Diagnosis and treatment auxiliary system and method for obstetrics and gynecology department

Abstract

The invention relates to the technical field of diagnosis, in particular to a diagnosis and treatment auxiliary system and method for the obstetrics and gynecology department, and the system comprises a parameter collection module, a feature offset recognition module, a feature reconstruction module, a collaborative trend recognition module and a diagnosis and treatment suggestion generation module. According to the system and the method, the body temperature, the heart rate, the pulse, the blood pressure and the image edge density are synchronously acquired and the timestamps are bound, so that a structured diagnosis and treatment data unit is formed, the time sequence comparison capability of multi-source information is effectively improved, and potential risk points can be positioned and feature tags can be constructed based on a numerical value offset identification and synchronous index combined judgment mechanism; according to the method, physical signs, images and laboratory data are normalized and fused into three-dimensional vectors, cross-dimensional feature linkage expression is achieved, the dynamic insight ability to pathological state changes is improved by screening features with large variation amplitude and long duration time and judging trend consistency, and the accurate response to the fetal risk state is enhanced by combining a specific trend combination matching rule.

Description

Technical Field

[0001] The present invention relates to the field of diagnostic technologies, and in particular, to an obstetrics and gynecology diagnosis and treatment assistance system and method. Background Art

[0002] The field of diagnostic technologies involves the process of identifying and determining the physiological state, biochemical indicators, or functional characteristics of the human body, and is widely used in the early detection of diseases, clinical evaluation, and health management. The core content of this technical field includes obtaining human-related information through means such as medical images, physical sign collection, bioelectrical signals, and physiological parameter measurement, and performing classification, comparison, and diagnostic judgment based on medical standards. The technical system covered by this field mainly consists of information collection, signal recognition, data analysis, and auxiliary judgment, and is widely used in the detection and auxiliary diagnosis scenarios of diseases in multiple systems such as cardiovascular, respiratory, nervous, endocrine, and reproductive systems.

[0003] Among them, the obstetrics and gynecology diagnosis and treatment assistance system refers to a comprehensive assistance plan designed for medical diagnosis activities related to the pregnancy and childbirth stages of women, involving multiple aspects such as pregnancy status monitoring, fetal health assessment, and childbirth risk warning, mainly through methods such as fetal heart signal acquisition and analysis, uterine contraction activity measurement, and reproductive tract physiological parameter monitoring. In clinical applications, this system often combines electronic fetal monitoring devices to obtain fetal heart rate and uterine contraction intensity data, and makes an auxiliary judgment on the status of fetal distress or the progress of labor by setting parameter thresholds or model calculation results, and is supplemented by reproductive health history data analysis and dynamic recording mechanisms to improve the pertinence and systematicness of diagnosis and treatment.

[0004] Existing technologies have limitations in data structure integration and trend analysis. The collected information is mostly single-category static indicators, lacking a linkage recognition mechanism between multi-dimensional features, and it is difficult to reflect the complex pathological evolution process. In the absence of time series binding and collaborative analysis conditions, the identification of abnormal nodes relies on single-threshold judgment, which is prone to inaccurate risk warning problems. For example, although the fetal heart rate does not exceed the boundary but changes with other indicators, the system is difficult to detect potential abnormalities, which is prone to judgment delay and intervention lag, affecting the efficiency of clinical treatment. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose an obstetrics and gynecology diagnosis and treatment assistance system and method.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: An obstetrics and gynecology diagnosis and treatment assistance system includes: A parameter acquisition module obtains the body temperature, heart rate, pulse, and blood pressure of a patient during obstetric monitoring, collects and records time stamps, extracts the edge density of the synchronous two-dimensional ultrasound image area, binds it with the physiological data and sets a dynamic identifier, and generates a set of bound diagnosis and treatment data units; The feature offset recognition module calls the bound diagnosis and treatment data unit set to collect the fetal heart rate value and compares it with the fetal heart rate variation threshold. If it exceeds the threshold, it is marked as a warning node. At the same time, the corresponding pulse, blood pressure, and image edge density are extracted to judge the synchronous change, and an obstetric risk warning feature label group is generated. Based on the fetal heart rate, edge density value, and estradiol value in the obstetric risk warning feature label group, the feature reconstruction module classifies them into the physical sign class, imaging class, and laboratory class feature groups respectively, and performs normalization processing according to the within-group acquisition standard deviation. If the three types of features appear simultaneously at the warning node, they are integrated into the form of a three-dimensional vector to generate an input body for obstetrics and gynecology feature fusion. The collaborative trend recognition module calls the three indicators in the input body for obstetrics and gynecology feature fusion, screens the one with the largest change amplitude and the longest duration, judges the direction consistency and the time window overlap ratio, and generates a multi-dimensional feature collaborative trend label.

[0007] As a further solution of the present invention, the bound diagnosis and treatment data unit set includes a dynamic identifier, physiological data image synchronization binding information, and a timestamp mark. The obstetric risk warning feature label group is specifically an abnormal recognition mark, a synchronous change parameter group, and a risk feature identifier. The input body for obstetrics and gynecology feature fusion includes a three-dimensional vector of the physical sign class, a three-dimensional vector of the imaging class, and a three-dimensional vector of the laboratory class. The multi-dimensional feature collaborative trend label is specifically a change amplitude index, a duration parameter, a direction consistency mark, and a time window overlap ratio.

[0008] As a further solution of the present invention, the parameter acquisition module includes: The physiological data acquisition sub-module collects the patient's body temperature, heart rate, pulse, and blood pressure during obstetric monitoring, records the timestamp of each data item, classifies and stores them, calculates the data change rate and fluctuation amplitude, obtains the change intervals of multiple data items, and generates a physiological parameter change interval. The image region extraction sub-module calls the synchronous ultrasound image, extracts the edge information of the fetal region, calculates the image gray-scale edge density, screens the regions that meet the reference density, and obtains the synchronous edge density value. The diagnosis and treatment unit generation sub-module calls the physiological parameter change interval and the synchronous edge density value, calculates the relationship between the physiological fluctuation and the image edge, extracts the bound data, and uses the formula: ; Performs arithmetic operations to obtain the interactive mapping difference degree value and generates a bound diagnosis and treatment data unit set. Among them, represents the group of physiological parameter fluctuation values, represents the group of image edge densities, represents the group of image edge gray-scale base differences, represents the Actual boundary fitting value of the group image area Represents the number of timestamp groups participating in the matching, and M is the interactive mapping difference value.

[0009] As a further solution of the present invention, the feature offset recognition module includes:[[]] The fetal heart rate early warning recognition sub-module calls the fetal heart rate values centralized in the bound diagnosis and treatment data unit, obtains the fetal heart rate fluctuation data in each monitoring period, and at the same time calculates the difference between the fluctuation range and the threshold based on the fetal heart rate variability threshold, marks the periods exceeding the threshold, and obtains the early warning mark node value; The synchronous change feature extraction sub-module extracts the pulse, blood pressure and image edge density data at the corresponding time nodes according to the early warning mark node value, calculates the change rate and the average change amount, and judges whether there is synchronous fluctuation to obtain the synchronous fluctuation analysis value; The obstetric risk label generation sub-module is based on the synchronous fluctuation analysis value and uses the formula: ; Operate to obtain the change intensity of the fetal heart rate and pulse, the composite fluctuation index under the influence of image features, and combine the consistency of the synchronous change direction to generate an obstetric risk early warning feature label group; Among them, Represents the change amplitude of the fetal heart rate, Represents the change amplitude of the pulse, Indicates the Group blood pressure value, Indicates the Group image edge density value, Indicates the Group image edge density change value, Is the reference value, Is the number of time nodes, Is the composite fluctuation index.

[0010] As a further solution of the present invention, the feature reconstruction module includes:[[]] The physical sign classification sub-module obtains the continuous measurement value sequence at multiple time nodes based on the fetal heart rate data in the obstetric risk early warning feature label group, calculates the standard deviation and performs within-group standard deviation normalization processing, classifies it into the physical sign class feature group, and generates the physical sign class normalization value; The image classification sub-module obtains the pixel distribution area of the edge density value in the image frame, extracts the maximum value and the average value, calculates its standard deviation and uses within-group standard deviation normalization, and classifies it into the image class feature group to obtain the image class normalization value; The multi-source feature fusion sub-module calls the physical sign class normalization value and the image class normalization value, obtains the measurement data of the estradiol value, calculates the coefficient of variation and eliminates the abnormal points, and uses the formula: ; Integrate the three types of normalized values, determine whether they co-occur in the same warning node. If they co-occur, combine them in the form of a three-dimensional vector to generate a feature fusion input body; Among them, represents the coefficient of variation after the fusion of three types of features, represents the coefficient of variation of the normalized values of physical signs, represents the mean value of the time series of physical signs, represents the skewness of the time series of physical signs, represents the kurtosis of the time series of physical signs, represents the coefficient of variation of the normalized values of imaging features, represents the coefficient of variation of the normalized value of estradiol, represents the absolute value operation to solve the difference between the coefficient of variation of estradiol value and the mean value of physical signs.

[0011] As a further solution of the present invention, the co-trend recognition module includes: The amplitude screening sub-module calculates the sequence of change values of multiple indicators within the time window based on the three indicators in the obstetrics and gynecology feature fusion input body, compares the amplitudes of the maximum fluctuation intervals, and screens the one with the largest amplitude value to generate the maximum amplitude trend value; The time correlation sub-module calls the change time window corresponding to the maximum amplitude trend value, calculates the overlap ratio with the other two indicators in the obstetrics and gynecology feature fusion input body, and screens the time period with a high overlap ratio to generate the time window overlap interval value; The trend determination sub-module determines the direction consistency within the time window according to the change directions of the indicators included in the time window overlap interval value. If the directions are consistent, it is recorded as positive, and if there is a conflict, it is negative. Combine the change amplitude and the overlap ratio to calculate the trend scalar, using the formula: ; By calculating the trend co-degree value and performing threshold judgment, a multi-dimensional feature co-trend mark is obtained; Among them, represents the multi-dimensional feature co-trend mark, represents the continuous change amplitude of the th indicator, represents the time window overlap ratio between the th indicator and the target indicator, represents the consistency matching value of the change direction between the th indicator and the remaining indicators in the obstetrics and gynecology feature fusion input body, is the number of indicators participating in the trend judgment.

[0012] As a further solution of the present invention, the system further includes: The diagnosis and treatment advice generation module collaborates with the combined trends of elevated fetal heart rate, expanded marginal density, and decreased estradiol in the multi-dimensional feature collaborative trend marking to match the conditional items in the advice list, trigger the fetal intrauterine distress monitoring condition, and generate obstetrics and gynecology diagnosis and treatment assistance advice items; The obstetrics and gynecology diagnosis and treatment assistance advice items include monitoring advice types, intervention advice types, and early warning level identifiers.

[0013] As a further solution of the present invention, the diagnosis and treatment advice generation module includes: The collaborative trend analysis sub-module extracts the difference between the fetal heart rate baseline value and the preset safe baseline range based on the multi-dimensional feature collaborative trend marking, calculates the deviation amplitude between the growth gradient of the marginal density per unit time and the critical expansion rate, fits the linear decline slope of the estradiol concentration change rate, performs weighted assignment on the difference, deviation amplitude, and slope, and then performs superposition operation to generate a collaborative trend response value; The monitoring condition matching sub-module calls the upper limit threshold of the fetal heart rate safety, the critical threshold of the marginal density expansion, and the lower limit threshold of the estradiol fluctuation, compares the fetal heart rate difference in the collaborative trend response value with the upper safety limit, compares the marginal density deviation amplitude with the critical rate, and compares the estradiol slope with the lower fluctuation limit. According to the combined trigger logic of the simultaneous over-limit of the three parameters, a monitoring trigger determination result is generated; The intervention advice formulation sub-module, based on the monitoring trigger determination result, calls the standard operation parameters of fetal heart rate monitoring and the maternal oxygen supply classification index, divides the monitoring frequency level according to the fetal heart rate difference, integrates the estradiol slope to adjust the oxygen supply flow threshold, and defines the cesarean section priority parameter in combination with the marginal density deviation amplitude to generate fetal intrauterine distress diagnosis and treatment advice items.

[0014] An obstetrics and gynecology diagnosis and treatment assistance method, which is executed based on the above-mentioned obstetrics and gynecology diagnosis and treatment assistance system, includes the following steps: S1: Obtain the physiological data including the patient's body temperature, heart rate, pulse, and blood pressure in obstetric monitoring, record the time stamp, extract the regional marginal density of the synchronous two-dimensional ultrasound image, bind it with the physiological data and dynamically identify it to generate a bound diagnosis and treatment data unit set; S2: Compare the fetal heart rate data in the bound diagnosis and treatment data unit set with the fetal heart rate variability threshold. If it exceeds the threshold, mark it as a warning node, extract the synchronous pulse, blood pressure, and image marginal density to generate an obstetric risk warning feature label group; S3: According to the fetal heart rate, marginal density, and estradiol value in the obstetric risk warning feature label group, classify them into physical sign type, imaging type, and laboratory type feature groups, perform standard deviation normalization. If the three types of features appear simultaneously at the warning node, integrate them into a three-dimensional vector to generate an obstetrics and gynecology feature fusion input body; S4: Invoke the three indicators in the obstetrics and gynecology feature fusion input body, screen the indicator with the largest change range and the longest duration, judge the direction consistency and the time window overlap ratio, and generate a multi-dimensional feature collaborative trend label; S5: According to the combination of fetal heart rate increase, edge density expansion, and estradiol decrease in the multi-dimensional feature collaborative trend label, match the conditional items, trigger the fetal intrauterine distress monitoring condition, and generate obstetrics and gynecology diagnosis and treatment assistance advice items.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by synchronously collecting body temperature, heart rate, pulse, blood pressure, and image edge density and binding time stamps, a structured diagnosis and treatment data unit is formed, effectively improving the time series comparison ability of multi-source information. Based on the numerical offset recognition and synchronous indicator joint judgment mechanism, potential risk points can be located and feature labels can be constructed. Normalize physical signs, images, and laboratory data and fuse them into a three-dimensional vector to achieve cross-dimensional feature linkage expression. By screening features with large change ranges and long durations and judging trend consistency, the dynamic insight ability into pathological state changes is improved. Combining specific trend combination matching rules enhances the accurate response to fetal risk states. Description of the Drawings

[0016] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the parameter acquisition module of the present invention; Figure 3 is the flow chart of the feature offset recognition module of the present invention; Figure 4 is the flow chart of the feature reconstruction module of the present invention; Figure 5 is the flow chart of the collaborative trend recognition module of the present invention; Figure 6 is the flow chart of the diagnosis and treatment advice generation module of the present invention. Detailed Embodiments

[0017] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0019] Embodiment 1: Please refer to Figure 1 , the present invention provides a technical solution: An obstetrics and gynecology diagnosis and treatment assistance system includes: The parameter acquisition module obtains the body temperature, heart rate, pulse and blood pressure of the patient during obstetric monitoring, collects and records the time stamp, extracts the edge density of the synchronous two-dimensional ultrasound image area, binds it with the physiological data and sets a dynamic identifier, and generates a set of bound diagnosis and treatment data units; The feature deviation recognition module calls the fetal heart rate value in the set of bound diagnosis and treatment data units and compares it with the fetal heart rate variability threshold. If it exceeds, it is marked as a warning node. At the same time, it extracts the corresponding pulse, blood pressure and image edge density to judge the synchronous change, and generates an obstetric risk warning feature label group; The feature reconstruction module classifies the fetal heart rate, edge density value and estradiol value in the obstetric risk warning feature label group into the physical sign class, imaging class and laboratory class feature groups respectively, and performs normalization processing according to the within-group acquisition standard deviation. If the three types of features appear simultaneously in the warning node, they are integrated into a three-dimensional vector form to generate an obstetrics and gynecology feature fusion input body; The collaborative trend recognition module calls the three indicators in the obstetrics and gynecology feature fusion input body, screens the one with the largest change amplitude and the longest duration, judges the direction consistency and the time window overlap ratio, and generates a multi-dimensional feature collaborative trend mark; The diagnosis and treatment recommendation generation module matches the condition items in the recommendation list according to the combination of increased fetal heart rate, expanded edge density and decreased estradiol in the multi-dimensional feature collaborative trend mark, triggers the monitoring conditions for fetal distress in utero, and generates obstetrics and gynecology diagnosis and treatment assistance recommendation items.

[0020] The set of bound diagnosis and treatment data units includes a dynamic identifier, the synchronous binding information of physiological data and images, and a time stamp mark. The obstetric risk warning feature label group specifically includes an abnormal recognition mark, a synchronous change parameter group, and a risk feature identifier. The obstetrics and gynecology feature fusion input body includes a three-dimensional vector of the physical sign class, a three-dimensional vector of the imaging class, and a three-dimensional vector of the laboratory class. The multi-dimensional feature collaborative trend mark specifically includes a change amplitude index, a duration parameter, a direction consistency mark, and a time window overlap ratio. The obstetrics and gynecology diagnosis and treatment assistance recommendation items include a monitoring recommendation type, an intervention recommendation type, and a warning level identifier.

[0021] Please refer to Figure 2 , the parameter acquisition module includes: The physiological data acquisition sub-module acquires the patient's body temperature, heart rate, pulse and blood pressure during obstetric monitoring, records the timestamp of each data item, classifies and stores it, calculates the data change rate and fluctuation range, obtains the change intervals of multiple data items, and generates the physiological parameter change interval; First, the physiological data acquisition sub-module uses sensors connected to specific parts of the parturient's body to continuously monitor and collect key physiological index data. For example, an electronic thermometer is used to measure the axillary temperature, and the body temperature value is obtained as 37.1°C. An electrocardiogram monitor is used to record the heart rate, and the current value is 85 beats per minute. A pulse oximeter is used to measure the fingertip pulse, and the current value is 88 beats per minute. An electronic sphygmomanometer is used to measure the upper arm blood pressure, and the systolic blood pressure is recorded as 125 mmHg and the diastolic blood pressure is 75 mmHg. At the same time, the system accurately stamps each piece of collected data with a timestamp.

[0022] Table 1 Example table of physiological data acquisition: ; As shown in Table 1, it shows some physiological data continuously collected within a short period of time and their timestamps. Then, these timestamped data are stored in the corresponding databases or memory areas according to the index types (body temperature, heart rate, pulse, blood pressure). Then, the module retrieves the continuous data points within a past period of time (for example, from 10:00:05 to 10:02:07) stored in the database, and calculates the change rate of each data item. For example, when calculating the heart rate change rate, the heart rate value of 87 beats per minute at 10:01:06 and the heart rate value of 85 beats per minute at 10:00:06 are extracted, and the difference is calculated to obtain a change amount of 2 beats per minute. Then, it is divided by the time interval of 1 minute to obtain a change rate of 2 beats per minute². Similarly, the change rates of other indicators are calculated, and the fluctuation range is calculated. For example, the maximum value (87 beats per minute) and minimum value (85 beats per minute) of the heart rate within this time period are found, and the difference is calculated to obtain a fluctuation range of 2 beats per minute. Based on these calculation results, the change intervals of each physiological data within the monitoring time period are determined. For example, the body temperature change interval is [37.0°C, 37.2°C], the heart rate change interval is [85 beats per minute, 87 beats per minute], the pulse change interval is [88 beats per minute, 90 beats per minute], the systolic blood pressure change interval is [125 mmHg, 128 mmHg], and the diastolic blood pressure change interval is [74 mmHg, 76 mmHg]. Finally, these intervals are integrated to generate the physiological parameter change interval.

[0023] The image region extraction sub-module calls the synchronous ultrasound image, extracts the edge information of the fetal region, calculates the grayscale edge density of the image, filters the regions that meet the benchmark density, and obtains the synchronous edge density value; Call the ultrasound image frame obtained synchronously with the physiological data acquisition time point (e.g., 10:00:06), and preprocess the image, such as grayscale conversion and median filtering for denoising. Then, use the Canny operator to process the image. This operator extracts all potential edge pixel point information in the image by calculating the image gradient, non-maximum suppression, and double-threshold connection to obtain a preliminary edge map. Then, calculate the gradient magnitude and direction of each pixel point in the image. Quantify the edge intensity based on the magnitude of the gradient value, and further calculate the gray edge density of a specific region (e.g., the position estimated by fetal biological parameters or the region of interest ROI manually framed). This density can be defined as the ratio of the number of pixels with a gradient magnitude greater than a certain threshold in the ROI to the total number of pixels in the ROI. For example, calculate that the preliminary edge density of the fetal region is 0.15. Set a reference density threshold, which is obtained through statistical analysis of a large number of normal ultrasound images. For example, analyze 1000 normal fetal ultrasound images of the same gestational age, calculate the edge density of their fetal regions, and obtain an average value of 0.11 and a standard deviation of 0.03. Set the reference threshold as the average value minus one standard deviation, that is or set a fixed value according to clinical needs, such as setting it to 0.10, to distinguish between fuzzy regions and clear regions, and screen out the regions in the image with an edge density greater than or equal to 0.10 as valid regions. For example, if the previously calculated 0.15 is greater than 0.10, then this region is screened out. Finally, obtain the edge density values of these screened regions at the synchronous time point as the synchronous edge density values. For example, record the synchronous edge density value as 0.15. ), such as or set a fixed value according to clinical needs, such as setting it to 0.10, to distinguish between fuzzy regions and clear regions, and screen out the regions in the image with an edge density greater than or equal to 0.10 as valid regions. For example, if the previously calculated 0.15 is greater than 0.10, then this region is screened out. Finally, obtain the edge density values of these screened regions at the synchronous time point as the synchronous edge density values. For example, record the synchronous edge density value as 0.15.

[0024] The sub-module of the diagnosis and treatment unit generation calls the physiological parameter change interval and the synchronous edge density value, calculates the relationship between physiological fluctuations and image edges, extracts the bound data, and uses the formula: ; Calculate through operations to obtain the interactive mapping difference degree value and generate a set of bound diagnosis and treatment data units; wherein, represents the physiological parameter fluctuation value of the th group, represents the image edge density of the th group, represents the gray edge base difference of the th group of images, represents the actual boundary fitting value of the th group of image regions, represents the number of timestamp groups participating in the matching, and M is the interactive mapping difference degree value.

[0025] Call the physiological parameter change range, such as the heart rate change range [85, 87] beats per minute, calculate its fluctuation value, such as taking the range amplitude , and call the synchronous edge density value obtained at the same time point . At the same time, it is also necessary to obtain the image edge gray base difference and the fitting value of the actual boundary of the image area . Represents the difference between the average gray value of the selected edge pixels and the average gray value of the adjacent non-edge background area. For example, if the average edge gray value is calculated as 150 and the average background gray value is 125, then . By performing shape matching on the extracted edge with the standard fetal anatomical boundary template (such as using the Hausdorff distance or Dice coefficient), and quantifying the matching degree. For example, if the matching degree is 0.8, then .

[0026] Suppose we select the data of the last 3 timestamps for matching, that is , and the data of each timestamp is as follows: Timestamp 1 (q = 1): , , , ; Timestamp 2 (q = 2): , , , (assuming that the heart rate fluctuation in the next minute is 3, and the edge density etc. also change); Timestamp 3 (q = 3): , , , (data of the next minute). Substitute these data into the formula: ; Perform the calculation. The detailed calculation process: Calculation of the numerator: Item 1 (q = 1): ; Item 2 (q = 2): ;

[0027] Item 3 (q = 3): ; Sum of the numerator = ; Calculation of the denominator: Item 1 (q = 1): ; Item 2 (q = 2): ; Item 3 (q = 3): ; Denominator = ; Calculate the value of M: ; The advantage of the formula is that by combining the volatility of physiological parameters ( ) with image features (edge density , gray - level base difference , boundary fitting degree ), and considering their internal relationships and the cumulative effect in the time series, it quantifies the degree of interactive mapping difference between multi - source information. The calculated value of the interactive mapping difference will be extracted and bound together with the original data such as to form a structured data unit containing timestamps, physiological fluctuations, image features, and their correlation metrics, generating a bound diagnosis and treatment data unit set. This result indicates that there is a certain correlation between physiological fluctuations and image features during this time period. The calculated is a quantitative indicator for subsequent module analysis.

[0028] Please refer to Figure 3 , the feature offset recognition module includes: The fetal heart rate early warning recognition sub - module calls the fetal heart rate values concentrated in the bound diagnosis and treatment data unit, obtains the fetal heart rate fluctuation data within each monitoring cycle, and at the same time calculates the difference between the fluctuation range and the threshold based on the fetal heart rate variability threshold, marking the cycles exceeding the threshold to obtain the early warning marker node values; First, the fetal heart rate early warning recognition sub - module extracts the concentrated fetal heart rate values (FetalHeartRate, FHR) within a specific time period (e.g., the past 15 minutes). Suppose the extracted FHR sequence (unit: beats per minute) is: [135, 138, 140, 142, 139, 145, 148, 150, 152, 155, 153, 150, 147, 144, 140], Table 2 Example of fetal heart rate (FHR) sequence and difference representation: ; As shown in Table 2, the FHR sequence, its adjacent differences, and the periodic fluctuations and sums are listed, and it is marked whether the warning threshold is triggered. The fetal heart rate fluctuation data within each monitoring period (for example, recorded once per minute) is obtained, that is, the difference sequence of FHR at adjacent time points: [3, 2, 2, -3, 6, 3, 2, 2, 3, -2, -3, -3, -3, -4]. At the same time, a fetal heart rate variability threshold needs to be set. This threshold is set with reference to the guidelines of the American College of Obstetricians and Gynecologists (ACOG) or based on statistical analysis of a large amount of historical data. For example, the normal short-term variability (STV) range is set to 5 - 25 beats per minute. Here, we focus on the single-beat change and the short-term cumulative change, and set a periodic variability threshold. For example, it is set that the sum of the absolute values of two consecutive FHR differences is greater than 10 beats per minute or the absolute value of a single change is greater than 8 beats per minute as the abnormal fluctuation threshold. Now, calculate the difference between the fluctuation range of each monitoring period (taking two consecutive FHR differences as a cycle unit, as shown in Table 2) and the threshold. For example, for cycle 1 (differences 3, 2), the fluctuation sum is 5, which is less than 10, and the maximum absolute value of a single difference is 3, which is less than 8, not exceeding the threshold. Assume that the data shown in Table 2 T6’ and T7’ appear in subsequent monitoring: the FHR sequence is […, 130, 145, 132, …], and the difference sequence is […, 15, -13]. At time point T6’, the absolute value of the single change is 15, which is greater than the threshold 8. Mark the time point (the moment when 145 appears) as the warning mark node. At time point T7’, the absolute value of the single change is 13, which is greater than the threshold 8, and the sum with the previous difference is |15| + |-13| = 28, which is greater than the threshold 10. Similarly, mark the time point (the moment when 132 appears) as the warning mark node. Assume that we identify that two nodes, time stamps T6’ and T7’, exceed the threshold, and obtain the warning mark node values T6’, T7’.

[0029] The synchronous change feature extraction sub-module extracts the pulse, blood pressure, and image edge density data at the corresponding time nodes according to the warning mark node values, calculates the change rate and the mean change amount, and determines whether there is synchronous fluctuation to obtain the synchronous fluctuation analysis value; According to the recognized warning mark node values T6' and T7', extract the corresponding maternal pulse values (e.g., 95 beats per minute at T6' and 92 beats per minute at T7'), blood pressure values (e.g., systolic blood pressure of 135 mmHg at T6' and 130 mmHg at T7'), and the previously calculated synchronous edge density values (e.g., 0.19 at T6' and 0.16 at T7') from the bound diagnosis and treatment data unit set. Then calculate the change rates of these parameters near the warning node. For example, calculate that within a short period (such as 1 minute) before and after T6', the pulse changes from 90 beats per minute to 95 beats per minute, with a rate of 5 beats per minute², the systolic blood pressure changes from 130 mmHg to 135 mmHg, with a rate of 5 mmHg per minute, and the edge density changes from 0.17 to 0.19, with a rate of 0.02 per minute. Also calculate the average change amount of these parameters within a time window (such as 5 minutes) containing the warning node. For example, within 5 minutes before and after the T6' node, the average pulse changes from 88 beats per minute to 92 beats per minute, with an average change amount of 4 beats per minute. By comparing whether the change directions of the fetal heart rate at the warning node (e.g., the FHR increases by 15 at T6') are consistent with those of other physiological parameters (the pulse increases by 5 and the blood pressure increases by 5) and image features (the edge density increases by 0.02), determine whether there is synchronous fluctuation. For example, at the T6' node, if the FHR, pulse, blood pressure, and edge density all show an upward trend, it is judged as synchronous fluctuation. Quantify this analysis result. For example, synchronous fluctuation is recorded as 1 and non-synchronous is recorded as 0, or assign a value between -1 and 1 according to the correlation coefficient. Assume that the synchronous fluctuation analysis value at the T6' node is 0.9. At the T7' node, the FHR decreases by 13, the pulse decreases by 3, the blood pressure decreases by 5, and the edge density decreases by 0.03, which is also judged as synchronous fluctuation (synchronous decrease), and the synchronous fluctuation analysis value is 0.8, obtaining the synchronous fluctuation analysis value sequence [0.9, 0.8].

[0030] The obstetric risk label generation sub-module, based on the synchronous fluctuation analysis value, uses the formula: ; Perform operations to obtain the composite fluctuation index of the fetal heart rate and pulse change intensity under the influence of image features, and combine the consistency of the synchronous change direction to generate an obstetric risk warning feature label group; Among them, represents the change amplitude of the fetal heart rate, represents the change amplitude of the pulse, represents the th group of blood pressure values, represents the th group of image edge density values, represents the th group of image edge density change values, is the reference value, is the number of time nodes, is the composite volatility index.

[0031] Based on these synchronous volatility analysis values, select the data of one of the nodes (such as the T6' node with higher risk) for calculation, using the formula: ; Parameter assignment and calculation: : The change range of fetal heart rate, referring to the change amount of a single FHR at the warning node T6'. . : The change range of pulse, referring to the change amount of a single pulse at the warning node T6'. . : The number of time nodes, taking 2 points before and after the T6' node, a total of time nodes, : The th group of blood pressure values (systolic pressure).

[0032] Assume The systolic pressure sequence of the points is [130, 132, 135, 133, 130] mmHg. : The th group of image edge density values. Assume The edge density sequence of the points is [0.17, 0.18, 0.19, 0.18, 0.16].[[]]END]] : The th group of image edge density change values (differences between adjacent points). The sequence is [0.01, 0.01, -0.01, -0.02]. To make up points, the first value is set to 0, and the sequence is [0, 0.01, 0.01, -0.01, -0.02].[[]]END]] : The image edge density reference value, with the setting basis the same as before, .

[0033] Detailed calculation process (taking the T6' node as an example, k = 5): Calculate the denominator : ; Calculate the second term : ; The second term = ; Calculate : ; The advantage of the formula is that it comprehensively evaluates the combined effects of multiple factors by integrating the fluctuation amplitudes of heart rate and pulse in the numerator, combining blood pressure and image edge density in the denominator, and quantifying the deviation of image feature changes in the second term. The composite fluctuation index of the change intensity of fetal heart rate and pulse obtained by the operation under the influence of image features , combined with the synchronous fluctuation analysis value calculated previously (the value at the T6' node is 0.9, indicating high synchrony and strong consistency in the change direction), after comprehensive judgment by the system, an obstetric risk warning feature label is generated, such as "FHR significantly accelerates with synchronous increase in blood pressure, pulse, and edge density, and the composite fluctuation index is medium", forming a group of obstetric risk warning feature labels. This result indicates that there is a certain composite fluctuation risk currently. Its numerical value can be compared with the preset risk level threshold (for example, low risk < 0.1, medium risk 0.1 - 0.2, high risk > 0.2), and it is initially judged as medium risk.

[0034] Please refer to Figure 4 , the feature reconstruction module includes: The physical sign classification sub-module obtains a sequence of continuous measurement values at multiple time nodes based on the fetal heart rate data in the obstetric risk warning feature label group, calculates the standard deviation and performs within-group standard deviation normalization, classifies it into the physical sign feature group, and generates a normalized physical sign value; First, the physical sign classification sub-module extracts the fetal heart rate data related to physical signs from it, especially the sequence of continuous fetal heart rate measurement values within the warning node and its nearby time periods. For example, for the previously identified T6' warning node, extract the FHR sequence of 10 time points before and after it: [138, 140, 142, 139, 130, 145, 132, 135, 138, 140] (unit: beats per minute), and calculate the standard deviation of this sequence , the mean value , the variance , the standard deviation beats per minute, and then perform within-group standard deviation normalization on this standard deviation. Using Z-score normalization, assume that the mean of the standard deviation of a group of historical FHR sequences is known as beats per minute, and the standard deviation is beats per minute (these values are obtained based on the statistical analysis of a large amount of historical monitoring data), then the normalized value is , and this normalized value is classified into the physical sign feature group to generate a normalized physical sign value, denoted as .

[0035] The image classification sub-module obtains the pixel distribution area of the edge density value within the image frame, extracts the maximum value and the average value, calculates its standard deviation and performs within-group standard deviation normalization, classifies it into the image feature group, and obtains a normalized image value; Retrieve the previously calculated edge density values in the ultrasound image frames synchronized with the vital sign data , analyze the pixel spatial distribution of these edge density values within the image frames, determine the regions where high density values are concentrated, extract the maximum value (e.g., Max_Ed = 0.20 near T6’) and the average value (e.g., Avg_Ed = 0.18) of the edge density within these regions, and then calculate the standard deviation of the edge density values in this region , for example, calculate to obtain . Similarly, perform within-group standard deviation normalization. Assume that the mean of the historical edge density standard deviation is , and the standard deviation is (based on historical image data statistics), then the image class normalization value is . This value is classified into the image class feature group to obtain the image class normalization value, denoted as .

[0036] The multi-source feature fusion sub-module calls the vital sign class normalization value and the image class normalization value, obtains the measurement data of estradiol values, calculates the coefficient of variation and eliminates outliers, using the formula: ; Integrate the three types of normalization values, and determine whether they co-occur at the same warning node. If they do, combine them in the form of a three-dimensional vector to generate the feature fusion input body; Among them, represents the coefficient of variation after the fusion of the three types of features, represents the coefficient of variation of the vital sign class normalization value, represents the mean of the vital sign class time series, represents the skewness of the vital sign class time series, represents the kurtosis of the vital sign class time series, represents the coefficient of variation of the image class normalization value, represents the coefficient of variation of the estradiol normalization value, represents the absolute value operation to solve the difference between the coefficient of variation of the estradiol value and the mean of the vital signs.

[0037] Call the previously obtained vital sign class normalization value and the image class normalization value , and additionally obtain the measurement data of the maternal estradiol (Estradiol, E2) level within the same time period. Estradiol is an important endocrine index reflecting placental function, Table 3 Example of estradiol (E2) measurement sequence: ; As shown in Table 3, assume that the obtained E2 measurement value sequence, with the last point adjusted to 4300 to reflect the downward trend, calculate the coefficient of variation (CV) of this sequence, the mean value pg / mL; variance ; standard deviation pg / mL, then ; Before calculation, check whether the data points 4400 and 4300 are outliers. Assume that according to the historical data distribution, they are still within a reasonable range and do not need to be excluded. Then normalize the E2 data, calculate the Z-score. Assume that the mean value of the historical E2 coefficient of variation is , the standard deviation is (based on historical data statistics), then the coefficient of variation of the estradiol normalized value (use the normalized CV value to replace).

[0038] ; Then use the formula: ; Perform integrated calculation, parameter assignment and calculation: : represented by . . : the mean value of the physical sign time series (FHR series), previously calculated . : the skewness of the physical sign time series (FHR series [138, 140, 142, 139, 130, 145, 132, 135, 138, 140]). Calculated to get . : the kurtosis of the physical sign time series (FHR series). Calculated to get (excess kurtosis). : represented by . . : represented by . .

[0039] Detailed calculation process: Calculate the terms in the numerator: ; Numerator term = ; Calculate the terms in the denominator: ; Denominator = ; Calculation : ; The advantage of the formula is that it integrates information from three different sources, namely physical signs, images, and endocrine, through a comprehensive formula. In particular, it takes into account the statistical characteristics of physical sign data and the variability of multi-source features, and structurally integrates the complex relationships of multi-source features. This module integrates three types of normalized values , and determines whether they appear simultaneously at the same warning node (e.g., T6'). If the T6' node simultaneously triggers an abnormality in physical signs ( deviation from the baseline), an abnormality in the image ( deviation from the baseline), and a specific change in the endocrine index ( reflected variability), then these three normalized values are combined into a three-dimensional vector form , serving as the feature fusion input body at this time node, generating a set of feature fusion input bodies. The calculated is a comprehensive variation index, and this result indicates that after considering various factors such as physical signs, images, and endocrine, a certain comprehensive variability is exhibited at this time point, which is used for subsequent trend analysis.

[0040] Please refer to Figure 5 , the collaborative trend recognition module includes: The amplitude screening sub-module calculates the change value sequence of multiple indicators within the time window based on the three indicators in the obstetrics and gynecology feature fusion input body, and compares the amplitudes of the maximum fluctuation intervals, screening the one with the largest amplitude value to generate the maximum amplitude trend value; Receives the feature fusion input body, that is, the three-dimensional vector sequence generated in the previous step, Table 4 Example of the feature fusion input body sequence: ; As shown in Table 4, assume that we have the feature fusion input bodies at 5 consecutive time points , where the three components of each vector represent the normalized values of the physical sign category , the image category , and the estradiol category respectively. First, the amplitude screening sub-module calculates the change value sequence (difference between adjacent points) of each indicator within the set time window (from time point 1 to time point 5) based on these three indicators, The change sequence of is [0.08, 0.07, -0.05, -0.05], The change sequence is [-0.065, 0.015, 0.02, 0.02], and then compare the amplitude of the maximum fluctuation range (the difference between the maximum value and the minimum value) of each indicator within the entire time window. For , the maximum value is 0.45, the minimum value is 0.30, and the amplitude . For , the maximum value is 0.70, the minimum value is 0.55, and the amplitude . For , the maximum value is -0.65, the minimum value is -0.715, and the amplitude . In this example, the amplitudes of the physical sign category and the imaging category are tied for the largest (both are 0.15). One or both of them can be selected as the target indicator. Here, the imaging category indicator is selected as the target indicator, recorded, and the maximum amplitude trend value is generated.

[0041] The time correlation sub-module calls the change time window corresponding to the maximum amplitude trend value, calculates the overlap ratio with the other two indicators in the obstetrics and gynecology feature fusion input body, and screens out the time periods with a high overlap ratio to generate the time window overlap interval value; Call the change time window (time points 1 to 5) of this maximum amplitude trend value corresponding indicator ( ), and calculate the overlap ratio of this time window with the significant change time windows of the other two indicators ( and ) in the feature fusion input body respectively. The significant change time window can be determined by setting a threshold (for example, the absolute value of the change is greater than 0.06). For , the significant change is [0.08, 0.07] (points 1 - 3). For , the significant change is [-0.065] (points 1 - 2). The significant change of the target indicator is [0.075, 0.075] (points 1 - 3). Then and have a significant window overlap of points 1 - 3, and the overlap ratio is (relative to the length of their respective significant windows). and have a significant window overlap of points 1 - 2, and the overlap ratio is (relative to the length of their respective significant windows) or 2 / 3 (relative to the significant window length of ) calculated according to the total window. Here, a simplified method is adopted, believing that the overlap ratio is high within the main change interval, and take (assuming obtained according to a certain calculation rule). Screen out the time periods with a higher overlap ratio. For example, set the threshold to 0.5, then and All are selected to generate the overlapping interval value of the time window.

[0042] Based on the change direction of the indicators contained in the overlapping interval value of the time window, the trend determination sub-module judges the direction consistency within the time window. If the directions are consistent, it is recorded as positive; if there is a conflict, it is recorded as negative. The trend scalar is calculated by combining the change amplitude and the overlapping ratio, using the formula: ; By calculating the trend synergy degree value and performing a threshold judgment, a multi-dimensional feature collaborative trend label is obtained; Among them, represents the multi-dimensional feature collaborative trend label, represents the continuous change amplitude of the th indicator, represents the time window overlapping ratio of the th indicator and the target indicator, represents the consistency matching value of the change direction between the th indicator and the remaining indicators in the obstetrics and gynecology feature fusion input body, is the number of indicators participating in the trend judgment.

[0043] Based on the selected overlapping interval value of the time window (for example, at points 1 - 3, at points 1 - 2) and the change directions of the indicators ( ) contained therein, it is judged that within the time point 1 - 3, has a change of [0.075, 0.075] (positive), has a change of [0.08, 0.07] (positive), and the directions are consistent, , within the time point 1 - 2, has a change of [0.075] (positive), has a change of [-0.065] (negative), and the directions are opposite, , combined with the change amplitude of each indicator (using the total change amplitude within the overlapping window), , ; and the time window overlapping ratio ( ), the trend scalar is calculated using the formula: ; Parameter assignment and calculation ( , the indicator is and ): , , , , ; Calculating the numerator : Item 1( ): ; Item 2( ): ; Sum of numerators = ; Calculating the denominator : Item 1( ): ; Item 2( ): ; Denominator = ; Calculation : ; The advantage of the formula is that it integrates the variation range and time overlap degree of each feature relative to the target feature through the numerator, and the denominator is adjusted using direction consistency, quantifying the co - movement trend between multi - dimensional features. The trend synergy degree value obtained through calculation , comparing this degree value with a preset threshold. For example, if the synergy trend threshold is set to 0.1, then currently , indicating that the synergy trend is not significant, obtaining the multi - dimensional feature synergy trend label as "weak synergy", and this result indicates that although each indicator fluctuates and there is a certain time synchronization between some indicators, the overall trend strength of their co - variation, calculated according to the current formula and parameters, is at a relatively low level Please refer to Figure 6 , the diagnosis and treatment advice generation module includes: The synergy trend analysis sub - module, based on the multi - dimensional feature synergy trend label, extracts the difference between the fetal heart rate baseline value and the preset safe baseline range, calculates the deviation amplitude between the edge density growth gradient and the critical expansion rate per unit time, fits the linear decline slope of the estradiol concentration change rate, performs weighted assignment on the difference, deviation amplitude, and slope, and then performs superposition operation to generate a synergy trend response value; Receiving the multi - dimensional feature synergy trend label ("weak synergy", synergy degree value ), first, based on this marker, the co - trend analysis sub - module further extracts specific indicators directly related to clinical judgment for analysis. It extracts the baseline value of the previously calculated fetal heart rate (FHR) time series [138, 140, 142, 139, 130, 145, 132, 135, 138, 140]. By performing a 10 - point moving average (or other filtering methods) on this series, the calculated baseline value is approximately 137.9 beats per minute. It obtains the preset safe baseline range of fetal heart rate (110 - 160 beats per minute), and calculates the difference between the current baseline value and the boundary of the safe range. , , take the difference from the nearest boundary, that is, 22.1 beats per minute. At the same time, calculate the growth gradient of the previously obtained imaging - type indicators (edge density ) within a unit time. For example, in the rising stage (points 1 - 3), the average gradient is approximately / time point, and compare it with the preset critical expansion rate of edge density. The basis for setting this critical rate is, for example, when the edge density grows too fast, it may indicate tissue abnormality. According to clinical statistics, the critical value is set to 0.05 / time point, and calculate the deviation amplitude between the current gradient and the critical rate, that is / time point. Then, fit the change rate of the previously obtained estradiol (E2) concentration value sequence (see Table 3) [4500, 4650, 4800, 4700, 4950, 5100, 5200, 5050, 4400, 4300]. Perform a linear fit on the last three points [5050, 4400, 4300], and the obtained slope is approximately pg / mL / time point, indicating a downward trend. Assign weights to these three key analysis results: the difference in fetal heart rate baseline (22.1 beats per minute), the deviation amplitude of edge density (0.025 / time point), and the change slope of estradiol (- 375 pg / mL / point). The basis for setting the weights is, for example, the weight of FHR baseline stability , the weight of edge density change , the weight of estradiol trend . These weights are determined based on the risk prediction ability of the indicators. First, different unit values need to be transformed into risk scores. For example, the FHR difference of 22.1 is within the safe range, and the risk score is 0.1; the density deviation of 0.025 is lower than the critical value, and the risk score is 0.2; the E2 slope of - 375 indicates a significant decline, and the risk score is 0.7. Then, the co - trend response value = , the monitoring condition matching sub-module calls the fetal heart rate safety upper limit threshold, the edge density expansion critical threshold, and the estradiol fluctuation lower limit threshold, compares the fetal heart rate difference in the collaborative trend response value with the safety upper limit, the edge density deviation amplitude with the critical rate, and the estradiol slope with the fluctuation lower limit, and generates a monitoring trigger determination result according to the combined trigger logic of the three parameters exceeding the limit at the same time; Call a plurality of preset thresholds, including the fetal heart rate safety upper limit threshold (160 beats / minute), the fetal heart rate safety lower limit threshold (110 beats / minute), the edge density expansion critical threshold (gradient > 0.05 / time point), and the estradiol fluctuation lower limit threshold (for example, E2 concentration < 4000 pg / mL or the decline rate exceeds -500 pg / mL / point), compare the original index value or risk score decomposed from the calculated collaborative trend response value with these thresholds. The current FHR baseline value (137.9) is within the range of [110, 160] and does not exceed the limit. The edge density growth gradient (0.075) exceeds the critical rate of 0.05, triggering condition 1. The lowest value of estradiol, 4300, is not lower than the lower limit of 4000, but the slope of -375 does not exceed -500, so condition 2 is not triggered. According to the preset combined trigger logic, for example, "the edge density gradient exceeds the limit and (the FHR baseline exceeds the limit or the E2 level / slope exceeds the limit)" for judgment. In this example, the edge density gradient exceeds the limit, but the FHR baseline and E2 do not exceed the limit (according to this logic), so the combined trigger condition is not met, and the generated monitoring trigger determination result is "partial indicators are abnormal and the combined trigger condition is not reached".

[0044] Based on the monitoring trigger determination result, the intervention recommendation formulation sub-module calls the standard operation parameters of fetal heart rate monitoring and the maternal oxygen supply classification indicators, divides the monitoring frequency level according to the fetal heart rate difference, integrates the estradiol slope to adjust the oxygen supply flow threshold, and defines the cesarean section priority parameter in combination with the edge density deviation amplitude to generate the diagnosis and treatment recommendation items for fetal distress in utero.

[0045] Based on the monitoring trigger determination result ("partial indicators are abnormal and the combined trigger condition is not reached"), and calls the standard operation parameters of fetal heart rate monitoring and the maternal oxygen supply classification indicators stored in the knowledge base. If the determination result is "triggered", recommendations will be formulated according to the specific exceeded indicators and degrees. However, since the combined trigger condition is not reached in this example, but there is an accelerating growth of the edge density and a downward trend of E2, the system will generate a recommendation item of "monitoring the accelerating growth of the edge density and the downward trend of estradiol. Although the emergency intervention indication is not reached, it is recommended to strengthen the monitoring frequency to NST once an hour and closely monitor the subsequent changes", rather than direct intervention measures. If the collaborative trend response value (0.31) is in a certain early warning range (for example, 0.3 - 0.5 indicates that attention is needed), the recommendation will also be strengthened, generating the diagnosis and treatment recommendation items for fetal distress in utero.

[0046] An obstetrics and gynecology diagnosis and treatment assistance method, which is executed based on the above-mentioned obstetrics and gynecology diagnosis and treatment assistance system, includes the following steps: S1: Obtain the physiological data including the patient's body temperature, heart rate, pulse, and blood pressure in obstetric monitoring, record the time stamp, extract the regional edge density of the synchronized two-dimensional ultrasound image, bind it with the physiological data and dynamically identify it to generate a bound diagnosis and treatment data unit set; S2: Compare the fetal heart rate data in the bound diagnosis and treatment data unit set with the fetal heart rate variability threshold. If it exceeds the threshold, mark it as a warning node, extract the synchronized pulse, blood pressure, and image edge density to generate an obstetric risk warning feature label group; S3: According to the fetal heart rate, edge density, and estradiol value in the obstetric risk warning feature label group, classify them into the physical sign class, imaging class, and laboratory class feature groups, perform standard deviation normalization. If the three types of features appear in the warning node simultaneously, integrate them into a three-dimensional vector to generate an obstetrics and gynecology feature fusion input body; S4: Call the three indicators in the obstetrics and gynecology feature fusion input body, screen the indicator with the largest change range and the longest duration, judge the direction consistency and the time window overlap ratio to generate a multi-dimensional feature collaborative trend label; S5: According to the combination of fetal heart rate increase, edge density expansion, and estradiol decrease in the multi-dimensional feature collaborative trend label, match the condition items, trigger the fetal intrauterine distress monitoring condition, and generate an obstetrics and gynecology diagnosis and treatment assistance recommendation item.

[0047] The above is only the preferred embodiment of the present invention, and it is not used to limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An obstetrics and gynecology diagnosis and treatment auxiliary system, characterized in that: The system comprises: The parameter acquisition module obtains the body temperature, heart rate, pulse and blood pressure of patients in obstetric monitoring, collects and records timestamps, extracts the edge density of the synchronized two-dimensional ultrasound image area, binds it with physiological data and sets dynamic identifiers, and generates a bound diagnosis and treatment data unit set; The feature offset identification module calls the fetal heart rate value in the bound diagnosis and treatment data unit and compares it with the fetal heart rate variation threshold. If it exceeds the threshold, it is marked as a warning node. At the same time, the corresponding pulse, blood pressure and image edge density are extracted to judge the synchronous changes, and an obstetric risk warning feature label group is generated; The feature reconstruction module classifies the fetal heart rate, edge density value and estradiol value in the obstetric risk warning feature label group into physical sign, imaging and laboratory feature groups respectively, and performs normalization according to the standard deviation of the acquisition within the group. If the three types of features co-occur at the warning node, they are integrated into a three-dimensional vector form to generate an obstetrics and gynecology feature fusion input body; The collaborative trend identification module calls the three indicators in the obstetrics and gynecology feature fusion input body, selects the largest change amplitude and the longest duration, determines the direction consistency and the time window overlap ratio, and generates a multi-dimensional feature collaborative trend mark.

2. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 1, characterized in that: The bound medical data unit set includes dynamic identification, physiological data image synchronization binding information, and timestamp mark; the obstetric risk warning feature label group specifically includes abnormal identification mark, synchronous change parameter group, and risk feature identification; the obstetrics and gynecology feature fusion input body includes three-dimensional vectors of physical signs, three-dimensional vectors of images, and three-dimensional vectors of laboratories; the multi-dimensional feature collaborative trend mark specifically includes change amplitude index, duration parameter, direction consistency mark, and time window overlap ratio.

3. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 2, characterized in that: The parameter acquisition module comprises: The physiological data collection submodule collects the patient's body temperature, heart rate, pulse and blood pressure in obstetric monitoring, records the timestamp of each data, classifies and stores it, calculates the data change rate and fluctuation range, obtains multiple data change intervals, and generates physiological parameter change intervals; The image region extraction submodule calls the synchronous ultrasound image, extracts the edge information of the fetal region, calculates the grayscale edge density of the image, screens the region that meets the reference density, and obtains the synchronous edge density value; The diagnosis and treatment unit generation submodule calls the physiological parameter change interval and the synchronous edge density value, calculates the relationship between the physiological fluctuation and the image edge, extracts the binding data, and uses the formula: ； The interactive mapping difference value is obtained by operation, and a bound diagnosis and treatment data unit set is generated; in, Representative The fluctuation value of physiological parameters of the group, Representative Group image edge density, Representative The grayscale basis difference of the edge of the group image, Representative The actual boundary fitting value of the group image region, Represents the number of timestamp groups participating in the matching, and M is the difference value of the interactive mapping.

4. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 3, characterized in that: The feature offset recognition module comprises: The fetal heart rate warning identification submodule calls the fetal heart rate value concentrated in the bound diagnosis and treatment data unit to obtain the fetal heart rate fluctuation data in each monitoring cycle, and at the same time calculates the difference between the fluctuation range and the threshold value based on the fetal heart rate variation threshold, marks the cycle exceeding the threshold, and obtains the warning mark node value; The synchronous change feature extraction submodule extracts the pulse, blood pressure and image edge density data at the corresponding time node according to the value of the warning mark node, calculates the change rate and the mean change, determines whether it is a synchronous fluctuation, and obtains the synchronous fluctuation analysis value; The obstetric risk label generation submodule is based on the synchronous fluctuation analysis value and adopts the formula: ； The fetal heart rate and pulse change intensity are calculated, and the composite fluctuation index under the influence of image features is combined with the consistency of the synchronous change direction to generate an obstetric risk warning feature label group; in, Represents the fetal heart rate variation. Represents the pulse amplitude, Indicates Blood pressure values ​​of the group, Indicates Group image edge density value, Indicates The edge density change value of the group image, is the base value, is the number of time nodes, It is a composite volatility index.

5. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 4, characterized in that: The feature reconstruction module comprises: The physical sign classification submodule obtains the continuous measurement value sequence at multiple time nodes based on the fetal heart rate data in the obstetric risk warning feature label group, calculates the standard deviation and performs standard deviation normalization processing within the group, classifies it into the physical sign feature group, and generates a physical sign normalization value; The image classification submodule obtains the pixel distribution area of ​​the edge density value in the image frame, extracts the maximum value and the average value, calculates the standard deviation and normalizes it using the standard deviation within the group, classifies it into the image class feature group, and obtains the image class normalized value; The multi-source feature fusion submodule calls the normalized value of the physical sign class and the normalized value of the image class to obtain the measurement data of the estradiol value, calculates the coefficient of variation and removes abnormal points, using the formula: ； Integrate the three types of normalized values ​​to determine whether they co-occur at the same warning node. If they co-occur, combine them into a three-dimensional vector form to generate a feature fusion input volume; in, Represents the coefficient of variation after the fusion of three types of features, represents the coefficient of variation of the normalized values ​​of the sign class, represents the mean of the time series of physical signs, Represents the skewness of the time series of physical signs, Represents the kurtosis of the time series of physical signs, represents the coefficient of variation of the normalized values ​​of the image class, represents the coefficient of variation of the normalized values ​​of estradiol, represents absolute value operation, and solves for the difference between the coefficient of variation of estradiol values ​​and the mean of physical signs.

6. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 5, characterized in that: The collaborative trend identification module includes: The amplitude screening submodule calculates the change value sequence of multiple indicators in the time window based on the three indicators in the gynecology and obstetrics feature fusion input body, compares the maximum fluctuation interval amplitude, screens the one with the largest amplitude value, and generates the maximum amplitude trend value; The time association submodule calls the change time window of the indicator corresponding to the maximum amplitude trend value, calculates the overlap ratio with the other two indicators in the obstetrics and gynecology feature fusion input body, selects the time period with a high overlap ratio, and generates the time window overlap interval value; The trend determination submodule determines the consistency of the direction within the time window according to the change direction of the indicators contained in the overlapping interval value of the time window. If the direction is consistent, it is recorded as positive, and if it is conflicting, it is recorded as reverse. The trend scalar is calculated by combining the change amplitude and the overlap ratio, using the formula: ； By calculating the trend synergy value and performing threshold judgment, a multi-dimensional feature synergy trend mark is obtained; in, Represents a multi-dimensional feature collaborative trend marker, Indicates The continuous change range of the index, Indicates The time window overlap ratio between the item indicator and the target indicator, Indicates The consistency matching value of the change direction of the index and the other indexes in the obstetrics and gynecology feature fusion input body, The number of indicators involved in trend judgment.

7. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 6, characterized in that: The system further comprises: The diagnosis and treatment suggestion generation module matches the condition items in the suggestion list according to the combination of increased fetal heart rate, expanded edge density and decreased estradiol in the multi-dimensional feature collaborative trend marker, triggers the fetal intrauterine distress monitoring condition, and generates obstetrics and gynecology diagnosis and treatment auxiliary suggestion items; The obstetrics and gynecology diagnosis and treatment auxiliary suggestion items include monitoring suggestion type, intervention suggestion type, and warning level identification.

8. The obstetrics and gynecology diagnosis and treatment auxiliary system according to claim 7, characterized in that: The diagnosis and treatment suggestion generating module comprises: The collaborative trend analysis submodule extracts the difference between the fetal heart rate baseline value and the preset safety baseline range based on the multi-dimensional feature collaborative trend mark, calculates the deviation amplitude of the edge density growth gradient and the critical expansion rate per unit time, fits the linear decline slope of the estradiol concentration change rate, and performs weighted superposition operation on the difference, deviation amplitude and slope to generate a collaborative trend response value; The monitoring condition matching submodule calls the fetal heart rate safety upper limit threshold, the edge density expansion critical threshold and the estradiol fluctuation lower limit threshold, compares the fetal heart rate difference in the collaborative trend response value with the safety upper limit, the edge density deviation amplitude with the critical rate, and the estradiol slope with the fluctuation lower limit, and generates a monitoring trigger judgment result based on the joint trigger logic of the three parameters exceeding the limit at the same time; The intervention recommendation formulation submodule calls the standard operating parameters of fetal heart monitoring and the maternal oxygen supply classification index based on the monitoring trigger judgment results, divides the monitoring frequency level according to the fetal heart rate difference, integrates the estradiol slope to adjust the oxygen supply flow threshold, defines the cesarean section priority parameter based on the edge density deviation amplitude, and generates fetal intrauterine distress diagnosis and treatment recommendation items.

9. A gynecological and obstetric diagnosis and treatment auxiliary method, characterized in that: The obstetrics and gynecology diagnosis and treatment auxiliary system according to any one of claims 1 to 8 comprises the following steps: S1: Obtain physiological data including body temperature, heart rate, pulse, and blood pressure of patients in obstetric monitoring and record timestamps, extract regional edge density of synchronous two-dimensional ultrasound images, bind and dynamically identify with physiological data, and generate a bound diagnosis and treatment data unit set; S2: comparing the fetal heart rate data in the bound diagnosis and treatment data unit set with the fetal heart rate variation threshold, marking it as a warning node if it exceeds the threshold, extracting the synchronous pulse, blood pressure and image edge density, and generating an obstetric risk warning feature label group; S3: According to the fetal heart rate, edge density and estradiol value in the obstetric risk warning feature label group, they are classified into physical sign, imaging and laboratory feature groups, and the standard deviation is normalized. If the three types of features co-occur in the warning node, they are integrated into a three-dimensional vector to generate an obstetrics and gynecology feature fusion input body; S4: calling the three indicators in the obstetrics and gynecology feature fusion input body, screening the indicators with the largest change amplitude and the longest duration, judging the direction consistency and the time window overlap ratio, and generating a multi-dimensional feature collaborative trend marker; S5: According to the combination of increased fetal heart rate, expanded edge density and decreased estradiol in the multi-dimensional feature collaborative trend marker, matching condition items are performed to trigger fetal intrauterine distress monitoring conditions and generate obstetrics and gynecology diagnosis and treatment auxiliary recommendation items.

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