Helicobacter pylori detection time optimization method and device and electronic equipment

Abstract

The invention discloses a helicobacter pylori detection time optimization method and device and electronic equipment, and relates to the field of data processing. In the method, a plurality of consecutive count data received from a measurement device is obtained, the count data representing a 14CO2 radioactive event count measured by the measurement device per minute; calculating a normalized difference value of the first counting data and the second counting data, wherein the first counting data and the second counting data are any two adjacent counting data in the plurality of counting data; comparing the normalized difference value with a preset threshold value so as to judge whether to enter the turf area or not; if it is determined that the user enters the turf area, it is determined that measurable time is reached, and the measuring equipment is controlled to stop measuring; and taking the target counting data corresponding to the measurable time as a final measurement result, and outputting the final measurement result. By implementing the technical scheme provided by the invention, the measurement accuracy is improved by judging the turf region moment with stable data.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, device, and electronic device for optimizing the detection time of Helicobacter pylori. Background Art

[0002] With the progress of medical diagnostic techniques and the increasing demand for pathogen detection, the rapid and accurate detection of Helicobacter pylori has become particularly important. Helicobacter pylori is a bacterium that can survive in the gastric mucosa of humans and is directly associated with various gastric diseases, including chronic gastritis, gastric ulcer, and even gastric cancer. Therefore, the development of an efficient and rapid detection method is of great significance for early diagnosis and treatment. Urea 14 13C breath test is one of the preferred methods for Helicobacter pylori detection. After the subject takes 14 13C urea drug, if there is Helicobacter pylori in the stomach, the urease secreted by Helicobacter pylori will decompose the urea drug to generate 14 CO2 and NH3, 14 CO2 will enter the alveoli through the blood circulation and be excreted from the human body during exhalation. Therefore, by using a dedicated device and method to detect whether the exhaled breath of the subject after taking the drug contains 14 CO2, it can be determined whether the subject is infected with Helicobacter pylori.

[0003] Currently, existing 14 13C-UBT measurements are mostly based on a fixed measurement time, and the same measurement time is used for all types of samples. This not only increases the redundant measurement time for highly active samples that require a shorter measurement time but may also lead to inaccurate measurements. For low-activity samples that require a longer measurement time, the time is not long enough, and the result accuracy is not high. Especially when measuring low-activity samples near the critical value, there will also be problems of false positive and false negative judgments, resulting in low measurement accuracy.

[0004] Therefore, there is an urgent need for a method, device, and electronic device for optimizing the detection time of Helicobacter pylori. Summary of the Invention

[0005] The present application provides a method, device, and electronic device for optimizing the detection time of Helicobacter pylori, which improves the measurement accuracy by determining the flat region time when the data is stable.

[0006] In a first aspect of the present application, a method for optimizing the detection time of Helicobacter pylori is provided. The method includes: obtaining a plurality of consecutive count data received from a measurement device, where the count data represents the 14 CO 2Radioactive event count; calculate the normalized difference between the first count data and the second count data, where the first count data and the second count data are any two adjacent count data among the multiple count data; compare the normalized difference with a preset threshold to determine whether to enter the plateau region; if it is determined to enter the plateau region, determine the measurable time and control the measurement device to stop measuring; use the target count data corresponding to the measurable time as the final measurement result and output the final measurement result.

[0007] By adopting the above technical solution, by obtaining the radioactive event count measured by the measurement device per minute 14 CO 2 Radioactive event count, calculate the normalized difference between two adjacent count data, and compare the normalized difference with a preset threshold to determine whether to enter a stable plateau region. When it is determined to enter the plateau region, determine that this is the measurable time, control the measurement device to stop measuring, and output the target count data corresponding to the measurable time as the final measurement result. This method dynamically monitors the change trend of the count data, automatically judges the optimal measurement stop time point, avoids the blind extension or premature stop of the measurement time, shortens the detection time while ensuring the reliability of the measurement result, and improves the detection efficiency. This method can automatically judge the stable point of the radioactive count, thereby accurately determining the best stop time for the measurement. At the same time, the final measurement result is optimized and selected, which is more representative and accurate, provides high-quality data support for subsequent diagnosis, and improves the accuracy of the measurement evaluation.

[0008] Optionally, the calculation of the normalized difference between the first count data and the second count data specifically includes: calculate the difference between the first count data and the second count data; according to the normalization factor and the difference, obtain the normalized difference, and the calculation formula of the normalized difference is: ; Wherein, is the normalized difference, CPM i is the second count data; CPM i-1 is the first count data.

[0009] By adopting the above technical solution, the normalized difference is used as the basis for judging whether the measurement enters the plateau region. The calculation of the normalized difference fully considers the statistical characteristics of the count data. By calculating the difference between two adjacent count data and dividing it by the square root of the second count data, the normalized difference not only reflects the change amount of the count data but also embodies the statistical fluctuation of the count data. When the count data enters the stable plateau region, the normalized difference will significantly decrease and tend to a constant. Compared with directly using the difference of the original count data, the normalized difference can more accurately reveal the true change trend of the count data, effectively reducing the influence of statistical fluctuations, thereby improving the reliability of plateau region determination. The use of the normalized difference enables this method to adapt to measurements under different radioactive levels and statistical fluctuations, having a wider applicability.

[0010] Optionally, before comparing the normalized difference with a preset threshold to determine whether to enter the plateau region, the method further includes: obtaining a coefficient-related threshold corresponding to the first count data based on a preset CPM time curve; obtaining a clinical determination threshold corresponding to the first count data according to historical clinical diagnosis results; adding the coefficient-related threshold and the clinical determination threshold to obtain the preset threshold.

[0011] By adopting the above technical solution, when determining whether the measurement enters the plateau region, the coefficient-related threshold and the clinical determination threshold are comprehensively considered, improving the accuracy and reliability of the determination. First, based on the preset CPM time curve, a coefficient-related threshold corresponding to the first count data is obtained, and the coefficient-related threshold reflects the expected change trend of the count level according to the radioactive decay law and the exhalation detection mechanism. Then, according to historical clinical diagnosis results, a clinical determination threshold corresponding to the first count data is obtained, and the clinical determination threshold reflects the statistical law of the count level determined as positive in actual clinical applications. Finally, the coefficient-related threshold and the clinical determination threshold are added to obtain the preset threshold. This threshold determination method can adapt to the differences of different detection objects and detection environments and has better generalization performance.

[0012] Optionally, the specific calculation formula for obtaining the clinical determination threshold corresponding to the first count data according to historical clinical diagnosis results is ; where k 2 is a constant coefficient, 0 < k 2 < 1, CPM i is the CPM value at the current T i seconds, BG is the instrument background, and DT is the instrument judgment threshold.

[0013] By adopting the above technical solution, a specific formula for calculating the clinical determination threshold is given. This formula is based on the mathematical form of the Gaussian function and comprehensively considers factors such as the instrument background, judgment threshold, and current counting level. By introducing the background value reflecting the instrument performance and the instrument judgment threshold, and performing the power exponent calculation of the Gaussian function according to the current counting level, the determination of the clinical determination threshold makes full use of the performance parameters of the instrument itself and makes an adaptive adjustment for different counting levels. At the same time, the formula includes constant coefficients determined according to historical data and expert experience, which reflects the characteristics of actual clinical applications to a certain extent. The clinical determination threshold calculation formula adopted by this method improves the scientificity and accuracy of the clinical determination threshold, providing an important basis for reliable plateau region determination.

[0014] Optionally, the comparing the normalized difference with a preset threshold to determine whether to enter the plateau region specifically includes: If it is determined that the normalized difference and the preset threshold satisfy , then it is determined to enter the plateau region, where is the normalized difference at the i-th second, and n is the number of CPM values; If it is determined that the normalized difference and the preset threshold do not satisfy , then it is determined not to enter the plateau region.

[0015] By adopting the above technical solution, a quantitative criterion for determining whether to enter the plateau region is proposed, that is, comparing the magnitude relationship between the normalized difference and the preset threshold. By calculating the arithmetic mean of the normalized differences within a certain number of seconds before the current time point, this method makes full use of the data information of consecutive multiple time points and reduces the influence of data fluctuations at a single time point. If the average value of the normalized differences is less than the preset threshold, it can be determined that the measurement has entered a stable plateau region state. Compared with the determination method that only considers the data at a single time point, this criterion based on the average value of multiple time points is more robust and reliable, and can effectively avoid misjudgment caused by data fluctuations. At the same time, by adjusting the number of time points included in the average, the strictness of the criterion can be flexibly controlled to meet different detection requirements. The use of the quantitative criterion makes the plateau region determination more standardized and repeatable, reduces the interference of human factors, and improves the objectivity and consistency of the detection process.

[0016] Optionally, after comparing the normalized difference with a preset threshold to determine whether to enter the plateau region, the method further includes: if it is not determined to enter the plateau region within a preset time period, obtaining the counting data at the end of the preset time period as the final measurement result; converting the final measurement result from CPM unit to DPM unit; based on a preset corresponding table of Helicobacter pylori concentration, obtaining the Helicobacter pylori concentration corresponding to the final measurement result, and outputting the Helicobacter pylori concentration.

[0017] By adopting the above technical solution, on the basis of determining whether the measurement enters the plateau region, the final measurement result is further converted into an index commonly used in clinical practice, and a diagnosis prompt is given, improving the practicability and integrity. When it is not determined to enter the plateau region within the preset time period, the method uses the count data at the end of the preset time period as the final measurement result, ensuring that the detection is completed and the result is given within the specified time. By converting the final measurement result from the original CPM unit to the DPM unit reflecting the actual radioactivity level of the sample, this method corrects the measurement deviation caused by the difference in counting efficiency, making the result more accurately reflect the sample situation. On this basis, according to the preset corresponding table of Helicobacter pylori concentration, the method automatically converts the final measurement result into the corresponding Helicobacter pylori concentration, and outputs the concentration value as an intuitive diagnosis prompt. This form of result presentation is closer to the clinical application requirements and facilitates clinical practice.

[0018] Optionally, the obtaining of multiple consecutive count data received from the measuring device is specifically calculated by the formula: ; where i is the i-th second, Ni is the count of 14CO2 radioactive events read by the measuring device per second, and T is the preset time. 14 CO2 radioactive event count, T is the preset time.

[0019] By adopting the above technical solution, by accumulating the count of 14CO2 radioactive events read by the measuring device per second 14 CO 2 and dividing by the accumulated time span, the method obtains the average count level per unit time, that is, the CPM value. The calculation of the CPM value takes into account the data accumulation of the entire measurement process and reduces the influence of the random fluctuation of individual data through averaging, making the count data more stable and reliable.

[0020] In the second aspect of the present application, a device for optimizing the detection time of Helicobacter pylori is provided. The device includes: an acquisition module and a processing module, where: the acquisition module is used to acquire multiple consecutive count data received from the measuring device, and the count data represents the count of 14CO2 radioactive events measured by the measuring device per minute; the processing module is used to calculate the normalized difference between the first count data and the second count data, and the first count data and the second count data are any two adjacent count data among the multiple count data; the processing module is further used to compare the normalized difference with a preset threshold to determine whether to enter the plateau region; the processing module is further used to, if it is determined to enter the plateau region, determine the measurable time and control the measuring device to stop measuring; the processing module is further used to use the target count data corresponding to the measurable time as the final measurement result and output the final measurement result.

[0021] In a third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. Both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device executes the method described in any one of the above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions that, when executed, execute the method described in any one of the above.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By obtaining the 14 CO 2 radioactive event count measured by the measuring device per minute, calculating the normalized difference between two adjacent count data, and comparing the normalized difference with a preset threshold to determine whether to enter a stable plateau region. When it is determined to enter the plateau region, the current time is determined as the measurable time, the measuring device is controlled to stop measuring, and the target count data corresponding to the measurable time is output as the final measurement result. This method dynamically monitors the change trend of the count data, automatically determines the optimal measurement stop time point, avoids blindly extending or prematurely stopping the measurement time, shortens the detection time while ensuring the reliability of the measurement result, and improves the detection efficiency. At the same time, the final measurement result is optimally selected, more representative and reliable, providing high-quality data support for subsequent diagnosis.

[0024] 2. Using the normalized difference as the basis for judging whether the measurement enters the plateau region, the calculation of the normalized difference fully considers the statistical characteristics of the count data. By calculating the difference between two adjacent count data and dividing it by the square root of the second count data, the normalized difference not only reflects the change amount of the count data but also reflects the statistical fluctuation of the count data. When the count data enters the stable plateau region, the normalized difference will significantly decrease and tend to be a constant. Compared with directly using the difference of the original count data, the normalized difference can more accurately reveal the true change trend of the count data, effectively reducing the influence of statistical fluctuations, thereby improving the reliability of the plateau region determination. The use of the normalized difference makes this method adaptable to measurements under different radioactive levels and statistical fluctuations, with a wider applicability.

[0025] 3. When determining whether the measurement has entered the plateau region, by comprehensively considering the coefficient-related threshold and the clinical determination threshold, the accuracy and reliability of the determination are improved. First, based on the preset CPM time curve, the coefficient-related threshold corresponding to the first count data is obtained, and the coefficient-related threshold reflects the changing trend of the expected count level according to the radioactive decay law and the exhaled gas detection mechanism. Then, according to the historical clinical diagnosis results, the clinical determination threshold corresponding to the first count data is obtained, and the clinical determination threshold reflects the statistical law of the count level determined as positive in actual clinical applications. Finally, the coefficient-related threshold and the clinical determination threshold are added together to obtain the preset threshold. This method for determining the threshold can adapt to the differences of different detection objects and detection environments and has better generalization performance. Description of the Drawings

[0026] Figure 1 is a schematic flowchart of a method for optimizing the detection time of Helicobacter pylori disclosed in an embodiment of the present application; Figure 2 is a schematic block diagram of a device for optimizing the detection time of Helicobacter pylori disclosed in an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0027] Description of the reference numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0028] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0029] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0030] In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprise", "include", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] The present application provides a method for optimizing the Helicobacter pylori detection time. Referring to Figure 1 , Figure 1 is a schematic flowchart of a method for optimizing the Helicobacter pylori detection time provided by an embodiment of the present application. This method is applied to a server, which is a server executing a program for optimizing the Helicobacter pylori detection time. The server can be a single server, a server cluster composed of multiple servers, or a cloud computing service center. This method includes steps S101 to S105, and the above steps are as follows: Step S101: Obtain a plurality of consecutive count data received from a measuring device. The count data represents the 14 CO 2 radioactive event count measured by the measuring device per minute.

[0032] In step S101, to obtain a plurality of consecutive count data received from the measuring device, the specific calculation formula is: ; where N i is the 14 CO2 radioactive event count read by the measuring device per second, and T is the time.

[0033] Specifically, the server reads the 14 CO 2 radioactive event count from the measuring device once per second, denoted as N i , where i represents the i-th second. The server continuously reads the count of the measuring device until the predetermined measurement time T, that is, the preset time, is reached. After obtaining all the count data from the 0th second to the Tth second, the server calculates the 14 CO 2 radioactive event count CPM per minute using the above formula.

[0034] For example, assume that the predetermined measurement time T is 10 seconds. The server reads the count of the measuring device once per second within these 10 seconds, and the obtained data is as follows: At the 0th second: N 0= 20; At the 1st second: N 1 = 22; At the 2nd second: N 2 = 19;...; At the 9th second: N 9 = 21; Substituting the count data within these 10 seconds into the formula, we can obtain: CPM = 120; This indicates that within these 10 seconds, the measuring device measures 120 times per minute on average 14 CO 2 radioactive events

[0035] After the server obtains the first CPM value, it continues to obtain subsequent CPM values in the same way, forming a continuous CPM data sequence. This sequence reflects 14 CO 2 the changing trend of the radioactive event count over time

[0036] Through the above steps, the server realizes receiving continuous count data from the measuring device and converting it into a more intuitive count of radioactive events per minute, CPM. This lays the foundation for subsequent data processing and analysis. The CPM data sequence will be used to determine when the measurement enters a stable plateau region to optimize the detection time. At the same time, the CPM data can also be converted into other units such as DPM (decays per minute) according to needs for comparison with clinical diagnostic criteria

[0037] Step S102: Calculate the normalized difference between the first count data and the second count data, where the first count data and the second count data are any two adjacent count data among multiple count data

[0038] In step S102, calculating the normalized difference between the first count data and the second count data specifically includes: calculating the difference between the first count data and the second count data; obtaining the normalized difference based on the normalization factor and the difference, and the calculation formula for the normalized difference is: ; where is the normalized difference, CPM i is the second count data; CPM i-1 is the first count data

[0039] Specifically, after the server obtains multiple consecutive count data, it further calculates the normalized difference between two adjacent count data. The purpose of this step is to measure the changing trend of the count data and provide a basis for determining whether the measurement enters a stable plateau region. Specifically, the server first selects any two adjacent count data from the obtained count data sequence, denoted as the first count data CPM i-1 and the second count data CPM i, where i represents the i-th count data. Then, the server calculates the difference between these two count data, i.e., CPM i - CPM i-1 . Considering that the count levels of different samples may vary greatly, directly using the difference may not accurately reflect the relative change trend of the counts. To eliminate the influence of the count level difference, the server introduces a normalization factor . The difference is divided by this normalization factor to obtain the normalized difference ∇i. The normalized difference ∇i reflects the degree of change of the i-th count data relative to the previous count data, and this change is relative to the current count level. The normalization process makes the data under different samples and different count levels comparable, which helps subsequent threshold judgment.

[0040] The server calculates the normalized difference for each pair of adjacent count data obtained, forming a sequence of normalized differences. When several consecutive normalized differences are small, it indicates that the change of the count data tends to be gentle and may have entered a stable plateau region. The server will use the sequence of normalized differences and compare it with a preset threshold to automatically judge whether the measurement has entered the plateau region to optimize the detection time.

[0041] Step S103: Compare the normalized difference with the preset threshold to determine whether it has entered the plateau region.

[0042] In a possible implementation manner, before step S103, the method further includes: obtaining a coefficient-related threshold corresponding to the first count data based on a preset CPM time curve; obtaining a clinical determination threshold corresponding to the first count data according to historical clinical diagnosis results; adding the coefficient-related threshold and the clinical determination threshold to obtain the preset threshold.

[0043] Specifically, the server obtains a coefficient-related threshold corresponding to the first count data based on a preset CPM time curve. The CPM time curve describes 14 CO 2 a theoretical model of the change of radioactive event counts over time. It gives the count levels that should be observed at different time points according to the physical laws of radioactive isotope decay and the biological mechanism of the breath test. By substituting the time point of the first count data into the CPM time curve, the server can obtain a theoretical prediction value, that is, the expected count level at this time point. Then, the server multiplies this theoretical prediction value by an empirical coefficient k1 to obtain the coefficient-related threshold. The empirical coefficient k1 is determined based on a large amount of historical data and expert experience, and it reflects the possible deviation between the theoretical model and the actual observation. Introducing the empirical coefficient can adjust the theoretical threshold to a certain extent to make it more in line with the actual situation.

[0044] For example, assume that according to the CPM time curve, the expected count level at the 100th second is 120 CPM. If the empirical coefficient k1 is 0.9, then the coefficient-related threshold is 120 × 0.9 = 108 CPM. This means that according to the theoretical model and empirical adjustment, around the 100th second, when the count level reaches 108 CPM, it can be regarded as entering the stable plateau region.

[0045] In another possible implementation, the calculation formula for the coefficient-related threshold is Th(k)=k 1 , k 1 is a constant, and the value is obtained based on the relative standard deviation (coefficient of variation) CV of radiation measurement. In the field of radioactive measurement, when the total count of the radiation source is very large: ; N is the total number of 14 CO 2 radioactive events measured by the measuring device.

[0046] Secondly, the server obtains the clinical determination threshold corresponding to the first count data according to the historical clinical diagnosis results. The historical clinical diagnosis results include the breath test data of a large number of confirmed Helicobacter pylori infection cases and the corresponding diagnosis results (positive or negative). By analyzing these data, the association between the count level and the diagnosis result can be found, and then a clinical determination threshold can be determined.

[0047] In a possible implementation, according to the historical clinical diagnosis results, the clinical determination threshold corresponding to the first count data is obtained, and the specific calculation formula is ; where k 2 is a constant coefficient, 0 < k 2 < 1, CPMi is the CPM value at the current Ti seconds, BG is the instrument background, and DT is the instrument judgment threshold.

[0048] Specifically, the server first obtains the breath test data of a large number of confirmed cases, including the count levels at different time points and the final diagnosis results (positive or negative). Then, the server analyzes and models these data to obtain a mathematical formula for calculating the clinical determination threshold at a given count level. In this embodiment, the server uses the following formula to calculate the clinical determination threshold:

[0049] where Th(CPM i )) represents the clinical determination threshold when the count level is CPM i , k 2 is a constant coefficient, satisfying 0 < k 2 < 1, CPM iCPMi is the counting level at the i-th second, BG is the instrument background, and DT is the instrument judgment threshold. This formula is based on the form of the Gaussian function (normal distribution function) and describes the non-linear relationship between the clinical judgment threshold and the counting level. The parameter k in the formula 2 , BG, and DT are all determined based on historical data and expert experience, and they reflect the influence of different factors on clinical judgment.

[0050] Specifically, CPM i represents the actual counting level observed at the i-th second, which is the basis for calculating the clinical judgment threshold. BG represents the instrument background, that is, the basic counting level of the instrument when there is no radioactive sample. DT represents the instrument judgment threshold, that is, the counting level difference at which the instrument can reliably distinguish positive and negative samples. Both BG and DT are inherent properties of the instrument and can be obtained through instrument calibration and performance testing.

[0051] The constant coefficient k 2 plays a role in adjusting the overall level of the clinical judgment threshold. The value range of k 2 is between 0 and 1, and it determines the sensitivity of the clinical judgment threshold to changes in the counting level. The larger the value of k 2 , the more sensitive the clinical judgment threshold is to changes in the counting level, that is, it can be judged as positive at a lower counting level; conversely, the smaller the value of k 2 , the less sensitive the clinical judgment threshold is to changes in the counting level, and a higher counting level is required to judge as positive. The specific value of k 2 needs to be adjusted according to historical data and clinical experience to achieve the best diagnostic effect.

[0052] For example, assume that the background BG of a certain instrument is 20 CPM, the judgment threshold DT is 50 CPM, and the constant coefficient k 2 takes 0.8. If the counting level CPM 300 observed at the 300th second is 180 CPM, then the clinical judgment threshold calculated according to the formula is: ; this means that at the 300th second, if the counting level reaches 180 CPM and the normalized difference is less than or equal to 0.107, the sample can be judged as positive.

[0053] In step S103, the normalized difference is compared with a preset threshold to determine whether to enter the plateau region, specifically including: If it is determined that the normalized difference and the preset threshold satisfy , then it is determined to enter the plateau region, where is the normalized difference at the i-th second, and n is the number of CPM values; If it is determined that the normalized difference and the preset threshold do not satisfy , then it is determined not to enter the plateau region.

[0054] Specifically, the server first calculates the average of the normalized differences over a period of time as a reference for the current normalized difference. Specifically, the server selects the normalized differences at the n time points before the current time point and calculates their arithmetic mean. This mean reflects the overall trend of the change in the count data over the recent period of time. Then, the server compares the preset threshold at the current time point with the average of the normalized differences. The preset threshold is determined comprehensively based on the theoretical model and clinical data, representing the critical condition for determining entry into the plateau region at the current time point. If the difference between the preset threshold and the average of the normalized differences is greater than 0, that is, the inequality is satisfied: ; where Thi is the preset threshold at the i-th second, ∇ i-j is the normalized difference at the (i - j)-th second, and n is the number of normalized differences participating in the averaging. If the above inequality holds, the server determines that the measurement has entered the plateau region at the i-th second. Intuitively, this means that the current normalized difference has been less than the preset threshold for n consecutive times, and the change in the count data tends to be flat, and it can be considered that the measurement has reached a stable state.

[0055] For example, assume that n is taken as 3, that is, the average of the 3 normalized differences before the current time point is compared. At the 100th second, the preset threshold Th 100 is 0.2, and the first 3 normalized differences are: ∇ 97 = 0.15; ∇ 98 = 0.12; ∇ 99 = 0.10; then the average of the normalized differences is: (0.15 + 0.12 + 0.10) / 3 = 0.123; substituting the preset threshold and the average of the normalized differences into the inequality, we get: 0.2 - 0.123 = 0.077 > 0; therefore, the server determines that the measurement has entered the plateau region at the 100th second.

[0056] Conversely, if the inequality does not hold, that is, the difference between the preset threshold and the average of the normalized differences is less than or equal to 0, the server determines that the measurement has not entered the plateau region. This means that the change in the current count data is still large and continuous monitoring is required.

[0057] In practical applications, the server can adjust the value of n and the form of the inequality according to specific requirements. The larger the value of n, the stricter the condition for determining entry into the plateau region, because more consecutive normalized differences need to be less than the preset threshold. The inequality can also be rewritten in other forms, such as considering the weighted average of the normalized differences, or introducing other statistical quantities such as variance. These adjustments are all to find the optimal plateau region determination condition under different detection objects and detection environments.

[0058] In a possible implementation, after step S103, the method further includes: if it is not determined to enter the plateau region within a preset time period, obtaining the count data at the end of the preset time period as the final measurement result; converting the final measurement result from the CPM unit to the DPM unit; based on a preset Helicobacter pylori concentration correspondence table, obtaining the Helicobacter pylori concentration corresponding to the final measurement result, and outputting the Helicobacter pylori concentration.

[0059] Specifically, the server sets a preset time period as the maximum duration of the measurement. The length of the preset time period is determined comprehensively according to factors such as instrument performance, sample characteristics, and clinical requirements, generally ranging from a few minutes to dozens of minutes. During the preset time period, the server continuously monitors the count data and determines whether to enter the plateau region. If the measurement enters the plateau region within the preset time period, the server uses the count data at the time of entering the plateau region as the final measurement result. This means that the count level has reached a stable state at this time, and continuing the measurement will not provide additional useful information. If the measurement does not enter the plateau region throughout the preset time period, the server uses the count data at the end of the preset time period as the final measurement result. This is a safeguard measure to ensure that even if the ideal stable state is not reached, a usable measurement value can be obtained within the specified time.

[0060] After obtaining the final measurement result, the server converts it from the original CPM unit to the DPM unit. CPM represents the count per minute, while DPM represents the decay count per minute. Due to factors such as counting efficiency, the CPM value is generally less than the actual decay count of the sample. By multiplying by a preset conversion coefficient, the server can convert the CPM value to the DPM value to more accurately reflect the radioactivity level of the sample.

[0061] Finally, the server obtains the Helicobacter pylori concentration corresponding to the final measurement result based on the preset Helicobacter pylori concentration correspondence table and outputs this concentration value. The Helicobacter pylori concentration correspondence table is established based on a large amount of clinical data and expert experience, and it gives the Helicobacter pylori infection degree corresponding to different DPM value ranges.

[0062] Step S104: If it is determined to enter the plateau region, determine the measurable time and control the measurement device to stop measuring.

[0063] In step S104, when the server determines that the measurement has entered the plateau region based on the comparison result between the normalized difference and the preset threshold, it determines the time point at this moment as the measurable time. The measurable time refers to the time point when the measurement result has reached stability and can be used as the final result. In this embodiment, the measurable time is the starting time point of the plateau region. After determining the measurable time, the server immediately sends an instruction to stop the measurement to the measuring device. After receiving the instruction, the measuring device stops collecting and counting the exhaled sample, ending the current measurement. The purpose of this step is to minimize the measurement time while achieving the optimal measurement result and improve the detection efficiency.

[0064] For example, assume that the server determines that the measurement has entered the plateau region at the 300th second. Then it determines the 300th second as the measurable time and sends an instruction to stop the measurement to the measuring device. The measuring device stops the measurement, and the current detection ends.

[0065] Step S105: Use the target count data corresponding to the measurable time as the final measurement result and output the final measurement result.

[0066] In step S105, the server uses the target count data corresponding to the measurable time as the final measurement result. The target count data refers to the count value actually obtained by the measuring device at the measurable time point. Since the measurable time is the starting time point of the plateau region, the count data at this time has reached a stable state and can be used as the final measurement result. The server reads the count value at the measurable time point from the data cache of the measuring device and determines it as the final measurement result. Then, the server outputs this final measurement result and provides it to the doctor or other relevant personnel. The output form can be digital display, chart presentation, diagnostic report, etc., to meet different usage requirements.

[0067] Continuing with the above example, assume that the actual count value obtained by the measuring device at the 300th second, which is the measurable time point, is 180 CPM. The server then uses 180 CPM as the final measurement result and outputs this result in an appropriate form.

[0068] Refer to Figure 2, this application also provides a device for optimizing the detection time of Helicobacter pylori. The device is a server, and the server includes an acquisition module 201 and a processing module 202, where: The acquisition module 201 is used to acquire a plurality of consecutive count data received from a measuring device, and the count data represents the count of 14CO2 radioactive events measured by the measuring device per minute; The processing module 202 is used to calculate the normalized difference between the first count data and the second count data. The first count data and the second count data are any two adjacent count data among the plurality of count data; The processing module 202 is further used to compare the normalized difference with a preset threshold to determine whether to enter the plateau region; The processing module 202 is further used to, if it is determined that the plateau region is entered, determine the measurable time and control the measuring device to stop measuring; The processing module 202 is further used to use the target count data corresponding to the measurable time as the final measurement result and output the final measurement result.

[0069] In a possible implementation manner, when the processing module 202 calculates the normalized difference between the first count data and the second count data, it specifically includes: The processing module 202 calculates the difference between the first count data and the second count data; The processing module 202 obtains the normalized difference according to the normalization factor and the difference, and the calculation formula of the normalized difference is:

[0070] where, is the normalized difference, CPM i is the second count data; CPM i-1 is the first count data.

[0071] In a possible implementation manner, before the processing module 202 compares the normalized difference with a preset threshold to determine whether to enter the plateau region, the method further includes: The acquisition module 201 obtains a coefficient-related threshold corresponding to the first count data based on a preset CPM time curve; The processing module 202 obtains a clinical determination threshold corresponding to the first count data according to historical clinical diagnosis results; The processing module 202 adds the coefficient-related threshold and the clinical determination threshold to obtain a preset threshold.

[0072] In a possible implementation manner, when the acquisition module 201 obtains a clinical determination threshold corresponding to the first count data according to historical clinical diagnosis results, the specific calculation formula is

[0073] where, k 2 is a constant coefficient, 0 < k 2 < 1, CPM i is the CPM value at the current T i seconds, BG is the instrument background, and DT is the instrument judgment threshold.

[0074] In a possible implementation, the processing module 202 compares the normalized difference with a preset threshold to determine whether to enter the plateau region. Specifically, it includes: If it is determined that the normalized difference and the preset threshold satisfy , then it is determined to enter the plateau region, where is the normalized difference at the i-th second, and n is the number of CPM values; If it is determined that the normalized difference and the preset threshold do not satisfy , then it is determined not to enter the plateau region.

[0075] In a possible implementation, after the processing module 202 compares the normalized difference with the preset threshold to determine whether to enter the plateau region, the method further includes: If the processing module 202 does not determine to enter the plateau region within a preset time period, it obtains the count data at the end of the preset time period as the final measurement result; the processing module 202 converts the final measurement result from CPM unit to DPM unit; the processing module 202 obtains the Helicobacter pylori concentration corresponding to the final measurement result based on a preset Helicobacter pylori concentration correspondence table, and outputs the Helicobacter pylori concentration.

[0076] In a possible implementation, obtaining a plurality of consecutive count data received from the measuring device, the specific calculation formula is:

[0077] where i is the i-th second, Ni is the 14 CO2 radioactive event count read by the measuring device per second, and T is the preset time.

[0078] It should be noted that: When the device provided in the above embodiment realizes its functions, only the above-mentioned division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0079] This application also provides an electronic device. Referring to Figure 3 , Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0080] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0081] Among them, the user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may further include standard wired interfaces and wireless interfaces.

[0082] Among them, the network interface 304 may optionally include standard wired interfaces and wireless interfaces (such as WI-FI interfaces).

[0083] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305, the processor 301 executes various functions of the server and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately through a single chip.

[0084] Among them, the memory 305 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 305 may further be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3, in the memory 305, which is a computer storage medium, there may be included an operating system, a network communication module, a user interface module, and an application program of a method for optimizing the detection time of Helicobacter pylori.

[0085] In Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input data and obtain the data input by the user; while the processor 301 can be used to call the application program of a method for optimizing the detection time of Helicobacter pylori stored in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the above embodiments. It should be noted that for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0086] The present application also provides a computer-readable storage medium storing instructions. When executed by one or more processors 301, the electronic device 300 is caused to execute one or more of the methods as described in the above embodiments.

[0087] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0088] In several implementation manners provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect couplings or communication connections of the devices or units can be in an electrical or other form.

[0089] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0091] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0092] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and the practice of the present disclosure, those skilled in the art will easily think of other implementation manners of the present disclosure.

[0093] The present application aims to cover any variations, uses, or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include the common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for optimizing the detection time of Helicobacter pylori, characterized in that: The method comprises: Acquire a plurality of continuous counting data received from a measuring device, wherein the counting data represents the number of times the measuring device measures each minute. 14 CO2 radioactive event counting; Calculating a normalized difference between first count data and second count data, where the first count data and the second count data are any two adjacent count data among the plurality of count data; Compare the normalized difference with a preset threshold to determine whether the area has entered a plateau; If it is determined that the plateau has been entered, it is determined that the measurable time has been reached, and the measuring device is controlled to stop measuring; The target counting data corresponding to the measurable time is taken as a final measurement result, and the final measurement result is output.

2. The method according to claim 1, characterized in that: The calculating of the normalized difference between the first count data and the second count data specifically includes: Calculating a difference between the first counting data and the second counting data; According to the normalization factor and the difference, the normalized difference is obtained, and the calculation formula of the normalized difference is: ； in, is the normalized difference, CPM i is the second counting data; CPM i-1 is the first counting data.

3. The method according to claim 1, characterized in that Before comparing the normalized difference with a preset threshold to determine whether a plateau has been entered, the method further includes: Based on a preset CPM time curve, obtaining a coefficient-related threshold value corresponding to the first counting data; According to historical clinical diagnosis results, obtaining a clinical judgment threshold corresponding to the first count data; The coefficient-related threshold is added to the clinical judgment threshold to obtain the preset threshold.

4. The method according to claim 3, characterized in that: According to the historical clinical diagnosis results, the clinical judgment threshold corresponding to the first counting data is obtained, and the specific calculation formula is: ； Among them, k2 is a constant coefficient, 0 <k2<1,CPM i For the current T i BG is the instrument background, and DT is the instrument judgment threshold.

5. The method according to claim 4, characterized in that The comparing the normalized difference with a preset threshold to determine whether the plateau is reached specifically includes: If it is determined that the normalized difference and the preset threshold meet , then it is determined to enter the flat area, where is the normalized difference at the ith second, and n is the number of CPM values; If it is determined that the normalized difference does not satisfy the preset threshold , it is determined that you have not entered the Ping area.

6. The method according to claim 1, characterized in that After comparing the normalized difference with a preset threshold to determine whether the plateau has been entered, the method further includes: If it is not determined that the vehicle enters the plateau area within the preset time period, the counting data at the end of the preset time period is obtained as the final measurement result; Convert the final measurement result from CPM to DPM; Based on a preset Helicobacter pylori concentration correspondence table, the Helicobacter pylori concentration corresponding to the final measurement result is obtained, and the Helicobacter pylori concentration is output.

7. The method according to claim 1, characterized in that The specific calculation formula for obtaining a plurality of continuous counting data received from the measuring device is: ； Among them, i is the i-th second, N i The readings of the measuring device per second 14 CO2 radioactive event count, T is the preset time.

8. A device for optimizing the detection time of Helicobacter pylori, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire a plurality of continuous counting data received from a measuring device, wherein the counting data represents the number of times the measuring device measures each minute. 14 CO2 radioactive event counting; The processing module (202) is used to calculate a normalized difference between first counting data and second counting data, wherein the first counting data and the second counting data are any two adjacent counting data among the plurality of counting data; The processing module (202) is further used to compare the normalized difference with a preset threshold value to determine whether the plateau area has been entered; The processing module (202) is further configured to determine that the measurable time has been reached if it is determined that the plateau has been entered, and control the measuring device to stop measuring; The processing module (202) is further configured to use the target counting data corresponding to the measurable time as a final measurement result, and output the final measurement result.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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