CN113380318B - Artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method and system - Google Patents
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Abstract
The invention provides an artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method, which comprises the following steps: step S1, extracting flow cytometry data in a case; s2, removing cell fragments except effective cells from the flow cytometry data to obtain effective flow cytometry data; s3, performing weighted combination on the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture model to establish a multidimensional Gaussian model; dividing the cell data into different cell groups according to the probability distribution of the effective flow cytometry data obtained in the step S2 on the multidimensional Gaussian model; s4, performing density detection on each cell population, and judging the cell population type; step S5, performing cross-tube matching on the cell population according to the determined cell population category to obtain the number, the proportion and the phenotype of various cells; and S6, judging the abnormal cell type according to the number, the proportion and the phenotype of each cell obtained in the step S5.
Description
Technical Field
The invention relates to the technical field of flow cytometry and software, in particular to a method and a system for detecting an artificial intelligence assisted flow cytometry 40CD immunophenotype.
Background
In the clinical screening of acute leukemia, flow cytometry is an important examination means, and can rapidly and accurately carry out multi-parameter quantitative analysis and sorting on internalization characteristics of single cells. At present, flow detection analysis mainly uses a manual gating analysis technology, which refers to selecting a specific cell population to be analyzed on a two-dimensional scatter diagram with a certain selected parameter according to the cell population distribution of the diagram. The doctor analyzes whether the case has cells with abnormal phenotype with acute leukemia, and diagnoses the case.
The traditional flow detection analysis method has the following disadvantages:
1. the need to spend a great deal of manpower classifying the flow data increases the labor cost of the hospital.
2. The professional quality of the analysts is high, the time for culturing is long, and particularly doctors with the report reviewing and sending capacity are not available or are few in number in general hospitals.
3. Manual gating may affect the cell classification result by subjective factors of the operator, resulting in erroneous final diagnosis results.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide an artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method.
In order to achieve the above object, an embodiment of the present invention provides an artificial intelligence assisted flow cytometry 40CD immunophenotyping method, which comprises the following steps:
s1, extracting flow cytometry data in a case;
s2, removing cell fragments except effective cells from the flow cytometry data to obtain effective flow cytometry data;
s3, performing weighted combination on the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture model to establish a multidimensional Gaussian model; dividing the cell data into different cell groups according to the probability distribution of the effective flow cytometry data obtained in the step S2 on the multidimensional Gaussian model;
s4, performing density detection on each cell population, and judging the cell population type;
s5, performing cross-tube matching on the cell population according to the determined cell population category to obtain the number, proportion and phenotype of various cells;
and S6, judging the abnormal cell type according to the number, the proportion and the phenotype of various cells obtained in the step S5.
Further, the file in FCS format or LM0 format is parsed to extract flow cytometric data in the case.
Further, in the step S2, two-dimensional nuclear density analysis is performed on all the cells on FSC-A/SSC-A, se:Sub>A cell group with se:Sub>A high density is obtained as an effective cell group according to the analysis result, and then cell debris except the effective cells is artificially removed.
Further, in step S4, analyzing the density distribution of all valid flow cytometry data on different fluorescent markers by using one-dimensional nuclear density estimation to obtain the negative-positive boundary of the cell on the fluorescent marker; the phenotype of each population of cells on the different antibodies was confirmed by means of an internal control, and the classification of each population of cells was confirmed.
Further, in step S5, after obtaining the specific population number of each kind of cell, finding the boundary of the cell according to the distribution on SSC-A/CD 45; cross-tube matching is achieved by known boundaries of cells to detect the number, proportion, and phenotype of cells on fluorescent markers.
The invention also provides an artificial intelligence assisted flow cytometry 40CD immunophenotype detection system, which comprises:
the cell data extraction module is used for extracting flow cell data in a case;
the cell debris removing module is used for removing cell debris except effective cells from the flow cytometry data to obtain effective flow cytometry data;
the clustering module is used for carrying out weighted combination on the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture model to establish a multidimensional Gaussian model; dividing the cell data into different cell groups according to the probability distribution of the effective flow cytometry data on the multidimensional Gaussian model;
the density detection module is used for performing density detection on each cell population and judging the cell population type;
the cross-tube matching module is used for performing cross-tube matching on the cell populations according to the determined cell population types so as to obtain the number, proportion and phenotype of various cells;
and the cell abnormal type judging module is used for judging the cell abnormal type according to the obtained number, proportion and phenotype of various cells.
Further, the cell data extraction module analyzes the file in the FCS format or the LM0 format to extract the flow cell data in the case.
Further, the cell debris removing module performs two-dimensional nuclear density analysis on all cells on FSC-A/SSC-A, obtains se:Sub>A cell population with high density as an effective cell population according to the analysis result, and then artificially removes cell debris except the effective cells.
Further, the density detection module analyzes the density distribution of all effective flow cytometry data on different fluorescent markers by utilizing one-dimensional nuclear density estimation to obtain the negative and positive boundaries of the cells on the fluorescent markers; the phenotype of each population of cells on the different antibodies was confirmed by means of an internal control, and the classification of each population of cells was confirmed.
Further, after the cross-tube matching module obtains the specific population number of various types of cells, finding the boundary of the cells according to the distribution of the cells on SSC-A/CD 45; cross-tube matching is achieved by known boundaries of cells to detect the number, proportion, and phenotype of cells on fluorescent markers.
According to the artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method and system disclosed by the embodiment of the invention, the following technical problems can be solved:
(1) Whether cells with abnormal phenotypes exist in the cases is analyzed through AI, and no false negative cases are ensured;
(2) For the judgment of negative and positive cases, AI can ensure the same accuracy rate as the artificial result or even higher than the artificial result, and for the positive cases, abnormal cell types are diagnosed;
(3) Aiming at the CD marks with different data of each tube, the cell types mainly distinguished by each tube can be classified, and the proportion of the cell types is correspondingly counted;
(4) The AI prompt can be given to the case that the AI can not be normally analyzed or other suspected cases.
The artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method provided by the embodiment of the invention has the following technical effects: firstly, parallel operation can be carried out on a plurality of data to be analyzed, the analysis time of each file is about 90s, and the time cost can be saved while the labor cost is saved; secondly, all data are analyzed objectively, and cell grouping is not influenced by artificial subjective factors; finally, noise interference can be effectively removed, so that abnormal conditions of cell groups can be automatically obtained and preliminary diagnosis can be performed.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method of artificial intelligence assisted flow cytometry 40CD immunophenotyping according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an artificial intelligence assisted flow cytometry 40CD immunophenotype detection method according to an embodiment of the present invention;
FIG. 3 is a block diagram of an artificial intelligence assisted flow cytometry 40CD immunophenotyping system according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1, the artificial intelligence assisted flow cytometry 40CD immunophenotyping method according to the embodiment of the present invention includes the following steps:
step S1, extracting flow cytometry data in a case.
In this step, the file in FCS format or LM0 format is parsed to extract flow cytometric data in the case.
And S2, removing cell fragments except the effective cells from the flow cytometry data to obtain effective flow cytometry data.
Specifically, two-dimensional nuclear density analysis is carried out on all cells on FSC-A/SSC-A, se:Sub>A cell group with higher density is obtained as an effective cell group according to the analysis result, and then cell fragments except the effective cells are removed by combining with an artificial detection habit. The method realizes automatic analysis to obtain effective cell groups, and performs preliminary screening; and then, artificial fine detection is combined to carry out secondary screening, so that effective flow cytometry data with more accurate results can be obtained.
And S3, performing weighted combination on the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture Model (Gaussian-Mixed-Model), and establishing a multi-dimensional Gaussian Model. Then, the cell data are divided into different cell groups according to the probability distribution of the effective flow cytometry data obtained in the step S2 on the multidimensional gaussian model.
By adopting the clustering algorithm of the Gaussian-Mixed-Model, the probability distribution of the effective loss cell data in the step S2 can be obtained, so that the effective flow cell data is divided into a plurality of cell groups. This is to lay down the classification basis for the cell class identification of the subsequent step.
And step S4, performing density detection on each cell group, and judging the cell group type.
Specifically, the density distribution of all effective flow cytometry data on different fluorescent markers is analyzed by utilizing one-dimensional nuclear density estimation to obtain the negative and positive boundaries of the cells on the fluorescent markers; the phenotype of each population of cells on the different antibodies was confirmed by means of an internal control, and the classification of each population of cells was confirmed.
In this step, according to the gaussian clustering analysis result in step S3, density detection is performed on each cell group, and a fluorescence labeling threshold is obtained to judge the phenotype of the cell group, thereby obtaining the category of each cell group.
In the embodiment of the invention, the cell types mainly distinguished in each tube can be classified according to the CD marks of different data in each tube, and the proportion of the cell types is calculated correspondingly. By the mode, the cell group classification is realized, statistics aiming at cell data of each tube can be realized, the proportion of different cell data of each tube is analyzed by taking the test tube as a unit, and the multi-dimensional statistical analysis is realized.
And S5, performing cross-tube matching on the cell population according to the determined cell population category to obtain the number, the proportion and the phenotype of various cells.
Specifically, after obtaining the specific population number of each kind of cells, finding the boundary of the kind of cells according to the distribution of the cells on SSC-A/CD 45; cross-tube matching is achieved by known boundaries of cells to detect the number, proportion, and phenotype of cells on fluorescent markers.
And S6, judging the abnormal cell type according to the number, the proportion and the phenotype of each cell obtained in the step S5.
For example, in this step, it is determined whether there is an abnormality associated with acute leukemia and the type of the abnormality based on the number, ratio and representation of various cells, thereby performing a preliminary diagnosis.
In addition, an AI prompt can be given for cases that the AI cannot be normally analyzed or other suspected cases.
It should be noted that the method of the present invention is not limited to the abnormality determination of acute leukemia, but the above is only an exemplary function, and other types of abnormality detection can also be implemented as required, and the method has a wide application range and is suitable for popularization and use.
According to the artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method provided by the embodiment of the invention, the following technical problems can be solved:
(1) Whether cells with abnormal phenotypes exist in the cases is analyzed through AI, and no false negative cases are ensured;
(2) For the judgment of negative and positive cases, AI can ensure the same accuracy rate as the artificial result or even higher than the artificial result, and for the positive cases, abnormal cell types are diagnosed;
(3) Aiming at the CD marks with different data of each tube, the cell types mainly distinguished by each tube can be classified, and the proportion of the cell types is correspondingly counted;
(4) An AI prompt can be given for a case or other suspected case for which the AI cannot be normally analyzed.
As shown in fig. 3, the artificial intelligence assisted flow cytometry 40CD immunophenotyping system provided in the embodiment of the present invention includes: the cell detection system comprises a cell data extraction module 1, a cell debris removal module 2, a clustering module 3, a density detection module 4, a cross-tube matching module 5 and a cell abnormity type judgment module 6.
Specifically, the cell data extraction module 1 is used for extracting flow cytometry data in a case.
In an embodiment of the present invention, the cell data extraction module 1 parses a file in FCS format or LM0 format to extract flow cytometry data in a case.
The cell debris removing module 2 is used for removing cell debris except for effective cells from the flow cytometry data to obtain effective flow cytometry data.
Specifically, the cell debris removal module 2 performs two-dimensional nuclear density analysis on all cells on FSC-A/SSC-A, obtains se:Sub>A cell population with high density as an effective cell population according to the analysis result, and then artificially removes cell debris except the effective cells. The method realizes automatic analysis to obtain effective cell groups, and performs primary screening; and then, artificial fine detection is combined to perform secondary screening, so that effective flow cytometry data with more accurate results can be obtained.
The clustering module 3 is used for weighting and combining the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture model to establish a multidimensional Gaussian model; and dividing the cell data into different cell groups according to the probability distribution of the effective flow cell data on the multidimensional Gaussian model.
The clustering and clustering module 3 obtains the probability distribution of the effective loss cell data by using the clustering and clustering algorithm of the Gaussian-Mixed-Model, so as to divide the effective flow cytometry data into a plurality of cell groups. This is to lay down the classification basis for the cell class identification of the subsequent step.
The density detection module 4 is used for performing density detection on each cell group and judging the cell group type.
Specifically, the density detection module 4 analyzes the density distribution of all effective flow cytometry data on different fluorescent markers by using one-dimensional nuclear density estimation to obtain the negative and positive boundaries of cells on the fluorescent markers; the phenotype of each population of cells on the different antibodies was confirmed by means of an internal control, and the classification of each population of cells was confirmed.
The density detection module 4 performs density detection on each cell group according to the Gaussian clustering analysis result, obtains a fluorescence labeling threshold value, judges the phenotype of the cell group, and further obtains the category of each cell group.
In the embodiment of the invention, the cell categories mainly distinguished in each tube can be classified according to the CD marks of each tube, and the proportion of the cell categories is correspondingly counted. By the mode, the cell group classification is realized, statistics aiming at cell data of each tube can be realized, the proportion of different cell data of each tube is analyzed by taking the test tube as a unit, and the multi-dimensional statistical analysis is realized.
The cross-tube matching module 5 is used for performing cross-tube matching of the cell population according to the determined cell population category so as to obtain the number, proportion and phenotype of various cells.
Specifically, after the cross-tube matching module 5 obtains the specific population number of the cells of various categories, the boundary of the cells is found according to the distribution of the cells on SSC-A/CD 45; cross-tube matching is achieved by the known boundaries of the cells to detect the number, proportion of cells and phenotype on the fluorescent label.
The cell abnormal type judging module 6 is used for judging the cell abnormal type according to the obtained number, proportion and phenotype of various cells.
For example, the number, ratio and representation of various cells are used to determine whether there are abnormalities and abnormality types related to acute leukemia, so as to make a preliminary diagnosis.
In addition, an AI prompt can be given for cases that the AI cannot be normally analyzed or other suspected cases.
It should be noted that the method of the present invention is not limited to the abnormality determination of acute leukemia, but the above is only an exemplary function, and other types of abnormality detection can also be implemented as required, and the method has a wide application range and is suitable for popularization and use.
The artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method and the system thereof have the following technical effects: firstly, parallel operation can be carried out on a plurality of data to be analyzed, the analysis time of each file is about 90s, and the time cost can be saved while the labor cost is saved; secondly, all data are analyzed objectively, and cell grouping is not influenced by artificial subjective factors; finally, noise interference can be effectively removed, so that abnormal conditions of cell groups can be automatically obtained and preliminary diagnosis can be performed.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. An artificial intelligence assisted flow cytometry 40CD immunophenotyping detection method is characterized by comprising the following steps:
s1, extracting flow cytometry data in a case;
s2, removing cell fragments except effective cells from the flow cytometry data to obtain effective flow cytometry data;
s3, performing weighted combination on the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture model to establish a multidimensional Gaussian model; dividing the cell data into different cell groups according to the probability distribution of the effective flow cytometry data obtained in the step S2 on the multidimensional Gaussian model;
s4, performing density detection on each cell population, and judging the cell population type; analyzing the density distribution of all effective flow cytometry data on different fluorescent markers by utilizing one-dimensional nuclear density estimation to obtain the positive and negative boundaries of the cells on the fluorescent markers; confirming the phenotype of each group of cells on different antibodies by an internal control method, and further confirming the category of each group of cells;
according to the Gaussian clustering analysis result in the step S3, performing density detection on each cell group, acquiring a fluorescence labeling threshold value, judging the phenotype of the cell group, and further acquiring the category of each cell group; aiming at the CD marks with different data of each tube, the cell types mainly distinguished by each tube can be classified, and the proportion of the cell types is correspondingly counted; by the method, the classification of cell groups is realized, statistics aiming at cell data of each tube can be realized, the proportion of different types of cell data in each tube is analyzed by taking a test tube as a unit, and multi-dimensional statistical analysis is realized;
s5, performing cross-tube matching on the cell population according to the determined cell population category to obtain the number, proportion and phenotype of various cells; after obtaining the specific population number of various types of cells, finding the boundary of the cells according to the distribution of the cells on SSC-A/CD 45; cross-tube matching is achieved by known boundaries of cells to detect the number, proportion of cells and phenotype on fluorescent markers;
and S6, judging the abnormal cell type according to the number, the proportion and the phenotype of various cells obtained in the step S5.
2. The artificial intelligence assisted flow cytometry 40CD immunophenotype detection method of claim 1, wherein files in FCS format or LM0 format are parsed to extract flow cytometric data in case.
3. The method for detecting 40CD immunophenotype according to claim 1, wherein in step S2, two-dimensional nuclear density analysis is performed on all cells on FSC-A/SSC-A, se:Sub>A dense cell group is obtained as an effective cell group according to the analysis result, and then cell debris except the effective cells is removed manually.
4. An artificial intelligence assisted flow cytometry 40CD immunophenotypic test system, comprising:
the cell data extraction module is used for extracting flow cell data in a case;
the cell debris removing module is used for removing cell debris except effective cells from the flow cytometry data to obtain effective flow cytometry data;
the clustering module is used for carrying out weighted combination on the Gaussian distribution of the fluorescence intensity of each antibody through a clustering algorithm based on a Gaussian mixture model to establish a multidimensional Gaussian model; dividing the cell data into different cell groups according to the probability distribution of the effective flow cytometry data on the multidimensional Gaussian model;
the density detection module is used for carrying out density detection on each cell population and judging the cell population type; the density detection module analyzes the density distribution of all effective flow cytometry data on different fluorescent markers by utilizing one-dimensional nuclear density estimation to obtain the negative and positive boundaries of the cells on the fluorescent markers; confirming the phenotype of each group of cells on different antibodies by an internal control method, and further confirming the category of each group of cells; analyzing the density distribution of all effective flow cytometry data on different fluorescent markers by utilizing one-dimensional nuclear density estimation to obtain the positive and negative boundaries of the cells on the fluorescent markers; confirming the phenotype of each group of cells on different antibodies by an internal control method, and further confirming the category of each group of cells;
according to the Gaussian clustering analysis result, performing density detection on each cell group to obtain a fluorescence labeling threshold value to judge the phenotype of the cell group, and further obtaining the category of each cell group; aiming at the CD marks with different data of each tube, the cell types mainly distinguished by each tube can be classified, and the proportion of the cell types is correspondingly counted; by the method, the classification of cell groups is realized, statistics aiming at cell data of each tube can be realized, the proportion of different types of cell data in each tube is analyzed by taking a test tube as a unit, and multi-dimensional statistical analysis is realized;
the cross-tube matching module is used for performing cross-tube matching on the cell populations according to the determined cell population types so as to obtain the number, proportion and phenotype of various cells; after the cross-tube matching module obtains the specific population number of various types of cells, finding the boundary of the cells according to the distribution of the cells on SSC-A/CD 45; cross-tube matching is achieved by known boundaries of cells to detect the number, proportion, and phenotype of cells on fluorescent markers;
and the cell abnormal type judging module is used for judging the cell abnormal type according to the obtained number, proportion and phenotype of various cells.
5. The system of claim 4, wherein the cell data extraction module parses a file in FCS format or LM0 format to extract flow cytometric data from a case.
6. The system of claim 4, wherein the cell debris removal module performs two-dimensional nuclear density analysis on all cells on FSC-A/SSC-A, obtains se:Sub>A dense cell population as an effective cell population according to the analysis result, and then combines the manual removal of cell debris except the effective cells.
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| CN105940301A (en) * | 2014-04-17 | 2016-09-14 | 深圳迈瑞生物医疗电子股份有限公司 | Flow cytometer and multidimensional data classification method and apparatus thereof |
| CN106548203A (en) * | 2016-10-21 | 2017-03-29 | 北京信息科技大学 | A kind of fast automatic point of group of multiparameter flow cytometry data and gating method |
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