Patent Publication Number: US-2021164886-A1

Title: Cell analysis method, cell analysis device, cell analysis system, cell analysis program, and trained artificial intelligence algorithm generation method

Description:
RELATED APPLICATIONS 
     This application claims priority to Japanese Patent Application No. 2019-217159, filed on Nov. 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, and trained artificial intelligence algorithm generation method, generation device, and generation 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 examinations of patients who may have a tumor, it is necessary to understand the presence of abnormal cells such as peripheral circulating tumor cells and the proportion of cells having chromosome abnormality in a sample containing multiple types of cells to determine the presence or absence of a tumor, the effect of anticancer therapy, the presence or absence of recurrence and the like. 
     A number of abnormal cells contained in a sample may be very small compared with the number of normal cells that should originally be present in the sample. Therefore, it is necessary to analyze more cells in order to detect abnormal cells contained in the sample. However, since the method described in WIPO Patent Publication No. 2015/065697 uses a microscope image, increasing the number of cells to be determined increases the time required to acquire the microscope image. 
     The present invention provides a cell analysis method, a cell analysis device, a cell analysis system, a cell analysis program, and a trained artificial intelligence algorithm generation method, generation device, and generation program to facilitate high-accuracy and high-speed analysis of more cells in the sample. 
     One embodiment of the present invention relates to a cell analysis method for analyzing cells using an artificial intelligence algorithm ( 60 ,  63 ,  97 ). The cell analysis method causes a sample ( 10 ) containing cells to flow through a flow channel ( 111 ), images cells passing through the flow channel ( 111 ) to generate analysis target images ( 80 ,  85 ,  95 ), generates analysis data ( 82 ,  87 ,  96 ) from the generated analysis target images ( 80 ,  85 ,  95 ), inputs the generated analysis data to the artificial intelligence algorithm ( 60 ,  63 ,  97 ), and generates data ( 84 ,  88 ,  98 ) indicating the properties of the cells contained in the analysis target images ( 80 ,  85 ,  95 ) by the artificial intelligence algorithm. 
     One embodiment of the present invention relates to a cell analysis device ( 400 A,  200 B,  200 C) that analyzes cells using an artificial intelligence algorithm ( 60 ,  63 ,  97 ). The cell analysis device ( 400 A,  200 B,  200 C) includes a control unit ( 40 A,  20 B,  20 C) configured to cause a sample ( 10 ) containing cells to flow in a path ( 111 ), inputs analysis data ( 82 ,  87 ,  96 ) generated from analysis target images ( 80 ,  85 ,  95 ) of cells passing through the flow path ( 111 ) into an artificial intelligence algorithm ( 60 ,  63 ,  97 ), and generates data  84 ,  88 ,  98 ) indicating the properties of the cell contained in the analysis target images ( 80 ,  85 ,  95 ) by the artificial intelligence algorithm  60 ,  63 ,  97 ). 
     One embodiment of the present invention relates to a cell analysis system ( 1000 ,  2000 ,  3000 ). Cell analysis system ( 1000 ,  2000 ,  3000 ) includes a flow cell ( 110 ) through which a sample ( 10 ) containing cells flows, light sources ( 120 ,  121 ,  122 ,  123 ) for irradiating light on the sample ( 10 ) flowing in the flow cell ( 110 ), an imaging unit ( 160 ) for imaging the cells in the sample ( 10 ) irradiated with the light, and a control unit ( 40 A,  20 B,  20 C). The control unit ( 40 A,  20 B,  20 C) is configured to acquire, as the analysis target images ( 80 ,  85 ,  95 ), images of the cells passing through the inside of the flow path ( 111 ) captured by the imaging unit ( 160 ), generate analysis target data ( 82 ,  87 ,  96 ) from the analysis target images ( 80 ,  80 ,  85 ,  95 ), input the analysis data ( 82 ,  87 ,  96 ) to the artificial intelligence algorithm ( 60 ,  63 ,  97 ), and generate data ( 84 ,  88 ,  98 ) indicating the properties of cells included in the analysis target images ( 80 ,  85 ,  95 ). 
     One embodiment of the present invention relates to a cell analysis program for analyzing cells. The cell analysis program executes processing including a step (S 22 ) of flowing a sample ( 10 ) containing cells into a flow path ( 111 ) and inputting analysis data ( 82 ,  87 ,  96 ) generated from analysis target images ( 80 ,  85 ,  95 ) obtained by imaging cells passing through the flow path ( 111 ) into an artificial intelligence algorithm ( 60 ,  63 ,  97 ), and a step (S 23 ) of generating data ( 84 ,  88 ,  98 ) indicating the properties of cells included in the analysis target images ( 80 ,  85 ,  95 ) by the artificial intelligence algorithm ( 60 ,  63 ,  97 ). 
     The cell analysis device ( 400 A,  200 B,  200 C), cell analysis system ( 1000 ,  2000 ,  3000 ), and cell analysis program facilitate high-accuracy and high-speed analysis of more cells contained in a sample. 
     One embodiment of the invention relates to a trained artificial intelligence algorithm ( 60 ,  63 ,  97 ) generation method for analyzing cells. The generation method includes inputting training data ( 73 ,  78 ,  92 ) generated from training images ( 70 ,  75 ,  90 ) which capture a cell passing through a flow path ( 111 ) when flowing a sample ( 10 ) containing cells in the flow path ( 111 ), and inputting a label ( 74 P,  74 N,  79 P,  79 N,  93 P,  93 N) showing the properties of cells contained in the training image ( 70 ,  75 ,  90 ) into an artificial intelligence algorithm ( 50 ,  53 ,  94 ) to train the artificial intelligence algorithm ( 50 ,  53 ,  94 ). 
     One embodiment of the present invention relates to a trained artificial intelligence algorithm ( 60 ,  63 ,  97 ) generation device ( 200 A,  200 B,  200 C) for analyzing cells. The generation device ( 200 A,  200 B,  200 C) is provided with a control unit ( 20 A,  20 B,  20 C) configured to input training data ( 73 ,  78 ,  92 ) generated from training image ( 70 ,  75 ,  90 ) of a cell passing through a flow path ( 111 ) when flowing a sample ( 10 ) containing cells in the flow path ( 111 ), and input a label ( 74 P,  74 N,  79 P,  79 N,  93 P,  93 N) indicating a property of a cell included in the training image ( 70 ,  75 ,  90 ) to an artificial intelligence algorithm ( 50 ,  53 ,  94 ) to train the artificial intelligence algorithm ( 50 ,  53 ,  94 ). 
     One embodiment of the present invention relates to a trained artificial intelligence algorithm ( 60 ,  63 ,  97 ) generation program for analyzing cells. The generation program executes processing including a step (S 12 ) of inputting training data ( 73 ,  78 ,  92 ) generated from training images ( 70 ,  75 ,  90 ) of a cell passing through a flow path ( 111 ) when flowing a sample ( 10 ) containing cells in the flow path ( 111 ) and inputting a label ( 74 P,  74 N,  79 P,  79 N,  93 P,  93 N) indicating the properties of cells contained in the training image ( 70 ,  75 ,  90 ) into the artificial intelligence algorithm ( 50 ,  53 ,  94 ), and a step (S 12 ) of training the artificial intelligence algorithm ( 50 ,  53 ,  94 ). 
     An artificial intelligence algorithm ( 60 ,  63 ,  97 ) can be generated to facilitate high-speed high-accuracy analysis of cells contained in a sample by a trained artificial intelligence algorithm ( 60 ,  63 ,  97 ) generation method, generation device ( 200 A,  200 B,  200 C), and generation program. 
     It is possible to facilitate high-accuracy and high-speed analysis of more cells contained in a sample. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIGS. 1A and 1B  show a method for generating training data for training a first artificial intelligence algorithm  50  for analyzing chromosomal abnormalities;  FIG. 1A  shows a method for generating positive training data;  FIG. 1B  shows a method for generating negative training data; 
         FIG. 2  shows a method of generating training data for training a first artificial intelligence algorithm  50  for analyzing chromosomal abnormalities; 
         FIG. 3  shows a method of generating analysis data for analyzing a chromosomal abnormality and a method of analyzing cells by a trained first artificial intelligence algorithm  60 ; 
         FIGS. 4A and 4B  show a staining pattern of PML-RARA chimera gene-positive cells by an imaging flow cytometer; The left of  FIG. 4A  shows an image of channel 2, and the right shows an image of channel 2;  FIG. 4B  is a cell different from  FIG. 4A , and the left shows an image of channel 2 and the right shows an image of channel 2; 
         FIG. 5  shows an example of a fluorescent label pattern; 
         FIG. 6  shows an example of a fluorescent label pattern; 
         FIG. 7  shows an example of a fluorescent label pattern; 
         FIGS. 8A and 8B  show a method of generating training data for training a first artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells; 
         FIG. 9A  shows a method for generating positive training data for training the first artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells;  FIG. 9B  shows a method for generating negative training data for training the first artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells; 
         FIG. 10  shows a method of generating training data for training the first artificial intelligence algorithm  53  for analyzing peripheral circulating tumor cells; 
         FIG. 11  shows a method of generating analysis data for analyzing peripheral circulating tumor cells and a method of analyzing cells by the trained first artificial intelligence algorithm  63 ; 
         FIG. 12A  shows a training data generation method for training a second artificial intelligence algorithm  94  for analyzing peripheral circulating tumor cells;  FIG. 12B  shows a method of generating analysis data and a method of analyzing cells by the second artificial intelligence algorithm  97 ; 
         FIG. 13  shows a feature quantity for training the second artificial intelligence algorithm  94 ; 
         FIGS. 14A, 14B, 14C, 14D and 14E  show a definition of a feature quantity for training the second artificial intelligence algorithm  94 ;  FIG. 14A  shows Height and Width;  FIG. 14B  shows Major Axis and Minor Axis;  FIG. 14C  shows Length, Thickness Max, and Thickness Min.  FIG. 14D  shows Aspect Ratio, Elongatedness, and Shape Ratio;  FIG. 14E  shows a Lobe Symmetry pattern; 
         FIG. 15  shows a hardware structure of the cell analysis system  1000 ; 
         FIG. 16  shows a hardware structure of training devices  200 A,  200 B, and  200 C; 
         FIG. 17  shows function blocks of the training device  200 A; 
         FIG. 18A  shows a flowchart of a training process of the first artificial intelligence algorithm;  FIG. 18B  shows a flowchart of the training process of a second artificial intelligence algorithm; 
         FIG. 19  shows a hardware structure of a cell imaging device  100 A and a cell analysis device  400 A; 
         FIG. 20  shows function blocks of the cell analysis device  400 A; 
         FIG. 21  shows a flowchart of cell analysis processing; 
         FIG. 22  shows a hardware structure of the cell analysis system  2000 ; 
         FIG. 23  shows function blocks of the training/analysis device  200 B; 
         FIG. 24  shows a hardware structure of a cell analysis system  3000 ; 
         FIG. 25  shows function blocks of training  200 C; 
         FIG. 26A  shows a data set for examining an artificial intelligence algorithm (CNN) for analyzing peripheral circulating tumor cells;  FIG. 26B  shows the correct answer rate of the trained artificial intelligence algorithm;  FIG. 26C  shows an example of a correct answer image; 
         FIG. 27  shows a data set for examining artificial intelligence algorithms (random forest, gradient boosting) for analyzing peripheral circulating tumor cells; 
         FIG. 28A  shows a CNN loss function for analyzing chromosomal abnormalities;  FIG. 28B  shows the correct answer rate of CNN for analyzing chromosomal abnormalities; 
         FIG. 29A  shows the inference result of sample number 04-785; 
         FIG. 29B  shows the inference result of sample number 03-352; and  FIG. 29C  shows the inference result of sample number 11-563. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Hereinafter, the summary and embodiments of the present invention will be described in detail with reference to the accompanying drawings. Note that in the following description and drawings, the same reference numeral denotes the same or similar component, and thus the description of the same or similar component may be omitted. 
     I. Cell Analysis Method 
     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, desirably 10 3 or more, preferably 10 4 or more, more preferably 10 5 or more, and ideally 10 6 or more cells. 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 part 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. 
     The chromosomal abnormality can be detected by a known method 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 algorithms  50  and  53  and the cell analysis method using the trained first artificial intelligence algorithms  60  and  63  will be described with reference to  FIGS. 1A and 1B to 11 . The first 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 method for training a first artificial intelligence algorithm  60  for detecting a chromosomal abnormality, and a cell analysis method using the 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. 1A, 1B and 2 . In  FIGS. 1A and 1B , 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. 1A and 1B , positive training data  73 P and negative 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 positive 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 positive training data  73 P and the negative 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. 1A and 1B , as shown in  FIG. 1A , 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. In order to extract the cell region, each image captured by the imaging unit  160  is trimmed, for example, to a predetermined number of pixels, for example, 100 pixels in the vertical direction and 100 pixels in the horizontal direction, 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. It can be exemplified that the trimming process determines the center of gravity of the cell and cuts out a region within a range of a predetermined number of pixels centered on the center of gravity. In the image of the cells flowing through the flow cell, the position of the cells in the image may differ between the images, but by trimming, more accurate training becomes possible. 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. 1A , 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. 1A , 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. 1A  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. 1B , 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. 1B , 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. 1B , 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. 1B  as a label indicating that it is the first negative control cell. 
       FIG. 2  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 
     A cell analysis method in which cells flowing through a flow cell  110  are imaged, integrated analysis data  82  are generated from the generated analysis image  80 , and a trained first artificial intelligence algorithm  60  is used will be described with reference to  FIG. 3 . 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. 3 , the cells flowing through the flow cell  110  are imaged by the imaging unit  160  to generate an analysis image  80 . By imaging the cells flowing through the flow cell  110 , a large number of analysis images  80  can be generated in a short time, and a large number of cells can be analyzed in a short time. Although the number of abnormal cells contained in a sample may be very small compared to the number of normal cells that should originally exist in the sample, according to the current analysis method, which enables analysis of a large number of cells in a short time, It is possible to suppress overlooking abnormal cells. 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 fluorescent label image  70 PA. As described above, in the image of the cells flowing through the flow cell, the positions of the cells in the image may differ between the images, but by trimming, more accurate analysis becomes possible. 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. 3 , 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. 3 , 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. 3 , 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. 3 , 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 i shown in  FIGS. 4A and 4B .  FIGS. 4A and 4B  show cells positive for the PML-RARA chimeric gene.  FIGS. 4A and 4B  are images of different cells. The images on the left side of  FIGS. 4A and 4B  show images of the first fluorescent label. The images on the right side of  FIGS. 4A and 4B  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 part 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 (B)CL1)/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, PDGFRIβ (5q32) translocation and the like can be detected. 
     Also, translocations can include various variations.  FIGS. 5 and 6  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. 6  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 part 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 part of the binding region of the probe targeting the ABL gene on chromosome 9 and a part 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. 7  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. 7  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. 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. 7 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 first artificial intelligence algorithm  63  for detecting peripheral circulating tumor cells and a method for analyzing cells using the first 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 first artificial intelligence algorithm  53  for detecting peripheral circulating tumor cells will be described with reference to  FIGS. 8A and 8B  to 10. 
       FIGS. 8A and 8B  show a preprocessing method for an image captured by the imaging unit  160 .  FIG. 8A  shows an 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 . In  FIG. 8A , (a) and (b) are images of the same cell, but the channels at the time of imaging are different. In  FIG. 8A , (c) and (a) are images of different cells. Although (c) and (d) are images of the same cell, the channels when imaging are different. As shown in (a) and (c) of  FIG. 8A , 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 crop the acquired image so as to reflect the size of the cells and have the same image size. In the example shown in  FIGS. 8A and 8B , 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. 8B .  FIG. 8B (a) is an image extracted from  FIG. 8A (a),  FIG. 8B (b) is an image extracted from  FIG. 8A (b),  FIG. 8B (c) is an image extracted from  FIG. 8A (c), and  FIG. 8B (d) is an image extracted from  FIG. 8A (d). Each image in  FIG. 8B  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 MarkIl, Luminex). 
       FIG. 9A ,  FIG. 9B  and  FIG. 10  show a training method for the first artificial intelligence algorithm  53 . 
     As shown in  FIG. 9A  and  FIG. 9B , 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 bright-field image can be an image of the phase difference of the cells. This imaging can be obtained, for example, on the 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. 
     The first channel (Ch1) indicates a bright-field image in  FIGS. 9A and 9B . In  FIGS. 9A and 9B , 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. 
     As shown in  FIG. 9A , 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. 9A , 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. 9A , 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. “2” is attached in  FIG. 9A  as a label indicating that it is a second 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. 9B , 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. 9B , 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. 9B , in the negative integrated training data  77 N become matrix data in which the numerical values in each pixel of 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. “1” is attached in  FIG. 9B  as a label indicating that it is a second negative control cell. 
       FIG. 10  shows a method of inputting the labeled positive integrated training data  78 P generated in the first 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 first artificial intelligence algorithm  53  is trained, and the trained first 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. 11 . The analysis image  85  can be captured and preprocessed in the same manner as the training image  75  was captured. 
     As shown in  FIG. 11 , 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. 11 , the cells flowing through the flow cell  110  were imaged according to the method for generating the positive integrated training data  77 P, and the generated first numerical analysis data  86 T 1  to the Xth numerical analysis data  86 Tx are used to generate the integrated analysis data  87  for each pixel. As shown in  FIG. 11 , the integrated analysis data  87  become matric 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. 11 , the cells flowing through the flow cell  11  are imaged by the imaging unit  160  to generate an analysis image  85 . By imaging the cells flowing through the flow cell  110 , a large number of analysis images  80  can be generated in a short time, and a large number of cells can be analyzed in a short time. Although the number of abnormal cells contained in a sample may be very small compared to the number of normal cells that should originally exist in the sample, according to the current analysis method, which enables analysis of a large number of cells in a short time, It is possible to suppress overlooking abnormal cells. The generated integrated analysis data  87  are input to the input layer  63   a  of the neural network in the trained first 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. To do. In the example shown in  FIG. 11 , “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 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 Method Using a Second Artificial Intelligence Algorithm 
     The training method of the second artificial intelligence algorithm  94  and the cell analysis method using the trained second artificial intelligence algorithm  97  will be described with reference to  FIGS. 12A, 12B and 13 . The second artificial intelligence algorithms  94  and  97  can be algorithms other than the deep learning algorithm having a neural network structure. The second artificial intelligence algorithm  94  extracts a user-defined feature amount from the above-mentioned second positive control cell or second negative control cell, and the extracted feature amount and the corresponding second positive control cell. Alternatively, it is trained using a label indicating the properties of the second negative control cell as training data. In addition, the trained second artificial intelligence algorithm  97  extracts the feature amount corresponding to the feature amount extracted when generating the training data from the analysis target image, and data showing the properties of cells are generated using the feature amount as analysis data. 
     In this embodiment, examples of the algorithms that can be used as the second artificial intelligence algorithms  94  and  97  include random forest, gradient boosting, support vector machine (SVM), relevance vector machine (RVM), naive bays, logistic regression, feed, Forward Neural Network, Deep Learning, K-Nearest Neighbor Method, AdaBoost, Bagging, C4.5, Kernel Approximation, Stochastic Gradient Descent (SGD) Classifier, Lasso, Ridge Regression, Elastic Net, SGD Regression, Kernel Regression, Lowess Regression, matrix fructization, non-negative matrix fructization, kernel matrix fructization, interpolation method, kernel smoother, co-filtering and the like. The second artificial intelligence algorithms  94 ,  97  are preferably random forest or gradient boosting. 
     As the second artificial intelligence algorithms  94  and  97 , for example, those provided by Python can be used. 
     Here, the term “train” or “training” may be used in place of the term “generate” or “generating”. 
     (1) Training Data Generation 
     As shown in  FIG. 12A , in the present embodiment positive training data  91 A and negative training data  91 B are generated from the positive training image  90 A obtained by imaging the second positive control cell used in section 2-2 above and the second negative control cell used in section 2-2 above. The positive training image  90 A and the negative training image  90 B may be collectively referred to as training image  90 . The positive training data  91 A and the negative training data  91 B also may be collectively referred to as training data  91 . 
     When detecting peripheral circulating tumor cells, the image captured by the imaging unit  160  (described later), which is used as the training image  90 , may be a bright-field image and/or a fluorescent image as in section 2-2 above. The bright-field image can be an image of the phase difference of the cells. The training image 90 can be acquired in the same manner as in section 2-2(1) above. 
     As the training data  91 , for example, the feature amount shown in  FIG. 13  can be exemplified. The features shown in  FIG. 13  can be classified into five categories. The categories include information about cell size (Size), information about cell location (Location), information about cell shape (Shape), information about cell texture (Texture), as well as light intensity (Signal strength) obtained from cell images. Details of the features included in each category are as shown in  FIG. 13 . These feature quantities can be used in combination of 1 or 2 or more. The feature amount preferably contains at least one piece of information selected from information about cell size. More preferably, it is desirable that the feature amount contains at least information on the area of the cells. These feature quantities can be determined using, for example, the above-mentioned analysis software (IDEAS). 
       FIGS. 14A-14E  show a description of typical features.  FIG. 14A  shows a description of Height and Width in  FIG. 13 . Height is, by way of example, intended to be the length of the long side (one side in the case of a square) of the smallest quadrangle (preferably a regular rectangle, or square) that can circumscribe the cell on the image. By way of example, the width is intended to be the length of the short side (one side in the case of a square) of the quadrangle.  FIG. 14B  shows a description of Major Axis (major axis) and Minor Axis (minor axis) in  FIG. 13 . The Major Axis is, for example, an ellipse (preferably a regular ellipse) that can surround the cell on the image, and the center of gravity of the ellipse overlaps with the center of gravity of the cell and it is intended that the long diameter of the smallest ellipse can surround the cell. Minor Axis is intended for the short diameter of the ellipse. The Minor Axis is, by way of example, intended to be the short diameter of the ellipse. 
       FIG. 14C  shows a description of cell length, thickness max (maximum thickness), and thickness min (minimum thickness). The length of the cell is different from the height shown in  FIG. 14A , and is intended to be the length of the longest line when assuming a line segment connecting one tip and the other tip of the cell on the image. The Thickness Max is intended to be the longest line segment, assuming an inner line segment that is orthogonal to the line segment representing the Length and is separated by the contour line of the cell. Thickness min is intended to be the shortest line segment length, assuming an inner line segment that is orthogonal to the line segment representing the Length and is separated by the contour line of the cell. 
       FIG. 14D  shows an explanation of Aspect Ratio, Elongatedness (elongation), and Shape Ratio. Aspect Ratio is the value obtained by dividing the length of Minor Axis by the length of Major Axis. Elongatedness is the value obtained by dividing the value of Height by the value of Width. The Shape Ratio is a value obtained by dividing the value of Thickness min by the value of Thickness Max. 
       FIG. 14E  shows a description of lobe symmetry (splitting).  FIG. 14E  shows an example of 2 lobe symmetry (2 lobes), 3 lobe symmetry (3 lobes), and 4 lobe symmetry (4 lobes). Splitting is one cell divided into lobes. 
     As shown in  FIG. 12A , the positive training data  91 A are combined with a label value of  93 A, for example, “2”, indicating that it is derived from the second positive control cell, and input to the second artificial intelligence algorithm  94  as labeled positive training data  92 A. Negative training data  91 B are combined with a label value  93 B, for example, “1”, indicating that it is derived the second negative control cell, and input to the second artificial intelligence algorithm  94  as labeled negative training data  92 B. The second artificial intelligence algorithm  94  is trained by the labeled positive training data  92 A and the labeled negative training data  92 B. 
     Although only the bright field image is shown in the example of  FIGS. 12A and 12B , when a plurality of fluorescent labels are imaged using a plurality of channels as shown in section  2 - 2  above, positive training data  91 A and negative training data  91 B are acquired for each channel, the respective labeled positive training data  92 A and labeled negative training data  92 B are generated and input to the second artificial intelligence algorithm  94 . 
     Here, the labeled positive training data  92 A and the labeled negative training data  92 B are also collectively referred to as training data  92 . 
     The training data  92  trains the second artificial intelligence algorithm  94 , and the trained second artificial intelligence algorithm  97  is generated. 
     (2) Analysis Data Generation and Cell Analysis 
       FIG. 12B  shows a cell analysis method in which analysis data  96  is generated from a third analysis image  95  of images of cells flowing through the flow cell  110  and a trained second artificial intelligence algorithm  97  is used. The trained second artificial intelligence algorithm  97  uses the analysis data  96  to generate data  98  indicating the properties of the cells to be analyzed. As shown in  FIG. 12B , the cells flowing through the flow cell  110  are imaged by the imaging unit  160  to generate a third analysis image  95 . The analysis data  96  can be generated from the third analysis image  95  captured in the same manner as the training image  90 . The analysis data  96  is preferably a feature amount corresponding to the third training data. 
     As data indicating normality or abnormality of cells, data  98  indicating whether the cells to be analyzed are peripheral circulating tumor cells are generated by inputting the analysis data  96  into the trained second artificial intelligence algorithm  97 . For example, “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 that the cell is a peripheral circulating tumor cell. Instead of the label value, labels such as “none”, “yes”, “normal”, and “abnormal” also may be output. 
     4. 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. 15 to 25 . In the following description, the first artificial intelligence algorithm  50 , the first artificial intelligence algorithm  53 , and the second artificial intelligence algorithm  94  may be referred to as “artificial intelligence algorithms” without distinction. 
     4-1. First Embodiment of a Cell Analysis System 
       FIG. 15  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  94 , 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. 
     4-1-1. Training Device 
     (1) Hardware Structure 
     The hardware structure of the training device  200 A will be described with reference to  FIG. 16 . The training device  200 A includes a control unit  20 A, an input unit  26 , an output unit  27 , and a media drive D 98 . 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 that assists in 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. 18 . The executable format is, for example, a format generated by being converted by a compiler from a programming language. 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 an algorithm database (DB)  205 . Step S 11  shown in  FIG. 18A  and step S 111  shown in  FIG. 18B  correspond to the training data generation unit  201 . Step S 12  shown in  FIG. 18A  and step S 112  shown in  FIG. 18B  correspond to the training data input unit  202 . Step S 14  shown in  FIG. 18A  corresponds to the algorithm update unit  203 . 
     The  75 Nx,  90 A and  90 B from the  75 Pxm  75 Nx and  75 N 1 , which are from the  70 PA,  70 PB,  70 NA,  70 NB and  75 P 1  are acquired beforehand from the cell imaging device  100 A by the cell analysis device  400 A, and prestored 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,  75 N 1  to  75 Nx,  90 A,  90 B from the cell analyzer  400 A via the network, or via the media drive D 98 . The training data database (DB)  204  stores the generated training data  73 ,  78 ,  92 . The pre-training artificial intelligence algorithm is pre-stored in the algorithm database  205 . The trained first artificial intelligence algorithm  60  can be recorded in the algorithm database  205  in association with the test items and analysis items for testing for chromosomal abnormalities. The trained first artificial intelligence algorithm  63  can be recorded in the algorithm database  205  in association with the test and analysis items for testing peripheral circulating tumor cells. The trained second artificial intelligence algorithm  97  can be recorded in the algorithm database  205  in association with the feature quantity item to be input. 
     (3) Training Process 
     The control unit  20 A of the training device  200 A performs the training process shown in  FIG. 18 . 
     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 stored in the storage unit  23  or the memory  22 ; then acquires the training  75 Nx or training images  90 A and  90 B from  75 Px and  75 N 1  from the training image  75 P 1 . Training images  70 PA,  70 PB,  70 NA,  70 NB are for training the first artificial intelligence algorithm  50 , training images  75 P 1  to  75 Px, and  75 N 1  to  75 Nx are for training the first artificial intelligence algorithm  53 , training images  90 A and  90 B are used to train the second artificial intelligence algorithm  94 . 
     i. First Artificial Intelligence Algorithm  50  Training Process 
     In step S 11  of  FIG. 18A , 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 73N is described in 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. 18A , 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. 18A , 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. First Artificial Intelligence Algorithm  53  Training Process 
     In step S 11  of  FIG. 18A , 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  78 P and the labeled negative integrated training data  78 N into the first artificial intelligence algorithm  53  in step S 12  of  FIG. 18A , and trains the first artificial intelligence algorithm  53 . The training result of the first 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. 18 , regarding the method for generating the labeled positive integrated training data  73 P and the labeled negative integrated training data  73 N is described in 2-1 above, the control unit  20 A predetermines whether the training results have been accumulated for the predetermined number of trials. 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 first artificial intelligence algorithm  53 . 
     Next, in step S 15 , the control unit  20 A determines whether the first 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 first artificial intelligence algorithm  63  in the storage unit  23 . 
     When the first 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. 
     iii. Second Artificial Intelligence Algorithm  94  Training Process 
     In step S 111  of  FIG. 18B , the control unit  20 A generates positive training data  91 A from the positive training image  90 A and generates negative training data  91 B from the negative training image  90 B. The control unit  20 A assigns a label value  93 P or a label value  93 N corresponding to each of the positive training data  91 A and the negative training data  91 B, and generates the labeled positive training data  92 A or the labeled negative training data  92 B. The labeled positive training data  92 A or the labeled negative training data  92 B are recorded in the storage unit  23  as training data  92 . The method of generating the labeled positive training data  92 A and the labeled negative training data  92 B is as described in section 3 above. 
     Next, the control unit  20 A inputs the generated labeled positive training data  92 A and labeled negative training data  92 B into the second artificial intelligence algorithm  94  in step S 112  of  FIG. 18B , and trains the second artificial intelligence algorithm  94 . 
     Next, in step S 113 , the control unit  20 A determines whether the second artificial intelligence algorithm  94  has been trained with a predetermined number of labeled positive training data  92 A and labeled negative training data  92 B. When training is performed with the specified number of labeled positive training data  92 A and labeled negative training data  92 B (in the case of “YES”), the training process is completed. The control unit  20 A stores the trained second artificial intelligence algorithm  97  in the storage unit  23 . 
     When the second artificial intelligence algorithm  94  is not trained with the specified number of labeled positive training data  92 A and the labeled negative training data  92 B (in the case of “NO”), the control unit  20 A advances from step S 113  to step S 114 , and performs the processes from step S 111  to step S 113  on the next positive training image  90 A and negative training image  90 B. 
     (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  or S 111  to S 114 . 
     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. 
     4-1-2. Cell Imaging Device 
       FIG. 19  shows the structure of a cell imaging device  100 A that captures the training images  70 ,  75 ,  90  and/or the analysis images  80 ,  85 ,  95 . The cell imaging device  100 A shown in  FIG. 19  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. 
     For example, 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 composed 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 bright-field 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 part 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 . 
     4-1-3. Cell Analysis Device 
     (1) Hardware Structure 
     The hardware structure of the cell analyzer  400 A will be described with reference to  FIG. 19 . 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 . he 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 ,  63 , and  94  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 D 98 . 
     The analysis images  80 ,  85 , and  95  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. 20  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 database (DB)  405 . Step S 21  shown in  FIG. 21  corresponds to the analysis data generation unit  401 . Step S 22  shown in  FIG. 21  corresponds to the analysis data input unit  402 . Step S 23  shown in  FIG. 21  corresponds to the analysis unit  403 . The analysis data database  404  stores analysis data  82 ,  88 ,  96 . 
     The trained first artificial intelligence algorithm  60  can be recorded in the algorithm database  405  in association with the exam items and analysis items for testing for chromosomal abnormalities. The trained first artificial intelligence algorithm  63  can be recorded in the algorithm database  405  in association with the exam and analysis items for testing peripheral circulating tumor cells. The trained second artificial intelligence algorithm  97  can be recorded in the algorithm database  405  in association with the feature quantity item to be input. 
     (3) Cell Analysis Process 
     The control unit  40 A of the cell analysis device  400 A performs the cell analysis process shown in  FIG. 21 . 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. 
     i. Cell Analysis Process by the First Artificial Intelligence Algorithm  60   
     The control unit  40 A generates integrated analysis data  82  from the analysis images  80 A and  80 B in step S 21  shown in  FIG. 21 . The method of generating the integrated analysis data  82  is as described in section 2-1 above. The control unit  40 A stores the generated integrated analysis data  82  in the storage unit  43  or the memory  42 . 
     In step S 22  shown in  FIG. 21 , the control unit  40 A calls the trained first artificial intelligence algorithm  60  stored in the storage unit  43  into the memory  42 , and inputs the integrated analysis data  82  generated in step S 21  to the first artificial intelligence algorithm  60 . 
     In step S 23  shown in  FIG. 21 , the control unit  40 A uses the first artificial intelligence algorithm  60  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 in the memory  42 . The determination method is as described in section 2-1 above. 
     The control unit  40 A determines whether all the analysis images  80 A and  80 B have been determined in step S 24  shown in  FIG. 21 , 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. 
     ii. Cell Analysis Process by the First Artificial Intelligence Algorithm  63   
     The control unit  40 A generates integrated analysis data  87  from the analysis images  85 T 1  to  85 Tx in step S 21  shown in  FIG. 21 . 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  87  in the storage unit  43  or the memory  42 . 
     In step S 22  shown in  FIG. 21 , the control unit  40 A calls the trained first artificial intelligence algorithm  63  stored in the storage unit  43  into the memory  42 , and inputs the integrated analysis data  87  generated in step S 21  to the first artificial intelligence algorithm  63 . 
     In step S 23  shown in  FIG. 21 , the control unit  40 A uses the first artificial intelligence algorithm  63  to determine the properties of the analysis target cells in the analysis images  85 T 1  to  85 Tx, and stores the label value  88  of the determination result in the storage unit  43  or in the memory  42 . The determination method is as described in section 2-2 above. 
     The control unit  40 A determines whether all the analysis images  85 T 1  to  85 Tx have been determined in step S 24  shown in  FIG. 21 , and when all the analysis images  85 T 1  to  85 Tx have been determined (in the case of “YES”), proceeds to step S 25 , and the determination result corresponding to the label value  88  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  85 T 1  to  85 Tx are not determined in step S 24  (in the case of “NO”), the control unit  40 A updates the analysis images  85 T 1  to  85 Tx in step S 26 , and step S 21  to step S 24  are repeated until the determinations are made for all the analysis images  85 T 1  to  85 Tx. 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. 
     iii. Cell Analysis Process by the Second Artificial Intelligence Algorithm  97   
     The control unit  40 A generates integrated analysis data  96  from the analysis image  95  in step S 21  shown in  FIG. 21 . The method of generating the integrated analysis data  96  is as described in section 3 above. The control unit  40 A stores the generated integrated analysis data  96  in the storage unit  43  or the memory  42 . 
     In step S 22  shown in  FIG. 21 , the control unit  40 A calls the trained second artificial intelligence algorithm  97  stored in the storage unit  43  into the memory  42 , and inputs the analysis data  96  generated in step S 21  into the second artificial intelligence algorithm  97 . 
     In step S 23  shown in  FIG. 21 , the control unit  40 A uses the second artificial intelligence algorithm  97  to determine the properties of the analysis target cells in the analysis image  95 , and stores the determination result in the storage unit  43  or in the memory  42 . The determination method is as described in section 3 above. 
     The control unit  40 A determines whether all the analysis images  95  have been determined in step S 24  shown in  FIG. 21 , and when all the analysis images  95  have been determined (in the case of “YES”), proceeds to step S 25 , and the label value  98  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  98  are not determined in step S 24  (in the case of “NO”), the control unit  40 A updates the analysis images  95  in step S 26 , and step S 21  to step S 24  are repeated until the determinations are made for all the analysis images  95 . 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 21  to S 26 . 
     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 is stored in a semiconductor memory element such as a hard disk or 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. 
     4-2. Second Embodiment of a Cell Analysis System 
     As shown in  FIG. 22 , the cell analysis system  2000  according to the second embodiment includes a cell imaging device  100 A and a training/analysis device  200 B that trains an artificial intelligence algorithm and analyzes cells. In the cell analysis system  1000  according to the first embodiment, training of an artificial intelligence algorithm and analysis of cells are performed by different computers. In the second embodiment, one computer trains an artificial intelligence algorithm and analyzes cells. The training/analysis device  200 B acquires training images  70 PA,  70 PB,  70 NA,  70 NB, and  75 P 1  to  75 Px,  75 N 1  to  75 Nx,  90 A,  90 B and analysis images  80 A,  80 B,  85 T 1  to  85 Tx, and  95  from the cell imaging device  100 A. 
     The hardware structure of the training/analysis device  200 B is the same as that of the cell analysis device  400 A shown in  FIG. 19 . The functions of the training/analysis device  200 B will be described with reference to  FIG. 23 . The training/analysis device  200 B includes a training data generation unit  201 , a training data input unit  202 , an algorithm update unit  203 , an analysis data generation unit  401 , an analysis data input unit  402 , an analysis unit  403 , a training data database (DB)  204 , and an algorithm database (DB)  205 . Each function structure is basically the same as the structure shown in  FIGS. 17 and 20 , but in the present embodiment, the training data  73 ,  78 ,  92  and the analysis data  82 ,  88 ,  96  are stored in the training data database (DB)  204 . Step S 11  shown in  FIG. 18A  and step S 111  shown in  FIG. 18B  correspond to the training data generation unit  201 . Step S 12  shown in  FIG. 18A  and step S 112  shown in  FIG. 18B  correspond to the training data input unit  202 . Step S 14  shown in  FIG. 18A  corresponds to the algorithm update unit  203 . Step S 21  shown in  FIG. 21  corresponds to the analysis data generation unit  401 . Step S 22  shown in  FIG. 21  corresponds to the analysis data input unit  402 . Step S 23  shown in  FIG. 21  corresponds to the analysis unit  403 . 
     The training process and the cell analysis process are described in section 4-1 above which is incorporated herein by reference. However, various data generated in the process are stored in the storage unit  23  or the memory  22  of the training/analysis device  200 B. 
     4-3. Third Embodiment of a Cell Analysis System 
     As shown in  FIG. 24 , the cell analysis system  3000  according to the third embodiment includes a cell imaging device  100 B, a training device  200 C that trains an artificial intelligence algorithm and analyzes cells, a cell imaging device  100 A, and an image acquisition device  400 B that acquires images from the cell imaging device  100 A. In the cell analysis system  1000  according to the first embodiment, training of an artificial intelligence algorithm and analysis of cells are performed by different computers. In the third embodiment, the training device  200 C is an example of a device for training an artificial intelligence algorithm and analyzing cells. The training device  200 C acquires training images  70 PA,  70 PB,  70 NA,  70 NB,  75 P 1  to  75 Px,  75 N 1  to  75 Nx,  90 A and  90 B from the cell imaging device  100 B, and analyzes images  80 A,  80 B,  85 T 1  to  85 Tx, and  95  acquired from the image acquisition device  400 B. 
     The hardware structure of the training device  200 C and the image acquisition device  400 B is the same as that of the cell analysis device  400 A shown in  FIG. 19 . The functions of the training device  200 C will be described with reference to  FIG. 25 . The function structure of the training device  200 C is the same as that of the training/analysis device  200 B shown in  FIG. 23 , and the training data generation unit  201 ; the training data input unit  202 , the algorithm update unit  203 , the analysis data generation unit  401 , the analysis data input unit  402 , an analysis unit  403 , a training data database (DB)  204 , and an algorithm database (DB)  205  are included. Each function structure is basically the same as the structure shown in  FIGS. 17 and 20 , but in the present embodiment, the training data  73 ,  78 ,  92  and the analysis data  82 ,  88 ,  96  are stored in the training data database (DB)  204 . Step S 11  shown in  FIG. 18A  and step S 111  shown in  FIG. 18B  correspond to the training data generation unit  201 . Step S 12  shown in  FIG. 18A  and step S 112  shown in  FIG. 18B  correspond to the training data input unit  202 . Step S 14  shown in  FIG. 18A  corresponds to the algorithm update unit  203 . Step S 21  shown in  FIG. 21  corresponds to the analysis data generation unit  401 . Step S 22  shown in  FIG. 21  corresponds to the analysis data input unit  402 . Step S 23  shown in  FIG. 21  corresponds to the analysis unit  403 . 
     The training process and the cell analysis process are described in section 4-1 above which is incorporated herein by reference. However, various data generated in the process are stored in the storage unit  23  or the memory  22  of the training  200 C. 
     5. Other 
     The present invention shall not be construed as being limited to the embodiments described above. 
     For example, although a plurality of different images of the same cell in the same field are used in the generation of training data and analysis data in the above-described embodiment, one training datum may be generated from one cell image, and one analysis datum may be generated from one cell image. 
     Although analysis data are generated from a plurality of images obtained by capturing images of different light wavelength regions of the same field of view of one cell in the above-described embodiment, one cell may be imaged multiple times to obtain a plurality of images by another method. For example, analysis data may be generated from a plurality of images obtained by imaging one cell from different angles, or analysis data may be generated from a plurality of images obtained by imaging with staggered timing the th cell of one label value  84 . 
     In the above-described embodiment, the normality or abnormality of the cell is determined, but the cell type and the cell morphology also may be determined. 
     EXAMPLES 
     Examples will be used to describe embodiments in more detail. However, the present invention shall not be construed as being limited to the examples. 
     I. Detection of Peripheral Circulating Tumor Cells 
     1. Data Acquisition Method 
     Breast cancer cell line MCF7 and peripheral blood mononuclear cells PBMC (Peripheral Blood Mononuclear Cells) were used as model samples of CTC and blood cells. The cells were stained with Hoechst reagent and then subjected to an imaging flow cytometer (ImageStream Mark II, Luminex) to obtain bright-field images and nuclear-stained images. The conditions of the imaging flow cytometer were magnification: 40 times, flow velocity: Medium, and EDF filter. 
     2. Analysis 
     2-1. Analysis Example by Deep Learning Algorithm 
     (1) Artificial Intelligence Algorithm 
     Python 3.7.3, TensorFlow 2.0 alpha Keras was used as the language and library. A convolutional neural network (CNN) was used as an artificial intelligence algorithm. 
     (2) Data Set 
     Details of the data set are shown in  FIG. 26A . Note that since two images, that is, a bright-field image and a nuclear-stained image, were used for each cell, twice the number of images was used for the analysis. The image size was trimmed to 32×32 pixels. At that time, the cells were extracted so that the center of gravity of the cell nucleus became the center of the image. Training data and analysis data were generated according to the method for analyzing peripheral circulating tumor cells using the first artificial intelligence algorithm  63  described in the text of the specification. 
     (3) Results 
     Two classes, MCF7 and PBMC, were discriminated. First, a discriminant model was created using the training data set.  FIG. 26B  shows the relationship between the number of epochs (number of leanings) and accuracy (correct answer rate). With less than 10 epochs, the correct answer rate reached almost 100%. The correct answer rate was examined using the model with the 50th epoch as the discrimination model. When the model with the 50th epoch number was used, the correct answer rate of the training data set was 99.19%, and the correct answer rate of the verification data set was 99.10%, which were very good results. 
       FIG. 26C  shows an example of the correct answer. Nuc indicates nuclear staining and BF indicates phase difference images. 
     2-2. Analysis Example by Machine Learning Other Than Deep Learning 
     (1) Artificial Intelligence Algorithm 
     Python 3.7.3, scikit-learn was used as the language and library. Random forest and gradient boosting were used as artificial intelligence algorithms. 
     (2) Data Set 
     For each of the bright-field image and the nuclear-stained image of the data set shown in  FIG. 26A , 70 types of feature quantities (bright-field image) shown in  FIG. 13  were used using the analysis software (IDEAS) attached to the imaging flow cytometer, and a data set was generated that defined the feature amount (feature amount: 140 types by combining the images and nuclear staining images). 
     (3) Result 
     Two classes, MCF7 and PBMC, were discriminated. A discriminant model was created by random forest and gradient boosting using the above dataset. The correct answer rate when each model is used is shown in  FIG. 27 . The correct answer rate was 99.9% or higher, and the correct answer rate was very good for both random forest and gradient boosting. 
     II. Detection of Chromosomally Abnormal Cells 
     1. Examination 1 
     (1) Artificial Intelligence Algorithm 
     Python 3.7.3, TensorFlow 2.0 alpha was used as the language and library. A convolutional neural network (CNN) was used as an artificial intelligence algorithm. The training was conducted up to 50 times. 
     (2) Data Acquisition Method 
     PML-RARA chimeric gene-positive cells were subjected to an imaging flow cytometer MI-1000 to acquire images of channel 2 (green) and channel 4 (red). The image was taken with a magnification of 60 times and an EDF filter. 
     Negative integrated training data were generated according to the analysis method of chromosomally abnormal cells using the first artificial intelligence algorithm  60  described in the text of the specification from the image set of channel 2 (green) and channel 4 (red) of negative control cells determined to be free of chromosomal aberrations (G2R2F0) by known methods. The negative integrated training data was labeled with a “nega label” indicating that the chromosomal abnormality was negative, and labeled negative integrated training data were generated. Similarly, positive integrated training data were generated from channel 2 and channel 4 image sets of positive control cells determined to have chromosomal abnormalities (G3R3F2) by known methods. The positive integrated training data were labeled with a “posi label” indicating that the chromosomal abnormality was positive, and labeled positive integrated training data were generated. Here, for example, “G” and “R” of G2R2F0 mean a channel number, and “F” means a fusion signal. The numbers indicate the number of signals in one cell. 
     3741 sets of labeled negative integrated training data and 2052 sets of labeled positive integrated training data were prepared, and 3475 sets, which is 60% of these, were used as training data. In addition, 1737 sets, which is 30%, were used as test data, and 581 sets, which was 10%, were used as validation data. 
     (3) Result 
     The correct answer rate was 100%. In addition,  FIG. 28A  shows the change in the loss rate as the number of epochs increases. A decrease in the loss rate was observed as the number of epochs increased. In addition,  FIG. 28B  shows the change in the correct answer rate as the number of epochs increases. As the number of epochs increased, the percentage of correct answers improved. 
     2. Examination 2 
     (1) Artificial Intelligence Algorithm 
     Python 3.7.3, TensorFlow 2.0 alpha was used as the language and library. A convolutional neural network (CNN) was used as an artificial intelligence algorithm. The training was conducted up to 100 times. 
     (2) Data 
     Three PML-RARA chimeric gene-positive samples (sample IDs: 03-532, 04-785, 11-563) were used for the imaging flow cytometer MI-1000 to acquire images of channel 2 (green) and channel 4 (red). The image was taken with a magnification of 60 times and an EDF filter. Negative integrated training data were generated from channel 2 and channel 4 image sets of cells determined to be free of chromosomal abnormalities (G2R2F0) by known methods, according to the method described in the text of the specification. The negative integrated training data was labeled with a “nega label” indicating that the chromosomal abnormality was negative, and labeled negative integrated training data were generated. Similarly, positive integrated training data were generated from channel 2 and channel 4 image sets of cells determined to have chromosomal abnormalities (G3R3F2) by known methods. The positive integrated training data were labeled with a “posi label” indicating that the chromosomal abnormality was positive, and labeled positive integrated training data were generated. Here, for example, “G” and “R” of G2R2F0 mean a channel number, and “F” means a fusion signal. The numbers indicate the number of signals in one cell. 
     Using the images of these samples, we attempted to detect the PML-RARA chimeric gene by a deep learning algorithm. The number of training data was 20537 and the number of analysis data was 5867. 
     (3) Result 
     The determination results for each sample are shown in  FIGS. 29A to 29C .  FIG. 29A  shows the inference result of sample number 04-785,  FIG. 29B  shows the inference result of sample number 03-352, and  FIG. 29C  shows the inference result of sample number 11-563. As for the inference result, 92% of all the analysis data were correctly determined to be positive or negative. In addition, the correct answer rate for each sample was about 90%, and no bias was observed. Furthermore, the rate of false positives or false negatives was 3 to 6%, showing no bias. From this result, it was considered that a model without bias for each sample and positive or negative bias could be generated.