Sysmex Corporation

Juntendo Educational Foundation

Konobu Kimura

Akimichi Ohsaka

Yoko Tabe

Takaaki Nagai

Yuji Masuda

Masaharu Shibata

Noriyuki Nakanishi

Takeshi Suzuki

Kinya Uchihashi

Toshihiro Mizukami

Shoichiro Asada

Osamu Kumano

Masamichi Tanaka

Koji Tsuchiya

Yuhgi Suzuki

Kenichiro Suzuki

Jo Linssen

Ken Nishikawa

Yuji Toya

Takahito Mihara

Munehisa Izuka

Masato Kuze

Hironori Tanaka

Kota Misawa

Yuuichi Hamada

Yuka Tabuchi

Jarob Saker

Hikaru Onoue

Yukiko Nakamura

Yuki Shida

Masaki Abe

Keisuke Nishi

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.

Disease differentiation support method, disease differentiation support apparatus, and disease differentiation support computer program

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.

Analysis method and analyzer

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.

Measurement apparatus and analysis method

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.

Image analysis method, apparatus, non-transitory computer readable medium, and deep learning algorithm generation method

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.

Measurement method, measuring device, and measurement program

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.

Method, apparatus, and computer program for supporting disease analysis, and method, apparatus, and program for training computer algorithm

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.

Cell classification method, classification device, and program

Disclosed is a particle analysis method for identifying infections, comprising:

Particle analysis method for identifying infections

A blood analyzer comprises a sample preparing part, a light source, a light receiving part, and a processing part configured to discriminate and count reactive B lymphocytes based on at least one of fluorescent light signals and forward scattered light signals.

Blood analyzer, blood analyzing method, and non-transitory storage medium

Disclosed is a blood analyzer including a specimen preparation unit that prepares a measurement specimen by mixing a hemolytic agent that hemolyzes red blood cells, a staining dye that dyes nucleic acids, and a blood specimen; a detector that detects intensity of side scattered light and intensity of fluorescence generated with application of light from the measurement specimen prepared by the specimen preparation unit; and an analysis unit that discriminates white blood cells from giant platelets based on the intensity of side scattered light and the intensity of fluorescence detected by the detector, and counts the white blood cells.

Blood analyzer, blood analyzing method, and non-transitory computer-readable storage medium

Disclosed is a cell analyzer that includes a flow cell through which a measurement specimen containing a body fluid flows, a light emission unit that applies light onto the measurement specimen flowing through the flow cell, a light detection unit that detects forward scattered light generated from cells in the measurement specimen to which the light is applied, an analysis unit that is programmed to analyze the cells in the body fluid based on a forward scattered light signal detected by the light detection unit, and an output unit. The analysis unit is programmed to control the output unit to output information about tumor cells in the body fluid, based on forward scattered light signal intensity and forward scattered light signal width.

Cell analyzer and cell analyzing method

A blood analyzer comprises a sample preparing part configured to prepare a measurement sample from a blood sample, an staining dye and diluent, a detecting part configured to detect the fluorescent light intensity and the scattered light intensity, an output part, and an analyzing part configured to identify the population including red blood cells infected by ring-form malaria parasite based on the fluorescent light intensity and the scattered light intensity, and output to the output part the information relating to infection ofbased on the scattered light distribution of particles associated with the identified population that includes red blood cells infected by the ring-form malaria parasite.

Blood analyzer and blood analyzing method

A method for analyzing blood cells includes: preparing a measurement specimen for classifying white blood cells, by mixing a sample with a reagent in order to hemolyze red blood cells contained in the sample and to fluorescently stain nucleic acid in white blood cells contained in the sample; detecting, by flowing the prepared measurement specimen in a flow cell, fluorescence emitted from each blood cell in the measurement specimen and two types of scattered light at respective different angles, and obtaining a fluorescence signal and two types of scattered light signals; and classifying the white blood cells into at least four groups and detecting neoplastic lymphocytes, by performing analysis using at least three types of parameters based on the obtained fluorescence signal and two types of scattered light signals.

Growing community of inventors

Kobe, Japan

Konobu Kimura