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1. WO2020196074 - CELL ANALYSIS METHOD, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS DEVICE, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS PROGRAM, AND TRAINING PROGRAM FOR DEEP LEARNING ALGORITHM

Publication Number WO/2020/196074
Publication Date 01.10.2020
International Application No. PCT/JP2020/011596
International Filing Date 17.03.2020
IPC
G01N 33/48 2006.01
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
C12M 1/34 2006.01
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
MAPPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY
1Apparatus for enzymology or microbiology
34Measuring or testing with condition measuring or sensing means, e.g. colony counters
C12Q 1/04 2006.01
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
02involving viable microorganisms
04Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
G01N 33/49 2006.01
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
483Physical analysis of biological material
487of liquid biological material
49blood
Applicants
  • シスメックス株式会社 SYSMEX CORPORATION [JP]/[JP]
Inventors
  • 木村 考伸 KIMURA Konobu
  • 田中 政道 TANAKA Masamichi
  • 朝田 祥一郎 ASADA Shoichiro
Agents
  • 大坂 雅浩 OSAKA Masahiro
Priority Data
2019-05538522.03.2019JP
Publication Language Japanese (JA)
Filing Language Japanese (JA)
Designated States
Title
(EN) CELL ANALYSIS METHOD, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS DEVICE, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS PROGRAM, AND TRAINING PROGRAM FOR DEEP LEARNING ALGORITHM
(FR) PROCÉDÉ D'ANALYSE DE CELLULE, PROCÉDÉ D'APPRENTISSAGE POUR ALGORITHME D'APPRENTISSAGE PROFOND, DISPOSITIF D'ANALYSE DE CELLULE, PROCÉDÉ D'APPRENTISSAGE POUR ALGORITHME D'APPRENTISSAGE PROFOND, PROGRAMME D'ANALYSE DE CELLULE ET PROGRAMME D'APPRENTISSAGE POUR ALGORITHME D'APPRENTISSAGE PROFOND
(JA) 細胞の分析方法、深層学習アルゴリズムの訓練方法、細胞分析装置、深層学習アルゴリズムの訓練装置、細胞の分析プログラム及び深層学習アルゴリズムの訓練プログラム
Abstract
(EN)
The present invention determines a cell type that has not been able to be determined in a conventional scattergram. The present invention addresses an issue by means of a cell analysis method, which analyzes a cell included in a biological sample by using a deep learning algorithm of a neural network structure, the method including: flowing the cell to a passage; acquiring a signal strength pertaining to each cell passing in the passage; inputting, to a deep learning algorithm, numerical data corresponding to the acquired signal strength pertaining to each cell; and determining, on the basis of an output result from the deep learning algorithm for each cell, the type of the cell for which the signal strength is acquired for each cell.
(FR)
La présente invention détermine un type de cellule n'ayant pas pu être déterminé dans un diagramme de dispersion classique. La présente invention traite un problème au moyen d'un procédé d'analyse cellulaire, qui analyse une cellule comprise dans un échantillon biologique en utilisant un algorithme d'apprentissage profond d'une structure de réseau neuronal, le procédé consistant à : faire en sorte que la cellule s'écoule dans un passage ; acquérir une intensité de signal liée à chaque cellule traversant le passage ; appliquer à l'entrée d'un algorithme d'apprentissage profond, des données numériques correspondant à l'intensité de signal acquise liée à chaque cellule ; et déterminer, sur la base d'un résultat de sortie provenant de l'algorithme d'apprentissage profond pour chaque cellule, le type de la cellule associé à l'intensité du signal acquise pour chaque cellule.
(JA)
従来のスキャッタグラムでは判定できなかった細胞の種別を判定する。 生体試料に含まれる細胞をニューラルネットワーク構造の深層学習アルゴリズムを用いて分析する細胞分析方法であって、流路に、前記細胞を流し、前記流路内を通過する個々の細胞に関する信号強度を取得し、取得された個々の細胞に関する信号強度に対応する数値データを深層学習アルゴリズムに入力し、深層学習アルゴリズムから出力された結果に基づいて、信号強度を取得した細胞の種別を細胞毎に判定する、ことを含む、前記細胞分析方法により、課題を解決する。
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