained with a fluorescent dye is obtained. 
     The obtained measurement sample is sent to the flow cell  4113  in the nucleated cell detection unit  611  together with a sheath solution (for example, Cellpack (II), manufactured by Sysmex Corporation), and is measured by a flow cytometry method in the nucleated cell detection unit  611 . 
     2-2. Training device  100 T/ 200 T 
     (1) Training Device Hardware Structure 
       FIG. 30  illustrates an example of the hardware structure of the training device  100 T. The training device  200 A includes a control unit  20 A, an input unit  26 , and an output unit  27 . The training device  100 T can be connected to the network  99 . 
     The Structure of the control unit  10 T is the same as the structure of the control unit  20 A of the training device  200 A. Here, the CPU  21 , the memory  22 , the storage unit  23 , the bus  24 , the I/F unit  25 , and the GPU  29  in the control unit  20 A of the training device  200 A shall be read as the CPU  11 , the memory  12 , the storage unit  13 , the bus  14 , and the I/F unit  15 , and GPU  19 , respectively. However, the storage unit  13  stores the third artificial intelligence algorithm  550  and the fourth artificial intelligence algorithm  560 . 
     The training waveform data  570  can be acquired by the measurement unit  600  and stored in the storage unit  13  or the memory  12  of the control unit  10 T of the training device  100 T. 
     (2) Analysis Device Hardware Structure 
     Referring to  FIG. 31 , the cell analysis device  200 T includes a control unit  20 , an input unit  26 , an output unit  27 , and a media drive D 98 . The cell analysis device  200 T can be connected to the network  99 . 
     The structure of the control unit  20 T is the same as the structure of the control unit  40 A of the cell analysis device  400 A. Here, the CPU  41 , the memory  42 , the storage unit  43 , the bus  44 , the I/F unit  45 , and the GPU  49  in the control unit  40 A of the training device  400 A shall be read as the CPU  21 , the memory  22 , the storage unit  23 , the bus  24 , and the I/F unit  25 , and GPU  29 , respectively. However, the storage unit  23  stores a plurality of trained third artificial intelligence algorithms  560  as a database shown in  FIG. 32 , which will be described later. 
     The training waveform data  580  can be acquired by the measurement unit  600  and stored in the storage unit  23  or the memory  22  of the control unit  20 T of the training device  200 T. 
     (3) Function Structure of Training Device 
     Referring to  FIG. 32 , the control unit  10 T of the training device  100 T includes a training data generation unit T 101 , a training data input unit T 102 , and an algorithm update unit T 103 . Step S 1001  shown in  FIG. 34  corresponds to the training data generation unit T 101 . Step S 1002  shown in  FIG. 34  corresponds to the training data input unit T 102 . Step S 1004  shown in  FIG. 34  corresponds to the algorithm update unit T 103 . The training data database (DB) T 104  and the algorithm databases (DB) T 105 ( a ) and T 105 ( b ) can be recorded in the storage unit  13  of the control unit  10 T. 
     The training waveform data  570   a,    570   b,  and  570   c  are acquired in advance by the measurement unit  600  and stored in advance in the training data database T 104 ( a ) of the control unit  10 T. The third artificial intelligence algorithm  550  is stored in advance in the algorithm database T 105 ( b ). 
     (4) Function Structure of Cell Analysis Device 
       FIG. 33  shows the function structure of the cell analyzer  200 T. The cell analysis device  200 T includes an analysis data generation unit T 201 , an analysis data input unit T 202 , an analysis unit T 203 , an analysis data database (DB) T 204 , and an algorithm database (DB) T 205 ( a ) and T 205 ( b ). Step S 2001  shown in  FIG. 35  corresponds to the analysis data generation unit T 201 . Step S 2002  shown in  FIG. 35  corresponds to the analysis data input unit T 202 . Step S 2003  shown in  FIG. 35  corresponds to the analysis unit T 403 . The waveform data  580  for analysis are acquired by the measurement unit  600  and stored in the analysis data database T 204 . The plurality of trained third artificial intelligence algorithms  560  are stored in the algorithm database T 205 ( a ). The fourth artificial intelligence algorithm  563  is stored in the algorithm database T 205 ( b ). 
     (5) Training Process 
       FIG. 34  shows an example of the processing performed by the control unit  10 T of the training device  100 T. 
     First, the control unit  10 T acquires training waveform data  570   a,    570   b ,  570   c.  The training waveform data  570   a  are the waveform data of the forward scattered light, the training waveform data  570   b  are the waveform data of the side scattered light, and the training waveform data  570   c  are the waveform data of the side fluorescence. The acquisition of the training waveform data  570   a,    570   b,    570   c  is taken from the measurement unit  600  or taken from the media drive D 98  by an operation of the operator via the I/F section  15  and the network. When the training waveform data  570   a,    570   b,    570   c  are acquired, information indicating which cell type the training waveform data  570   a,    570   b,    570   c  represents is also acquired. The information indicating which cell type is represented is tied to the training waveform data  570   a,    570   b,    570   c,  or the operator may input such from the input unit  16 . 
     In step S 1001 , the control unit  10 T associates information indicating the cell type associated with the training waveform data  570   a,    570   b,  and  570   c,  a label value associated with the cell type stored in the memory  12  or the storage unit  13 , and a label value  577  corresponding to sequence data  576   a,    576   b,    576   c  synchronized with the waveform data of the forward scattered light, side scattered light, and side fluorescent light during waveform data acquisition with the sequence data  572   a,    572   b,    572   c.  Thus, the control unit  10 T generates training data  575 . 
     In step S 1002 , the control unit  10 T trains the third artificial intelligence algorithm  550  or the fourth artificial intelligence algorithm  553  using the training data  575 . The training results of the third artificial intelligence algorithm  550  and the fourth artificial intelligence algorithm  553  are accumulated each time training is performed using a plurality of training data  575 . When the label value  577  of the training data  575  indicates neutrophils, lymphocytes, monocytes, eosinophils, basophils, and immature granulocytes, the third artificial intelligence algorithm  550  is trained, whereas when the label value  577  of  575  indicates abnormal cells, the fourth artificial intelligence algorithm  553  is trained. 
     In the cell type analysis method according to the present embodiment, since a convolutional neural network and a stochastic gradient descent method are used, in step S 1003 , the control unit  10 T determines whether the accumulated training results have reached a predetermined number of training cycles. When the training results have accumulated for a predetermined number of cycles (when “YES”), the control unit  10 T proceeds to the process of step S 1004 , and when the training results have not accumulated for a predetermined number of cycles (“NO”), the control unit  10 T proceeds to the process of step S 15 . 
     Next, when the training results are accumulated for a predetermined number of cycles, in step S 1004 , the control unit  10 T uses the training results accumulated in step S 1002  to update the weight w of the third artificial intelligence algorithm  550  or the fourth artificial intelligence algorithm  553 . Since the cell type analysis method according to the present embodiment uses the stochastic gradient descent method, the weight w of the third artificial intelligence algorithm  550  or the fourth artificial intelligence algorithm  553  is updated at the stage at which the training results for a predetermined number of trials are accumulated. 
     In step S 1005 , the control unit  10 T determines whether the third artificial intelligence algorithm  550  or the fourth artificial intelligence algorithm  553  has been trained with the specified number of training data  575 . When training is performed with the specified number of training data  575  (in the case of “YES”), the training process is terminated. 
     When the control unit  10 T determines in step S 1005  that the third artificial intelligence algorithm  550  or the fourth artificial intelligence algorithm  553  has not been trained with the specified number of training data  575  (in the case of “NO”), the routine advances from step S 1005  to step S 1006 , and the next training waveform data  570  is processed from step S 1001  to step S 1005 . 
     According to the process described above, the control unit  10 T trains the third artificial intelligence algorithm  550  and the fourth artificial intelligence algorithm  553 , and generates the third artificial intelligence algorithm  560  and the fourth artificial intelligence algorithm  563 . The third artificial intelligence algorithm  560  and the fourth artificial intelligence algorithm  563  can be recorded on one computer. 
     (6) Cell Analysis Process 
     First, the control unit  20 T acquires analysis waveform data  580   a,    580   b , and  580   c.  The acquisition of the waveform data for analysis  580   a,    580   b,  and  580   c  can be performed by the user&#39;s operation or automatically from the measurement unit  600 , from the recording medium  98 , or via the I/F unit  25  via the network. 
     In step S 2000  shown in  FIG. 35 , the control unit  20 T receives an exam item by the input unit  26 . Specifically, the exam item is accepted by reading the information about the exam item from the barcode attached to each sample by the barcode reader which is an example of the input unit  46 . The exam item is selected from “blood cell classification test” and “abnormal cell test”. In step S 2001 , the control unit  20 T generates analysis data  585  for cells from the sequence data  582   a,    582   b,    582   c  according to the procedure described in the above cell analysis method. The control unit  20 T stores the generated analysis data  585  in the storage unit  23  or the memory  22 . 
     In step S 2002 , the control unit  20 T selects the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563  according to the exam item received in step S 2000 . The control unit  20 T selects the third artificial intelligence algorithm  560  when the exam item is “blood cell classification test”, and selects the fourth artificial intelligence algorithm  563  when the exam item is “abnormal cell test”. 
     In step S 2003 , the control unit  20 T classifies the analysis target cells using the third artificial intelligence algorithm  560  or the fourth artificial intelligence algorithm  563  selected in step S 2003 , and stores the label value  577  of the determination result in the storage unit  43  or the memory  42 . 
     The control unit  20 T determines whether all the analysis target cells have been determined in step S 2004 , and if all have been determined (in the case of “YES”), proceeds to step S 2005  and determines the determination result, and outputs the determination result to the output unit. If not all have been determined in step S 2004  (in the case of “NO”), the control unit  20 T updates the analysis target cells in step S 2006  and repeats steps S 2001  to S 2004  until all the determinations are made. 
     According to each of the above-described embodiments, it is possible to determine the cell type regardless of the skill of the inspector. It also facilitates the analysis of a plurality of analysis items in cell analysis. 
     (7) Various Programs 
     A second embodiment includes a computer program for training an artificial intelligence algorithm that causes a computer to perform the processes of steps S 1001  to S 1006 . 
     A second embodiment includes a computer program for analyzing cells that causes a computer to perform the processes of steps S 2000 -S 2006  and S 2201 -S 2002 . 
     An implementation of the present embodiment relates to a program product such as a storage medium that stores the computer program. That is, the computer program can be stored on a hard disk, a semiconductor memory element such as a flash memory, or a storage medium such as an optical disk. The recording format of the program on the storage medium is not limited insofar as the training device  200 A can read the program. Recording on the storage medium is preferably non-volatile. 
     Here, the “program” is a concept including not only a program that can be directly executed by the CPU, but also a source format program, a compressed program, an encrypted program, and the like. 
     IV. Other 
     The present invention shall not be construed as being limited to the embodiments described above. For example, in the above embodiment, the algorithm to be used is selected according to the received exam item, but the algorithm to be used also may be selected according to the received analysis item after receiving the analysis item.