A data conversion method, device, and program product between functional detection systems

Abstract

The application relates to the field of intelligent medical treatment, in particular to a data conversion method between functional detection systems, equipment and a program product. The method comprises the following steps: acquiring a detection value of a first functional detection system; and inputting the detection value to a fitting curve to obtain a detection value of a second functional detection system; the process of obtaining the fitting curve comprises the following steps: S1, acquiring a detection sample set N; S2, inputting the detection sample set N to the first functional detection system and the second functional detection system to obtain a first detection value set N1 and a second detection value set N2; and S3, performing data fitting on the first detection value set N1 and the second detection value set N2 to obtain a data fitting curve, wherein the fitting curve is a mapping relationship between detection data of the first detection system and the second detection system. The application can effectively solve the problem of inconsistent data between different functional detection systems and has good clinical value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent medical treatment, in particular to a data conversion method between functional detection systems, equipment, program product and computer readable storage medium. BACKGROUND

[0002] Most studies believe that there are differences in the detection results of some projects between different brands of chemiluminescence systems and different models of chemiluminescence(CLIA) immune detection equipment of the same brand, which causes confusion or misdiagnosis in clinical diagnosis. Therefore, most of the existing studies compare the consistency of different systems. For example, thyroid hormones almost act on all nucleated cells in the human body and affect their growth and development and energy metabolism. The accuracy and standardization of thyroid function detection results are crucial. When a new detection system or method is used, methodological comparison is needed to ensure the quality, consistency and reliability of the analysis results and to avoid misdiagnosis in clinical diagnosis. With the development of technology, chemiluminescence systems have been gradually applied. However, the detection data of some chemiluminescence systems during detection are not suitable for the existing calculation standard, which makes it impossible to use the data of some examination projects, making it difficult for doctors to make subsequent diagnosis when using this part of the system for examination. SUMMARY

[0003] In view of the above problems, the present application provides a data conversion method between functional detection systems, which specifically comprises:

[0004] obtaining a detection value of a first functional detection system;

[0005] inputting the detection value into a fitting curve to obtain a detection value of a second functional detection system;

[0006] The process of obtaining the fitting curve is:

[0007] S1, obtaining a detection sample set N;

[0008] S2, inputting the detection sample set N into a first functional detection system and a second functional detection system to obtain a first detection value set N1 and a second detection value set N2;

[0009] S3, data fitting of the first detection value set N1 and the second detection value set N2 to obtain a data fitting curve, wherein the fitting curve is a mapping relationship between the detection data of the first detection system and the second detection system.

[0010] The S2 further comprises mean value calculation, and the mean value calculation is performed on the detection value sets N1 and N2 to obtain a first mean value detection data N3 and a second mean value detection data N4, and S3 is replaced by: data fitting of the first mean value detection data N3 and the second mean value detection data N4 to obtain a data fitting curve.

[0011] The first functional detection system comprises one or more of the following: YHLO iFlash 3000G, AutoLumo A2000Plus, Mindray CL-8000i;

[0012] Optionally, the second functional detection system is one or more of the following: Roche cobas 601, Roche Cobase602, Siemens ADVIA Centaur XP, Abbott ARCHITECT i4000, Beckman UniCel Dxl 800.

[0013] The method further comprises data judgment to determine whether the detection values of the first functional detection system and the detection values of the second functional detection system are consistent;

[0014] When the detection results are consistent, the results of the first functional detection system or the second functional detection system are outputted, and when the detection results of the functional detection systems are inconsistent, the results of the second functional detection system are outputted.

[0015] The detection sample set is selected by a calibration method, and the surrounding samples are selected around the calibration point to obtain the detection samples.

[0016] The detection sample is one or more of the following: serum, plasma;

[0017] Optionally, the functions include one or more of the following: thyroid function, prolactin test.

[0018] Optionally, the thyroid function includes one or more of the following: TSH, T3, T4, FT3, FT4, TPOAb, TgAb, TRAb.

[0019] The method further comprises multi-system conversion, the Lth functional detection system and the first functional detection system obtain the data conversion results between systems through a first fitting curve, or the Lth functional detection system and the second functional detection system obtain the data conversion results between systems through a second fitting curve, L is a natural number greater than or equal to 1;

[0020] Optionally, the Lth functional detection system, the first functional detection system, and the second functional detection system obtain the data conversion results between systems through a third fitting curve;

[0021] Optionally, the L functional detection systems include systems with functional detection data standardization, new systems of the same series, old systems of the same series, systems of the same brand but different series, and systems of different brands.

[0022] The present application aims to provide a computer program product, which has a computer program or instructions thereon, and the computer program or instructions are executed by a processor to realize the above-mentioned function detection system data conversion method.

[0023] The present application aims to provide a computer device, which comprises a memory, a processor and a computer program or instructions stored on the memory, and the computer program or instructions are executed by the processor to realize the above-mentioned function detection system data conversion method.

[0024] The present application aims to provide a computer readable storage medium, which has a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to realize the above-mentioned function detection system data conversion method.

[0025] Advantages of the present application:

[0026] 1. The present application is aimed at different systems between the data inconsistency, through the data mapping between different systems data conversion, so that the system data that does not meet the existing computing standards can be used smoothly, through the method of the present application for data conversion, ensure that the data mapping of chemiluminescence system is the data that meets the existing computing standards, so that the doctor who is used to diagnose according to the existing computing standards can smoothly use the data of chemiluminescence system for subsequent diagnosis, further, avoid the problem that the function of the system is idle / self-limited, improve the utilization rate of the system, save resources, and have practical application value.

[0027] 2. The present application can convert data for different brands of systems, different series of the same brand, and new and old systems of the same series, and can convert data for multiple systems, so that a single system can get the mapping data of other systems through the data mapping relationship, which can avoid the inconsistency between different systems, and can also help to avoid the error between systems. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0029] Figure 1 A functional detection system data conversion method flowchart is provided for the embodiments of the present application.

[0030] Figure 2 A functional detection system data conversion system schematic diagram is provided for the embodiments of the present application.

[0031] Figure 3A schematic diagram of a data conversion device between functional testing systems provided in an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram of the TSH mapping results provided in an embodiment of the present invention;

[0033] Figure 5 This is a schematic diagram of the T3 mapping results provided in an embodiment of the present invention;

[0034] Figure 6 This is a schematic diagram of the T4 mapping results provided in an embodiment of the present invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0036] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as S101, S102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0037] Figure 1 A schematic diagram of a data conversion method between functional testing systems provided in this embodiment of the invention specifically includes:

[0038] S101: Obtain the detection value from the first functional detection system;

[0039] In one embodiment, the first function detection system includes, but is not limited to, one or more of the following: YHLO iFlash 3000G, AutoLumo A2000Plus, and Mairui CL-8000i;

[0040] In one embodiment, the function includes, but is not limited to, one or more of the following: thyroid function and prolactin testing;

[0041] Optionally, the thyroid function includes one or more of the following: TSH, T3, T4, FT3, FT4, TPOAb, TgAb, and TRAb.

[0042] In one specific embodiment, serum: collect 133 cases of clinical detection remaining specimens without hemolysis, jaundice, and lipemia from Beijing Obstetrics and Gynecology Hospital, Capital Medical University from March to April 2024, each specimen not less than 0.5 mL. Used for method comparison and bias evaluation of Roche cobas601 fully automatic chemiluminescence analyzer and Yihuidong YHLO iFlash3000G fully automatic chemiluminescence analyzer. Instruments and reagents: Roche cobas (Roche, Germany) and Yihuidong YHLO iFlash3000G fully automatic chemiluminescence immunoassay analyzer and its matching reagents.

[0043] Instrument operation:

[0044] According to the requirements of the manufacturer, the instrument is maintained, calibrated and daily quality controlled. In the case of ensuring the calibration and quality control within control, the performance of the instrument is verified.

[0045] Method comparison:

[0046] In this test, Roche and Yihuidong systems were used to detect the levels of TSH, T3, T4, FT3 and FT4 in serum samples, a total of 133 specimens. Among them, Roche cobas601 fully automatic chemiluminescence immunoassay analyzer has participated in quality evaluation for many years and the results are excellent, with good traceability. The results are judged according to 4 times the average value of absolute difference and 4 times the average value of relative difference, and at most 2.5% of the data can be defined as inter-method outliers. Scatter plots of TSH, T3, T4, FT3 and FT4 were drawn respectively, showing regression equation Y=aX+b, and the determination coefficient R2, 95% confidence interval was calculated, and the expected bias was calculated. Using the reference range provided by the manufacturer, the minimum and maximum values were used as the medical decision level of this experiment, and the expected bias at this medical decision level was calculated.

[0047] Statistical analysis:

[0048] The software used for analysis is R version 4.4.0. The mean ± standard deviation and coefficient of variation CV were used to describe the measurement data, Pearson linear correlation analysis was used to calculate the correlation results, extreme studentized deviation ESD was used to evaluate the difference between the two detection methods, linear regression model was used to calculate the quantity relationship of the two detection methods, scatter plot of linear regression fitting line and Bland-Altman analysis were used to evaluate the consistency of the two measurement methods. P<0.05 indicates that the difference is statistically significant.

[0049] S102: input the detection value into the fitting curve to obtain the detection value of the second functional detection system;

[0050] The process of the fitting curve is:

[0051] S1, obtaining a detection sample set N;

[0052] S2, inputting the detection sample set N to a first functional detection system and a second functional detection system to obtain a first detection value set N1 and a second detection value set N2;

[0053] S3, performing data fitting on the first detection value set N1 and the second detection value set N2 to obtain a data fitting curve, the fitting curve being a mapping relationship of detection data between the first detection system and the second detection system.

[0054] In one embodiment, the S2 further comprises mean value calculation, and the mean value calculation is performed on the detection value sets N1 and N2 respectively to obtain first mean value detection data N3 and second mean value detection data N4, and the S3 is replaced by: performing data fitting on the first mean value detection data N3 and the second mean value detection data N4 to obtain the data fitting curve.

[0055] In one embodiment, the second functional detection system comprises one or more of the following: Roche cobas 601, Roche Cobas e602, Siemens ADVIA Centaur XP, Abbott ARCHITECT i4000, Beckman UniCel Dxl 800.

[0056] In one embodiment, the method further comprises data judgment, which judges whether the detection values of the first functional detection system and the second functional detection system are consistent.

[0057] When the detection results are consistent, the result of the first functional detection system or the second functional detection system is outputted, and when the detection results of the functional detection systems are inconsistent, the result of the second functional detection system is outputted.

[0058] When the detection results are consistent, the detection values of the first functional detection system and the second functional detection system are equivalent, and the detection value of the first detection system or the mapped detection value of the second functional detection system is outputted; when the detection results are inconsistent, the mapped detection value of the second functional detection system is outputted.

[0059] In one embodiment, the method further comprises verification judgment, which obtains the detection values of the first functional detection system and the second functional detection system, judges whether the detection values of the first functional detection system and the second functional detection system are consistent, outputs the detection value of the first functional detection system or the detection value of the second functional detection system (system directly outputted data) when the detection results are consistent, and inputs the detection value of the first functional detection system to the fitting curve to obtain the mapped detection value of the second functional detection system when the detection results are inconsistent.

[0060] In one embodiment, the detection sample set is selected by a calibration method, and the detection sample is selected around the calibration point.

[0061] In one embodiment, the calibration method is a 5-point or 7-point calibration method. For example, in a 5-point calibration method, several samples around the 5 calibration points are selected.

[0062] Mean value calculation: each sample is detected 10 times by two sets of instruments to obtain the average value of the two sets of instruments for the sample.

[0063] In one embodiment, the detection sample includes, but is not limited to, one or more of the following: serum, plasma;

[0064] In one embodiment, the method further comprises a multi-system conversion, and the Lth functional detection system and the first functional detection system obtain a data conversion result between the systems through a first fitting curve, or the Lth functional detection system and the second functional detection system obtain a data conversion result between the systems through a second fitting curve, L is a natural number greater than or equal to 1.

[0065] In one embodiment, the first fitting curve is a fitting curve obtained by mapping the data relationship between the Lth functional detection system and the first functional detection system, the second fitting curve is a fitting curve obtained by mapping the data relationship between the Lth functional detection system and the second functional detection system, and the first fitting curve is not equal to the second fitting curve. When any two functional detection systems are converted by the data conversion method of the present application, the data fitting curve applied by the two functional detection systems is obtained by mapping the data relationship between the two functional detection systems.

[0066] In one embodiment, the Lth functional detection system, the first functional detection system, and the second functional detection system obtain a data conversion result between the systems through a third fitting curve.

[0067] In the multi-system conversion process, the third fitting curve is a function or model for final data conversion. Specifically, there are any S functional detection systems, S is a natural number greater than or equal to 3; when the Lth functional detection system and the first functional detection system, the second functional detection system are converted, the data conversion is based on a fitting relationship curve set, i.e., the Lth functional detection system and the first functional detection system obtain L1 data (process data) through L1 fitting curve (fitting curve of the Lth functional detection system and the first functional detection system), and the L1 data obtains L2 data through L12 fitting curve (fitting curve of the first functional detection system and the second functional detection system). The third fitting curve is obtained based on the L1 fitting curve and the L12 fitting curve, and the third fitting curve is f (L1, L12). Further, the Lth functional detection system converts the detection value of the second functional detection system through the third fitting curve.

[0068] In one embodiment, the detection values of different systems are obtained by the data conversion method of the application, wherein the converted detection values are data obtained by data mapping, rather than data directly generated by the systems.

[0069] In the application, the data conversion between different systems is achieved by the data conversion method, which avoids the problem that the diagnosis process and method formed by a doctor after using a system for many years cannot be implemented after the system is replaced.

[0070] In one embodiment, the L functional detection systems include systems with functional detection data standardization, new systems of the same series, old systems of the same series, systems of the same brand but different series, and systems of different brands.

[0071] The conversion method of the application can convert the data of systems meeting the existing calculation standard and systems not meeting the existing calculation standard to solve the errors existing in the detection function items between systems.

[0072] In one specific embodiment, for the inconsistency between different systems, mapping is performed by the method of the application. Figures 4-6 As shown in the table, the data of the new device (not meeting the calculation standard) and the data of the old device (meeting the calculation standard) are linearly related.

[0073] In one embodiment, the timing of data conversion between any two systems includes one or more of the following: before system function detection, after system function detection, after replacing the batch number of detection samples, and after replacing the batch number or type of detection reagents.

[0074] Before system function detection, data conversion is performed before sample testing.

[0075] After system function detection, data conversion is performed after sample testing to solve the inconsistency problem.

[0076] After replacing the batch number of detection samples, data conversion is performed after detecting the previous batch of samples and replacing the next batch of detection samples.

[0077] The data conversion is performed after a reagent batch number for assisting in detecting a sample is changed.

[0078] The embodiment of the present application further provides a computer program product or system, comprising a computer program, which realizes the steps of the data conversion method between functional detection systems when executed by a processor.

[0079] Figure 2 The embodiment of the present application provides a functional detection system data conversion system schematic diagram, and specifically comprises:

[0080] The acquisition module acquires a detection value of the first functional detection system.

[0081] The conversion module inputs the detection value into the fitting curve to obtain a detection value of the second functional detection system.

[0082] The process of the fitting curve is as follows:

[0083] S1, acquiring a detection sample set N.

[0084] S2, inputting the detection sample set N into the first functional detection system and the second functional detection system to obtain a first detection value set N1 and a second detection value set N2.

[0085] S3, performing data fitting on the first detection value set N1 and the second detection value set N2 to obtain a data fitting curve, and the fitting curve is a mapping relationship between detection data of the first detection system and the second detection system.

[0086] Figure 3 The embodiment of the present application provides a functional detection system data conversion device schematic diagram, and specifically comprises:

[0087] The memory is used for storing program instructions, and the processor is used for calling the program instructions, so as to realize any one of the above-mentioned data conversion methods between functional detection systems.

[0088] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program realizes any one of the above-mentioned data conversion methods between functional detection systems when executed by a processor.

[0089] The verification result of the verification embodiment shows that assigning inherent weights to the indications can improve the performance of the method compared with the default setting. It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described here. In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms. The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme. In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be a physically independent unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of software functional units. Those skilled in the art can understand that all or part of the steps of the various methods in the above embodiments can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium, which can include read only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0090] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium, and the above-mentioned medium storage can be read only memory, magnetic disk or optical disk, etc.

[0091] The computer device provided by the present application has been described in detail above. For those skilled in the art, according to the idea of the embodiment of the present application, there will be changes in specific implementation and application range. In view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A data conversion method between functional detection systems, characterized by, The method comprises the following steps: obtaining a detection value of a first function detection system; judging whether the detection value of the first function detection system is consistent with a detection value of a second function detection system; when the detection results are consistent, outputting a result of the first function detection system or the second function detection system, the result being data directly output by the system; when the detection results of the function detection systems are inconsistent, inputting the detection values into a fitting curve to obtain a mapped detection value of the second function detection system; outputting a result of the mapped second function detection system; the result is not data directly output by the system; the first function detection system is a system that does not meet existing calculation standards, and the second function detection system is a system that meets existing calculation standards; the first function detection system comprises YHLO iFlash 3000G, and the second function detection system comprises cobas 601 of Roche; the process of obtaining the fitting curve comprises the following steps: S1, obtaining a detection sample set N; S2, inputting the detection sample set N into the first function detection system and the second function detection system to obtain a first detection value set N1 and a second detection value set N2; S3, performing data fitting on the first detection value set N1 and the second detection value set N2 to obtain a data fitting curve, the fitting curve being a mapping relationship between detection data of the first detection system and the second detection system; the timing of data conversion between any two systems comprises one or more of the following: before system function detection, after system function detection, after changing a detection sample batch number, or after changing a detection reagent batch number or type; multi-system conversion, when the Lth function detection system and the first function detection system and the second function detection system perform data conversion, data conversion is performed based on a fitting relationship curve set, that is, the Lth function detection system and the first function detection system obtain L1 data through a fitting curve L1 between the Lth function detection system and the first function detection system, and the L1 data obtains L2 data through a fitting curve L12 between the first function detection system and the second function detection system; a third fitting curve is obtained based on the fitting curve L1 and the fitting curve L12, and the third fitting curve is f(L1, L12); the Lth function detection system obtains a detection value of the second function detection system through the third fitting curve; the L function detection systems comprise a system with function detection data standardization.

2. The data conversion method between functional detection systems according to claim 1, characterized in that, S2 further comprises mean value calculation, and mean value calculation is performed on the detection value sets N1 and N2 to obtain first mean value detection data N3 and second mean value detection data N4, and S3 is replaced by: performing data fitting on the first mean value detection data N3 and the second mean value detection data N4 to obtain a data fitting curve.

3. The data conversion method between functional detection systems according to claim 1, characterized by, The first function detection system comprises one or more of the following: AutoLumo A2000Plus of Antu, and CL-8000i of Mindray.

4. The data conversion method between functional detection systems according to claim 1, characterized by, The second function detection system is one or more of the following: Cobas e602 of Roche, ADVIA Centaur XP of Siemens, ARCHITECT i4000 of Abbott, and UniCel Dxl 800 of Beckman.

5. The data conversion method between functional detection systems according to claim 1, wherein, The detection sample set is selected by calibration, and the detection sample is selected from the periphery of the calibration point.

6. The data conversion method between functional detection systems according to claim 1, wherein, The detection sample is one or more of serum and plasma.

7. The data conversion method between functional detection systems according to claim 1, wherein, The functions include one or more of thyroid function and prolactin examination.

8. The data conversion method between functional detection systems according to claim 7, wherein, The thyroid function includes one or more of TSH, T3, T4, FT3, FT4, TPOAb, TgAb and TRAb.

9. The data conversion method between functional detection systems according to claim 1, wherein, The method further comprises multi-system conversion, and the Lth function detection system and the first function detection system obtain the data conversion result between the systems through the first fitting curve, or the Lth function detection system and the second function detection system obtain the data conversion result between the systems through the second fitting curve, L being a natural number greater than or equal to 1.

10. The data conversion method between functional detection systems according to claim 9, wherein, The Lth function detection system, the first function detection system and the second function detection system obtain the data conversion result between the systems through the third fitting curve.

11. The data conversion method between functional detection systems according to claim 9, wherein, The L function detection systems include a new system of the same series, an old system of the same series, a system of the same brand but different series, and a system of different brands.

12. A computer program product having a computer program or instructions thereon, characterized in that, The computer program or instruction is executed by the processor to realize the data conversion method between the function detection systems according to any one of claims 1-11.

13. A computer device comprising a memory, a processor, and a computer program or instructions stored on the memory, wherein, The computer program or instruction is executed by the processor to realize the data conversion method between the function detection systems according to any one of claims 1-11.

14. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instruction is executed by the processor to realize the data conversion method between the function detection systems according to any one of claims 1-11.

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