Abstract: Disclosed is a method for supporting disease analysis, the method including classifying, on the basis of images obtained from a plurality of analysis target cells contained in a specimen collected from a subject, a morphology of each analysis target cell, and obtaining cell morphology classification information corresponding to the specimen, on the basis of a result of the classification; and analyzing a disease of the subject by means of a computer algorithm, on the basis of the cell morphology classification information.

Abstract: Disclosed is a cell classification method, to be executed by an analyzer, for classifying cells contained in a specimen, including: preparing a first measurement sample by treating a specimen under a first preparation condition; obtaining a first signal from the prepared first measurement sample; classifying, by using the first signal, cells contained in the first measurement sample; preparing a second measurement sample by treating the specimen under a second preparation condition different from the first preparation condition; obtaining a second signal from the prepared second measurement sample; classifying, by using the second signal, cells contained in the second measurement sample; and comparing a result of the cell classification performed by using the first signal and a result of the cell classification performed by using the second signal, with each other, and outputting an analysis result including a number of cells on the basis of a result of the comparison.

Abstract: Disclosed is an image analysis method including inputting analysis data, including information regarding an analysis target cell to a deep learning algorithm having a neural network structure, and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.

Abstract: Disclosed is an image analysis method including inputting analysis data, including information regarding an analysis target cell to a deep learning algorithm having a neural network structure, and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.

Abstract: Disclosed is a specimen analyzer configured to analyze an analyte in a specimen, the specimen analyzer including: a measurement unit including an optical detection part configured to obtain an optical signal from the specimen; and an analysis unit configured to analyze first data and second data that correspond to the optical signal, wherein the analysis unit executes, on the first data, a first analysis operation according to an artificial intelligence algorithm, and executes a second analysis operation of processing a representative value, of the second data, that corresponds to a feature of the analyte.

Abstract: Disclosed is a specimen analyzer for analyzing an analyte in a specimen, the specimen analyzer including: a measurement unit including a plurality of first sample preparation parts each configured to prepare a first measurement sample on the basis of the specimen and a first reagent, a second sample preparation part configured to prepare a second measurement sample on the basis of the specimen and a second reagent, and an optical detection part configured to obtain a first optical signal from the first measurement sample and obtain a second optical signal from the second measurement sample; and an analysis unit configured to analyze first data that corresponds to the first optical signal and second data that corresponds to the second optical signal, wherein the analysis unit executes analysis of a first measurement item with respect to the first measurement sample, through a first analysis operation of processing the first data according to an artificial intelligence algorithm, executes analysis of a second m

Abstract: Provided is a cell analysis method and a cell analyzer that, in a configuration in which data obtained from a plurality of cells contained in a specimen is analyzed, can process, at a requested throughput, data of cells having a significantly increased information amount. The cell analysis method, using a cell analyzer that comprises a host processor and a parallel-processing processor, includes: obtaining, on the basis of control by the host processor, data regarding each of a plurality of cells in a specimen; executing, by the parallel-processing processor, parallel processing regarding the data; and generating, on the basis of a result of the parallel processing, information regarding a cell type with respect to each of the plurality of cells.

Abstract: Disclosed is a measurement apparatus for analyzing a cell contained in a specimen, comprising: a chamber for preparing a measurement sample in which the cell is stained with first and second fluorescent dyes contained in a reagent supplied from at least one reagent container; a liquid feeding section for feeding the reagent from the reagent container to the chamber via a liquid feeding tube provided between the reagent container and the chamber; and a detection section that acquires first and second signals each corresponding to fluorescence of a first wavelength and fluorescence of a second wavelength emitted from the cell stained with the first and second fluorescent dyes in response to irradiation of the measurement sample flowing in a flow cell with light; and an analysis section that analyzes the cell on the basis of the first and second signals.

Abstract: In a configuration for analyzing data of cells measured by a cell measuring apparatus, accuracy of cell classification is improved without requiring the cell measuring apparatus to have high information processing capability. A cell analysis method, using a cell analyzer for analyzing cells in accordance with an artificial intelligence algorithm, includes: obtaining the data regarding the cells measured by the cell measuring apparatus; analyzing the data to generate information regarding a cell type of each of the cells; and transmitting the information to the cell measuring apparatus.

Abstract: Disclosed is a disease differentiation support method for supporting disease differentiation, the disease differentiation support method including: obtaining a first parameter obtained by analyzing an image including a cell contained in a sample collected from a subject; obtaining a second parameter regarding a number of cells contained in the sample; and generating, by using a computer algorithm, differentiation support information for supporting disease differentiation, on the basis of the first parameter and the second parameter.

Abstract: Disclosed is a detection method for detecting occurrence of nonspecific reaction in analysis for an antigen or an antibody contained in a biological sample with use of a measurement reagent containing an antibody or an antigen that causes antigen-antibody reaction with the antigen or the antibody in the biological sample, and the detection method includes: generating a data group about progress of antigen-antibody reaction between the antigen or the antibody contained in the biological sample and the antibody or the antigen contained in the measurement reagent; inputting the data group to a deep learning algorithm; and generating information about occurrence of nonspecific reaction, based on a result outputted by the deep learning algorithm.

Abstract: Disclosed is an analysis method for analyzing a specimen containing cells, the analysis method including: applying light to a measurement sample prepared from the specimen and detecting light generated from cells; obtaining, with respect to each of a plurality of cells contained in the specimen, feature data of the cell on the basis of the detected light; analyzing the feature data with use of an artificial intelligence algorithm, thereby classifying each of the cells into a plurality of cell types; and generating result data including a result of the classifying of each of the cells into the plurality of cell types.

Abstract: Disclosed is an analysis method for analyzing a specimen containing cells, the analysis method including: applying light to a measurement sample prepared from the specimen and detecting light generated from cells; obtaining, with respect to each of a plurality of cells contained in the specimen, feature data of the cell on the basis of the detected light; analyzing the feature data with use of an artificial intelligence algorithm, thereby classifying each of the cells into a plurality of cell types; and displaying information based on a result of the classifying.

Abstract: Disclosed is an analysis method for a specimen using an analyzer connected to a host computer, the analysis method including: obtaining, with respect to each of a plurality of cells contained in the specimen, feature data of the cell; generating classification information in which each of the cells is classified into a plurality of cell types, by analyzing the feature data with use of an artificial intelligence algorithm and performing classifying; generating a measurement result of the specimen on the basis of the classification information; displaying, on a display part of the analyzer, the measurement result and at least a part of the classification information; and transmitting, to the host computer, output data that includes the measurement result and in which at least a part of the classification information has been removed.

Abstract: Disclosed is an analysis method for a blood specimen, including: obtaining a data group including a plurality of data forming a blood coagulation curve or a differential curve thereof; inputting the data group into a deep learning algorithm; and outputting, on the basis of a result obtained from the deep learning algorithm, information regarding a cause of prolongation of blood coagulation time of the blood specimen.

Abstract: A measurement method, a measurement device, and a measurement program for acquiring information related to lipid particles contained in a measurement sample prepared without using a fluorescent dye are provided. The problem is resolved by the measurement method for measuring the number of particles in a measurement sample prepared without using a fluorescent dye, the method including obtaining information related to lipid particles contained in the measurement sample based on a plurality of characteristic values regarding light scattering in each particle obtained by a flow cytometer from the individual particles contained in the measurement sample.

Abstract: The types of cells that cannot be determined by use of a conventional scattergram are determined. The problem is solved by a cell analysis method for analyzing cells contained in a biological sample, by using a deep learning algorithm having a neural network structure, the cell analysis method including: causing the cells to flow in a flow path; obtaining a signal strength of a signal regarding each of the individual cells passing through the flow path, and inputting, into the deep learning algorithm, numerical data corresponding to the obtained signal strength regarding each of the individual cells; and on the basis of a result outputted from the deep learning algorithm, determining, for each cell, a type of the cell for which the signal strength has been obtained.

Abstract: Disclosed is an image analysis method including inputting analysis data, including information regarding an analysis target cell to a deep learning algorithm having a neural network structure, and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.

Abstract: Disclosed is a cell classification method, to be executed by an analyzer, for classifying cells contained in a specimen, including: preparing a first measurement sample by treating a specimen under a first preparation condition; obtaining a first signal from the prepared first measurement sample; classifying, by using the first signal, cells contained in the first measurement sample; preparing a second measurement sample by treating the specimen under a second preparation condition different from the first preparation condition; obtaining a second signal from the prepared second measurement sample; classifying, by using the second signal, cells contained in the second measurement sample; and comparing a result of the cell classification performed by using the first signal and a result of the cell classification performed by using the second signal, with each other, and outputting an analysis result including a number of cells on the basis of a result of the comparison.

Abstract: Disclosed is an image analysis method including inputting analysis data, including information regarding an analysis target cell to a deep learning algorithm having a neural network structure, and analyzing an image by calculating, by use of the deep learning algorithm, a probability that the analysis target cell belongs to each of morphology classifications of a plurality of cells belonging to a predetermined cell group.