Congenital heart disease process evaluation method and system based on ultrasonic data

Abstract

The invention provides a congenital heart disease process evaluation method and system based on ultrasonic data, and the method comprises the steps: obtaining a dynamic examination table of corresponding detection time of a target detection person, calling the attribute information of the target detection person, and generating an initial time sequence diagnosis tree according to the attribute information and the dynamic examination table; calling ultrasonic data intercepted by a physician end, acquiring comment information and interception time of the ultrasonic data, and filling the corresponding ultrasonic data into a dynamic check table corresponding to check nodes in the initial time sequence diagnosis tree based on the comment information and the interception time to obtain an ultrasonic time sequence diagnosis tree; determining an abnormal item in the ultrasonic time sequence diagnosis tree, and performing splicing processing on each dynamic check table in the ultrasonic time sequence diagnosis tree based on the abnormal item to obtain an abnormal sequence table corresponding to the abnormal item; and according to the trend prediction strategy and the ultrasonic data in the abnormal sequence table, generating a heart prediction change chart and sending the heart prediction change chart to a detection end.

Description

Technical Field

[0001] The invention relates to data processing technology, and in particular to a congenital heart disease progression assessment method and system based on ultrasonic data. Background Art

[0002] Congenital heart disease (CHD) is the most common birth defect in children. Its incidence rate remains high worldwide, seriously affecting the quality of life and life expectancy of children. Early progression assessment of congenital heart disease is of great significance for formulating treatment plans and improving children's physical fitness.

[0003] Traditional diagnostic methods mainly rely on the doctor's clinical experience and cardiac ultrasound examinations, but this method has problems such as strong subjectivity and low diagnostic efficiency. In addition, due to the large number of test reports, in order to ensure the accuracy of the diagnostic results, it is necessary to analyze multiple test items in multiple test reports one by one. When personnel do not conduct tests in a timely manner according to the doctor's orders and lack test data at the corresponding time, it will affect the diagnostic efficiency and may also reduce the accuracy of the diagnostic results.

[0004] Therefore, how to automatically summarize, analyze and display the detection data of corresponding abnormal items based on the actual detection data of the target personnel so that personnel can view them intuitively and improve diagnostic accuracy and efficiency has become an urgent problem to be solved. Summary of the invention

[0005] The embodiment of the present invention provides a congenital heart disease progression assessment method and system based on ultrasound data, which can automatically analyze, summarize and display the detection data of corresponding abnormal items according to the actual detection data of the target person, so as to facilitate the intuitive viewing of the personnel and improve the diagnostic accuracy and efficiency.

[0006] A first aspect of an embodiment of the present invention provides a method for evaluating the progression of congenital heart disease based on ultrasound data, comprising:

[0007] Obtain a dynamic checklist of target detection personnel corresponding to detection time, retrieve attribute information of the target detection personnel, and generate an initial time series diagnosis tree according to the attribute information and the dynamic checklist;

[0008] Retrieving ultrasound data intercepted by the physician, obtaining annotation information and interception time of the ultrasound data, and filling the corresponding ultrasound data into a dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time, to obtain an ultrasound time series diagnosis tree;

[0009] Determine an abnormal item in the ultrasound time series diagnosis tree, and perform splicing processing on each of the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal item to obtain an abnormal sequence table corresponding to the abnormal item;

[0010] According to the trend prediction strategy and the ultrasound data in the abnormal sequence table, a heart prediction evaluation change diagram is generated and sent to the detection end.

[0011] Optionally, in a possible implementation of the first aspect, obtaining a dynamic check table corresponding to the detection time of the target detection personnel, retrieving attribute information of the target detection personnel, and generating an initial time series diagnosis tree according to the attribute information and the dynamic check table, includes:

[0012] Obtaining a dynamic inspection table corresponding to the inspection time of the target inspection personnel, and retrieving the attribute information of the target inspection personnel, wherein the dynamic inspection table includes an ultrasonic filling area and a coefficient evaluation area corresponding to a plurality of dynamic inspection items;

[0013] Constructing a person node according to the attribute information, extracting the month information and date information in the detection time, constructing a month summary node based on the month information, and connecting the month summary node with the person node;

[0014] Constructing a date subdivision node based on the date information, connecting the date subdivision node with the corresponding month summary node, and constructing a check node connected to the date subdivision node;

[0015] The dynamic check table is bound to the corresponding check node according to the detection time to obtain an initial timing diagnosis tree.

[0016] Optionally, in a possible implementation of the first aspect, the ultrasound data intercepted by the physician end is retrieved, annotation information and interception time of the ultrasound data are obtained, and the corresponding ultrasound data is filled into a dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time to obtain the ultrasound time series diagnosis tree, including:

[0017] Retrieving ultrasound data intercepted by the physician, and obtaining annotation information and interception time of the ultrasound data;

[0018] Determine the corresponding month summary node as the month update node and the date subdivision node as the date update node based on the interception time, and use the check node connected to the date update node as the node to be updated;

[0019] Determine a dynamic check table corresponding to the node to be updated as a dynamic update table, and locate a dynamic check item in the dynamic update table as an update item based on the annotation information;

[0020] The ultrasound data is filled into the ultrasound filling area corresponding to the update item, and the evaluation coefficient of the ultrasound data by the physician is received, and the evaluation coefficient is filled into the coefficient evaluation area corresponding to the update item to obtain an ultrasound timing diagnosis tree.

[0021] Optionally, in a possible implementation of the first aspect, when it is determined that the dynamic inspection item corresponding to the dynamic inspection table in the ultrasound time series diagnosis tree does not have an evaluation coefficient, the corresponding dynamic inspection item is used as an item to be completed;

[0022] Obtaining date information of the item to be completed that does not have an evaluation coefficient as the missing date, and taking the remaining date information corresponding to the item to be completed as the reference date, and retrieving the evaluation coefficient of the item to be completed corresponding to the reference date as the reference coefficient;

[0023] According to the missing date, the base date and the base coefficient, a completion coefficient of the item to be completed corresponding to the missing date is obtained.

[0024] Optionally, in a possible implementation manner of the first aspect, obtaining, according to the missing date, the base date and the base coefficient, a completion coefficient of the to-be-completed item corresponding to the missing date includes:

[0025] Sort the missing dates and the reference dates in ascending order to obtain a sequence of completion dates corresponding to the items to be completed;

[0026] Sequentially counting the adjacent missing dates in the complement date sequence to obtain multiple missing date sequences;

[0027] When it is determined that only one side of the missing date sequence has a reference date, the corresponding missing date sequence is used as a trend missing sequence, and the reference date is used as a trend completion date;

[0028] Acquire a reference coefficient corresponding to the trend completion date as a trend completion coefficient, and determine a completion coefficient of the to-be-completed item corresponding to the missing date in the trend missing sequence according to the trend completion coefficient and the trend completion date;

[0029] When it is determined that both sides of the missing date sequence are reference dates, the corresponding missing date sequence is used as the missing sequence at both ends, and the corresponding reference date is used as the completion date at both ends;

[0030] A reference coefficient corresponding to the two-end filling date is obtained as the two-end filling coefficient, and a filling coefficient of the item to be filled corresponding to the missing date in the two-end missing sequence is determined according to the two-end filling coefficient and the two-end filling date.

[0031] Optionally, in a possible implementation manner of the first aspect, determining, according to the trend completion coefficient and the trend completion date, the completion coefficient of the to-be-completed item corresponding to the missing date in the trend missing sequence includes:

[0032] Obtaining two adjacent trend completion dates in the completion date sequence as trend calculation dates, and retrieving trend completion coefficients corresponding to the trend calculation dates as trend calculation coefficients;

[0033] Calculate the absolute value of the trend date difference according to the trend to obtain the trend date difference, and calculate the absolute value of the trend coefficient difference based on the trend to obtain the trend coefficient difference;

[0034] Based on the ratio of the trend coefficient difference and the trend date difference, the trend change rate corresponding to the two adjacent trend completion dates is obtained, and the average value of the trend change rate is obtained to obtain the average change rate;

[0035] When it is determined that the trend missing sequence is at the end of the supplementary date sequence, a trend supplementary date adjacent to the trend missing sequence in the supplementary date sequence is obtained as a starting prediction date, and a trend supplementary coefficient corresponding to the starting prediction date is retrieved as a starting prediction coefficient;

[0036] Obtaining the missing date in the trend missing sequence, obtaining the predicted date difference according to the difference between the missing date and the starting predicted date, and obtaining the prediction coefficient difference according to the product of the predicted date difference and the average change rate;

[0037] According to the sum of the starting prediction coefficient and the difference of the prediction coefficient, the filling coefficient of the item to be filled corresponding to the missing date is obtained;

[0038] When it is determined that the trend missing sequence is located at the front end of the supplementary date sequence, the trend supplementary date adjacent to the trend missing sequence in the supplementary date sequence is obtained as the end prediction date, and the trend supplementary coefficient corresponding to the end prediction date is retrieved as the end prediction coefficient;

[0039] Obtain the missing date in the trend missing sequence, obtain the predicted date difference according to the difference between the last predicted date and the missing date, and obtain the prediction coefficient difference according to the product of the predicted date difference and the average change rate;

[0040] According to the difference between the final prediction coefficient and the prediction coefficient difference, the filling coefficient of the item to be filled corresponding to the missing date is obtained.

[0041] Optionally, in a possible implementation manner of the first aspect, determining the filling coefficient of the to-be-filled item corresponding to the missing date in the missing sequence at both ends according to the filling coefficient at both ends and the filling date at both ends includes:

[0042] According to the sum of the padded coefficients at both ends, the sum of the coefficients at both ends is obtained, and based on the sum of the padded dates at both ends, the sum of the dates at both ends is obtained;

[0043] Obtain the missing dates in the missing sequence at both ends, and obtain the date proportion according to the ratio of the missing dates to the sum of the dates at both ends;

[0044] Based on the date proportion and the product of the coefficient and value at both ends, the filling coefficient of the to-be-filled item corresponding to the missing date in the missing sequence at both ends is obtained.

[0045] Optionally, in a possible implementation manner of the first aspect, determining the abnormal items in the ultrasound time series diagnosis tree, and splicing the dynamic check tables in the ultrasound time series diagnosis tree based on the abnormal items to obtain an abnormal sequence table corresponding to the abnormal items, includes:

[0046] Acquire medical indicators of ultrasound data corresponding to each dynamic examination item in the ultrasound time series diagnosis tree, wherein the medical indicators at least include inner diameter, area and length;

[0047] Retrieving the standard index interval of the dynamic examination item corresponding to each date information in the ultrasound time series diagnosis tree;

[0048] When it is determined that the medical indicator is not within the standard indicator range, treating the corresponding dynamic examination item as an abnormal item;

[0049] Based on the abnormal items, the ultrasonic filling areas in each of the dynamic inspection tables are intercepted as the areas to be spliced, and the areas to be spliced ​​are sequentially spliced ​​according to the detection time to obtain an abnormal sequence table corresponding to the abnormal items.

[0050] Optionally, in a possible implementation of the first aspect, generating a heart prediction change graph according to the trend prediction strategy and the ultrasound data in the abnormal sequence table and sending it to the detection end includes:

[0051] Obtaining the medical index of the ultrasound data corresponding to each detection time in the abnormal sequence table as a construction index, and the standard index interval corresponding to each detection time;

[0052] When it is determined that the construction index is less than the standard index interval, the minimum value of the standard index interval is retrieved as the minimum reference value;

[0053] According to the difference between the construction index and the minimum reference value, a negative anomaly difference is obtained, based on the ratio of the negative anomaly difference to the minimum reference value, a negative anomaly proportion is obtained, and the detection time of the construction index corresponding to the negative anomaly proportion is obtained as the negative anomaly time;

[0054] When it is determined that the construction index is greater than the standard index interval, the maximum value of the standard index interval is retrieved as the maximum reference value;

[0055] According to the difference between the construction index and the maximum reference value, a positive anomaly difference is obtained, based on the ratio of the positive anomaly difference to the maximum reference value, a positive anomaly proportion is obtained, and the detection time of the construction index corresponding to the positive anomaly proportion is obtained as the positive anomaly time;

[0056] When it is determined that the construction indicator is within the standard indicator range, the detection time corresponding to the construction indicator is used as the standard time, and a preset standard proportion is configured for the standard time;

[0057] Retrieving a preset base plate image and a preset change coordinate system, obtaining the center point of the preset base plate image, aligning the origin of the preset change coordinate system with the center point, wherein the horizontal axis of the preset change coordinate system has a plurality of preset time points, and the vertical axis of the preset change coordinate system has a plurality of preset proportions;

[0058] Determine a negative trend point in the preset change coordinate system according to the negative abnormal time and the negative abnormal proportion;

[0059] Determine a positive trend point in the preset change coordinate system based on the positive abnormality time and the positive abnormality proportion;

[0060] Determine a standard trend point in the preset change coordinate system according to the standard time and the preset standard proportion;

[0061] The detection times in the abnormal sequence table are sorted in ascending order to obtain a time connection sequence, and the negative trend points, the positive trend points and the standard trend points are connected in sequence based on the order of the detection times in the time connection sequence to generate a heart prediction change graph and send it to the detection end.

[0062] A second aspect of an embodiment of the present invention provides a congenital heart disease progression assessment system based on ultrasound data, comprising:

[0063] A generation module, used to obtain a dynamic checklist of target detection personnel corresponding to detection time, and retrieve attribute information of the target detection personnel, and generate an initial time series diagnosis tree according to the attribute information and the dynamic checklist;

[0064] A filling module is used to retrieve the ultrasound data intercepted by the physician, obtain the annotation information and the interception time of the ultrasound data, and fill the corresponding ultrasound data into the dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time to obtain the ultrasound time series diagnosis tree;

[0065] A splicing module, used for determining abnormal items in the ultrasound time series diagnosis tree, splicing each of the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal items, and obtaining an abnormal sequence table corresponding to the abnormal items;

[0066] The sending module is used to generate a heart prediction change diagram based on the trend prediction strategy and the ultrasound data in the abnormal sequence table and send it to the detection end.

[0067] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a memory, a processor and a computer program, wherein the computer program is stored in the memory, and the processor runs the computer program to execute the first aspect of the present invention and various methods that may be involved in the first aspect.

[0068] According to a fourth aspect of an embodiment of the present invention, a storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the first aspect of the present invention and various methods that may be involved in the first aspect.

[0069] The beneficial effects of the present invention are as follows:

[0070] 1. The present invention can automatically summarize and display the detection data of the corresponding abnormal items according to the actual detection data of the target personnel, so that the personnel can view it intuitively, thereby improving the accuracy and efficiency of diagnosis. First, the present invention can obtain an initial time series diagnosis tree according to the attribute information and the dynamic inspection table, so that the diagnosis data can be predicted and supplemented according to the initial time series diagnosis tree in the future, so as to provide more diagnosis data as a reference and improve the accuracy of the diagnosis result. Secondly, according to the annotation information and interception time of the ultrasound data, the corresponding ultrasound data is filled in the corresponding dynamic inspection table to generate an ultrasound time series diagnosis tree, so as to facilitate the dynamic management and analysis of the ultrasound data. Thus, the present invention can splice the dynamic inspection tables corresponding to the determined abnormal items, thereby obtaining an abnormal sequence table corresponding to the abnormal items, so that personnel can quickly locate and analyze heart abnormalities. Finally, the present invention can generate a heart prediction change diagram based on the ultrasound data in the abnormal sequence table and send it to the detection end, so that personnel can view it intuitively, thereby improving the accuracy and efficiency of diagnosis.

[0071] 2. The present invention can accurately predict the missing data, and fill the obtained supplementary data in the corresponding dynamic inspection table, increase the amount of inspection data for diagnostic analysis, so as to facilitate intuitive viewing by personnel, thereby improving the accuracy of the diagnostic results. Among them, the present invention can be divided into multiple situations according to the position of the missing date sequence corresponding to the missing date in the supplementary date sequence, so as to calculate the supplementary coefficient corresponding to the corresponding missing date in different ways, increase the amount of diagnostic data, so as to improve the accuracy of the supplementary coefficient, so as to facilitate personnel to perform diagnostic analysis, so as to improve the accuracy of the diagnostic results and the root diagnosis efficiency.

[0072] 3. The present invention can generate a heart prediction change graph based on ultrasound data, thereby showing the data changes of multiple examination results, so that personnel can intuitively view the change trend of the corresponding examination items, thereby quickly determining the diagnosis results. Among them, the present invention can generate a heart prediction change graph based on ultrasound data in the abnormal sequence table, clearly showing the changes in the examination results of the corresponding dynamic examination items, so that physicians can intuitively view the change trend of the examination results of the corresponding dynamic examination items through the heart prediction change graph, thereby quickly diagnosing the corresponding fetal heart condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flowchart of a method for evaluating the progression of congenital heart disease based on ultrasound data provided by the present invention;

[0074] Figure 2 A schematic diagram of an initial timing diagnosis tree provided by the present invention;

[0075] Figure 3 A schematic diagram of a heart prediction change diagram provided by the present invention;

[0076] Figure 4 This is a schematic diagram of the structure of a congenital heart disease progression assessment system based on ultrasound data provided by the present invention;

[0077] Figure 5 A schematic diagram of the hardware structure of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0078] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0079] like Figure 1 As shown, the present invention provides a flow chart of a method for evaluating the progression of congenital heart disease based on ultrasound data, the method for evaluating the progression of congenital heart disease based on ultrasound data comprising:

[0080] S1, obtaining a dynamic inspection table of target detection personnel corresponding to detection time, and retrieving attribute information of the target detection personnel, and generating an initial timing diagnosis tree according to the attribute information and the dynamic inspection table.

[0081] It should be noted that congenital heart disease is a congenital cardiovascular malformation caused by developmental defects of the fetal heart in the mother's body. Therefore, during pregnancy, fetal cardiac color Doppler ultrasound can be used to check the development of the fetal heart, mainly including normal heart morphology and cardiac function. Since the time of physical examination and pregnancy cycle of each person are different, the corresponding fetal development is also different. Therefore, a dynamic checklist with corresponding examination items at different periods, and the items to be checked in the dynamic checklist can change with the change of time. Therefore, in order to facilitate the medical staff to view and diagnose the dynamic checklist, the dynamic checklist of the target detection personnel can be obtained to generate an initial time series diagnostic tree, so as to facilitate the subsequent prediction and completion of missing data, and improve the accuracy of the diagnosis results.

[0082] It can be understood that the target detection personnel are the personnel who undergo color ultrasound examination, for example, person A, the detection time is the time of physical examination during pregnancy, for example, March 4, March 22, April 8, etc., the dynamic inspection form is the item form for the personnel to conduct the inspection, the attribute information is the information of the target detection personnel, for example, it can be a name, and the initial time series diagnosis tree is the time series structure tree for the target detection personnel to perform the result diagnosis.

[0083] Through the above implementation, the present invention can obtain an initial time series diagnosis tree, so as to facilitate subsequent prediction and completion of diagnosis data according to the initial time series diagnosis tree, so as to provide more diagnosis data as a reference and improve the accuracy of the diagnosis result.

[0084] In some embodiments, the specific implementation of step S1 (obtaining a dynamic checklist of target detection personnel corresponding to detection time, retrieving attribute information of the target detection personnel, and generating an initial time series diagnosis tree according to the attribute information and the dynamic checklist) includes:

[0085] S11, obtaining a dynamic inspection table of the target inspection personnel corresponding to the inspection time, and retrieving the attribute information of the target inspection personnel, wherein the dynamic inspection table includes an ultrasonic filling area and a coefficient evaluation area corresponding to a plurality of dynamic inspection items.

[0086] It can be understood that in order to customize the initial timing diagnosis tree of the target detection personnel, a dynamic inspection table and corresponding attribute information of the target detection personnel corresponding to the detection time can be obtained, and the dynamic inspection table includes ultrasonic filling areas and coefficient evaluation areas corresponding to multiple dynamic inspection items.

[0087] Among them, the dynamic examination items are items for ultrasound examination, such as heart development, cardiac blood flow, heart rhythm, heart valve function, etc. The ultrasound filling area is the area filled with ultrasound images, and the coefficient evaluation area is the area filled with the evaluation coefficients of the examination results.

[0088] S12, constructing a personnel node according to the attribute information, extracting the month information and date information in the detection time, constructing a month summary node based on the month information, and connecting the month summary node with the personnel node.

[0089] It should be noted that the target detection personnel may conduct multiple inspections. Therefore, in order to facilitate subsequent data viewing and processing, the month information and date information can be extracted from the detection time, so that the corresponding nodes can be constructed according to the month information and date information, so as to summarize the information according to time.

[0090] It is not difficult to understand that the detection time contains month information and date information, that is, the month corresponding to 3.4 is March, and the date information is 4, so the month summary node can be constructed based on the month information.

[0091] It can be understood that the personnel node is the node corresponding to the personnel information, the month information is the month in the detection time, the date information is the date in the detection time, and the month summary node is the node corresponding to the month information.

[0092] For example: Figure 2 As shown, when the extracted month information includes March, April, and May, corresponding month summary nodes can be constructed according to March, April, and May, thereby connecting each month summary node with the personnel node.

[0093] S13, constructing a date subdivision node based on the date information, connecting the date subdivision node with the corresponding month summary node, and constructing a check node connected to the date subdivision node.

[0094] It can be understood that the date subdivision node is a node corresponding to date information, and the check node is a node for checking in one-to-one correspondence with the date subdivision node.

[0095] For example: Figure 2As shown, when the corresponding date information of 4th, 22nd, 4th, 8th and 25th is extracted from the detection times 3.4, 3.22nd, 4.8th and 4.25th, the date subdivision nodes corresponding to 4th, 22nd, 8th and 25th are constructed respectively, so that the date subdivision nodes corresponding to 4th and 22nd are connected to the corresponding month summary nodes of March, and the date subdivision nodes corresponding to 8th and 25th are connected to the corresponding month summary nodes of April. In addition, since the detection time needs to be checked, there is a corresponding dynamic check table, so that the check nodes corresponding to each date subdivision node can be generated one by one and connected accordingly.

[0096] S14, binding the dynamic check table with the corresponding check node according to the detection time to obtain an initial timing diagnosis tree.

[0097] It is understandable that the corresponding dynamic check table is bound and associated with the corresponding check node according to the detection time, thereby generating an initial timing diagnosis tree, so that the dynamic check table bound to the corresponding check node can be retrieved and analyzed according to the date information later, thereby improving data processing efficiency.

[0098] S2, retrieve the ultrasound data intercepted by the physician, obtain the annotation information and interception time of the ultrasound data, fill the corresponding ultrasound data into the dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time, and obtain the ultrasound time series diagnosis tree.

[0099] It should be noted that in order to enable doctors to obtain accurate diagnosis results, doctors can intercept ultrasound data during the ultrasound test and annotate the ultrasound data so that doctors can carefully review the detection information in the ultrasound examination during the annotation process.

[0100] It is understandable that when the physician performs an ultrasonic test on the target person, the data of the test process will be intercepted to obtain the corresponding ultrasonic data, such as the color ultrasound image, so as to fill the corresponding ultrasonic data in the corresponding dynamic examination table, thereby facilitating the personnel to view the examination results.

[0101] Among them, the physician end is the instrument terminal used by the physician for examination, the ultrasound data is the image data in the ultrasound examination, the annotation information is the information for annotating and describing the ultrasound data, such as small and hypoplastic tricuspid valve, etc., the capture time is the time when the data is captured during the ultrasound detection, and the ultrasound time series diagnosis tree is a result tree filled with ultrasound data in the dynamic inspection table corresponding to the corresponding inspection node.

[0102] It is not difficult to understand that the corresponding annotation information and interception time can be obtained through ultrasound data, so that the corresponding ultrasound data can be filled in the dynamic inspection table of the inspection node connected to the corresponding date subdivision node in the initial time series diagnosis tree, thereby completing the filling of the detection data, so as to facilitate the subsequent prediction and completion of the data of the corresponding missing date, which is convenient for personnel to view and accurately diagnose.

[0103] In some embodiments, the specific implementation of step S2 (the step of retrieving ultrasound data intercepted by the physician, obtaining annotation information and interception time of the ultrasound data, and filling the corresponding ultrasound data into the dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time to obtain the ultrasound time series diagnosis tree) includes:

[0104] S21, retrieve the ultrasound data intercepted by the physician, and obtain the annotation information and interception time of the ultrasound data.

[0105] It is understandable that the corresponding annotation information and the interception time are obtained through the ultrasound data so that the corresponding ultrasound data can be filled in the corresponding dynamic inspection table later, thereby facilitating personnel to view.

[0106] S22, based on the interception time, determine the corresponding month summary node as the month update node, and the date subdivision node as the date update node, and use the check node connected to the date update node as the node to be updated.

[0107] It is understandable that the corresponding month information and date information can be obtained from the intercepted time, so that the nodes that need to be filled and updated with information later can be determined.

[0108] Among them, the month update node is the month summary node corresponding to the month information in the interception time, the date update node is the date subdivision node corresponding to the date information in the interception time, and the node to be updated is the check node connected to the date update node.

[0109] For example: when the interception time is March 4, 2024, the corresponding month update node is the month summary node corresponding to March, and the date update node is the date subdivision node corresponding to the 4th under the month update node. Therefore, the inspection node connected to the date update node corresponding to the 4th is the node to be updated, so as to fill and update the ultrasonic data in the dynamic inspection table bound to the node to be updated.

[0110] S23, determining a dynamic check table corresponding to the node to be updated as a dynamic update table, and locating a dynamic check item in the dynamic update table as an update item based on the annotation information.

[0111] It can be understood that the dynamic update table is a dynamic check table corresponding to the nodes to be updated, and the update items are dynamic check items corresponding to the annotation information in the dynamic update table.

[0112] For example, when the annotation information is that the tricuspid valve is small and hypoplastic, the dynamic examination items of the heart valve function in the dynamic update table can be used as update items.

[0113] Through the above implementation, the present invention can determine the update items so as to subsequently fill the corresponding ultrasound data in the corresponding ultrasound filling area.

[0114] S24, filling the ultrasound data into the ultrasound filling area corresponding to the update item, receiving the evaluation coefficient of the ultrasound data from the physician, filling the evaluation coefficient into the coefficient evaluation area corresponding to the update item, and obtaining an ultrasound timing diagnosis tree.

[0115] It is understandable that medical staff can evaluate the corresponding dynamic examination items based on the detected ultrasound data. For example, based on the detected inner diameter comment data of the left atrium and right atrium of the fetus, they can compare them with the normal inner diameter corresponding to the standard cycle to obtain the evaluation coefficient, and then fill the corresponding evaluation coefficient in the corresponding coefficient evaluation area of ​​the updated image to obtain the ultrasound timing diagnosis tree.

[0116] Among them, the evaluation coefficient is a coefficient for evaluating and diagnosing the results of fetal heart dynamic detection items based on the detected ultrasound data. It is a coefficient that is manually evaluated based on the examination results. When the examination data is more different from the standard normal data, the corresponding evaluation coefficient is more different from 0. When the evaluation coefficient tends to 0, it can be said that the corresponding heart examination result is more normal.

[0117] Through the above implementation, the present invention can obtain an ultrasound time series diagnostic tree, so that the dynamic inspection table corresponding to the missing date can be supplemented with data through the ultrasound time series diagnostic tree in the future, so as to provide doctors with more comprehensive reference data, thereby enhancing the accuracy of the diagnosis results.

[0118] It should be noted that the target detection personnel may not perform the inspection at the regular inspection time due to personal reasons, so that the diagnostic data of the dynamic inspection table of the inspection node connected to the corresponding date subdivision node in the ultrasound time series diagnosis tree is missing, which in turn affects the physician's diagnosis of the fetal heart. Therefore, in order to enhance the accuracy of the diagnosis result, the missing data can be accurately predicted and supplemented. Therefore, in some embodiments, it also includes:

[0119] A1, when it is determined that the dynamic inspection item corresponding to the dynamic inspection table in the ultrasound time series diagnosis tree does not have an evaluation coefficient, the corresponding dynamic inspection item is used as an item to be completed.

[0120] It can be understood that the items to be completed are dynamic examination items for which the dynamic examination items corresponding to the dynamic examination table in the ultrasound time series diagnosis tree do not have evaluation coefficients.

[0121] For example: when it is determined that the dynamic examination item (heart valve function) corresponding to the dynamic examination table does not have a corresponding evaluation coefficient, it can be said that the corresponding detection time has not been inspected, and thus the dynamic examination item in the corresponding dynamic examination table does not have a corresponding evaluation coefficient. The corresponding dynamic examination item (heart valve function) can be used as an item to be filled in, so that the missing data of the item to be filled can be predicted and filled in according to the evaluation coefficient of the same dynamic examination item.

[0122] A2, obtaining the date information of the item to be completed that does not have an evaluation coefficient as the missing date, and taking the remaining date information corresponding to the item to be completed as the reference date, and retrieving the evaluation coefficient of the item to be completed corresponding to the reference date as the reference coefficient.

[0123] It can be understood that the missing date is the date information when the item to be completed does not have an evaluation coefficient, the base date is the date information when the item to be completed has an evaluation coefficient, and the base coefficient is the evaluation coefficient of the item to be completed corresponding to the base date.

[0124] For example: when the item to be completed is heart valve function, and it is determined that the date information corresponding to the dynamic inspection table without an evaluation coefficient for heart valve function is 22nd in the March information, then March 22nd can be used as the missing date, and when the other date information containing the dynamic inspection item of heart valve function is April 8th and April 25th, the corresponding benchmark dates are April 8th and April 25th, so that the evaluation coefficients of the items to be completed (heart valve function) in the dynamic inspection table corresponding to April 8th and April 25th can be used as benchmark coefficients.

[0125] Through the above implementation, the present invention can determine the missing date, the reference date and the reference coefficient, so as to subsequently obtain the filling coefficient of the item to be filled in the corresponding missing date.

[0126] A3, according to the missing date, the base date and the base coefficient, obtain a completion coefficient of the item to be completed corresponding to the missing date.

[0127] It can be understood that the filling coefficient of the item to be filled corresponding to the missing date is determined based on the missing date, the base date and the base coefficient, so as to be filled in the corresponding area subsequently, thereby increasing the reference data for diagnosis and improving the accuracy of the diagnosis result.

[0128] The completion coefficient is a coefficient for predicting and completing the evaluation coefficient of the to-be-completed items corresponding to the dynamic checklist.

[0129] In some embodiments, the specific implementation of step A3 (obtaining the completion coefficient of the item to be completed corresponding to the missing date according to the missing date, the base date and the base coefficient) includes:

[0130] A31, sorting the missing dates and the reference dates in ascending order to obtain a sequence of completion dates corresponding to the items to be completed.

[0131] It is understandable that in order to determine the calculation method for obtaining the corresponding filling coefficient, the missing date and the reference date can be sorted in ascending order, so as to obtain a filling date sequence corresponding to the items to be filled.

[0132] The completion date sequence is a date sequence in which the missing dates and the base dates corresponding to the items to be completed are sorted in ascending order.

[0133] A32, sequentially counting adjacent missing dates in the complement date sequence to obtain multiple missing date sequences.

[0134] It should be noted that in multiple inspection times for the same dynamic inspection item, the target inspection personnel may miss multiple inspections, so that when the date sequence is completed to count the missing dates, there may be multiple missing date sequences.

[0135] It can be understood that the missing date sequence is a date sequence of statistically adjacent missing dates.

[0136] For example: when the completed date sequence is (3.4, 3.22, 4.8, 4.25, 5.12, 5.30), where the missing dates are 3.22, 5.12 and 5.30, then the two missing date sequences that can be obtained are (3.22) and (5.12, 5.30).

[0137] Through the above implementation, the present invention can obtain multiple missing date sequences, so as to subsequently determine the method for obtaining the corresponding missing date filling coefficient according to the position of the missing date sequence in the filled date sequence, so as to obtain an accurate filling coefficient and improve the accuracy of the diagnosis result.

[0138] A33, when it is determined that only one side of the missing date sequence has a reference date, the corresponding missing date sequence is used as a trend missing sequence, and the reference date is used as a trend completion date.

[0139] It can be understood that the trend missing sequence is a missing date sequence having a reference date on only one side of the missing date sequence, and the trend completion date is the reference date in the corresponding completion date.

[0140] For example: when the completed date sequence is (3.4, 3.22, 4.8, 4.25, 5.12, 5.30), the missing date sequences are (3.22) and (5.12, 5.30), among which the missing date sequence (5.12, 5.30) has a base date on only one side, so the missing date sequence (5.12, 5.30) can be used as a trend missing sequence, and since the completed date sequence (3.4, 3.22, 4.8, 4.25, 5.12, 5.30) has base dates of 3.4, 4.8 and 4.25, the base dates of 3.4, 4.8 and 4.25 can be used as trend completed dates.

[0141] Through the above implementation, the present invention can determine the trend completion date, so as to facilitate the subsequent determination of the completion coefficient corresponding to the missing date in the corresponding trend missing sequence according to the evaluation coefficient of the trend completion date.

[0142] A34, obtaining a reference coefficient corresponding to the trend completion date as a trend completion coefficient, and determining a completion coefficient of the item to be completed corresponding to the missing date in the trend missing sequence according to the trend completion coefficient and the trend completion date.

[0143] It can be understood that the trend completion coefficient and the trend completion date are used to predict and complete the completion coefficients of the items to be completed corresponding to the missing dates in the trend missing sequence.

[0144] Among them, the trend filling coefficient is the benchmark coefficient corresponding to the trend filling date.

[0145] Through the above implementation, the present invention can determine the corresponding padding coefficient, so that the padding coefficient can be subsequently filled in the corresponding coefficient evaluation area for personnel to view and refer to, thereby improving the accuracy of the diagnosis result.

[0146] In some embodiments, the specific implementation of step A34 (determining the filling coefficient of the item to be filled corresponding to the missing date in the trend missing sequence according to the trend filling coefficient and the trend filling date) includes:

[0147] A341, obtaining two adjacent trend completion dates in the completion date sequence as trend calculation dates, and retrieving trend completion coefficients corresponding to the trend calculation dates as trend calculation coefficients.

[0148] It can be understood that the trend calculation dates are the two adjacent trend completion dates in the completion date sequence. For example, when the completion date sequence is (3.4, 3.22, 4.8, 4.25, 5.12, 5.30), among which 3.4, 4.8 and 4.25 are trend completion dates, since 4.8 and 4.25 are two adjacent dates, 4.8 and 4.25 can be used as trend calculation dates.

[0149] The trend calculation coefficient is the trend filling coefficient corresponding to the trend calculation date.

[0150] Through the above implementation, the present invention can obtain the trend calculation date and the trend calculation coefficient, so that an accurate filling coefficient can be calculated for the corresponding missing date later.

[0151] A342, calculate the absolute value of the trend date difference based on the trend to obtain the trend date difference, and calculate the absolute value of the trend coefficient difference based on the trend to obtain the trend coefficient difference.

[0152] It can be understood that the trend date difference is the absolute value of the trend calculation date difference. For example, when the trend calculation dates are April 8 and April 25, the trend date difference is 17 days.

[0153] Among them, the trend coefficient difference is the absolute value of the trend calculation coefficient difference. For example, when the trend calculation dates are 4.8 and 4.25, the trend calculation coefficient corresponding to 4.8 is -2, and the trend calculation coefficient corresponding to 4.25 is -0.3, so the trend coefficient difference is 1.7.

[0154] A343, based on the ratio of the trend coefficient difference and the trend date difference, obtain the trend change rate corresponding to the two adjacent trend completion dates, and obtain the average value of the trend change rate to obtain the average change rate.

[0155] It can be understood that the trend change rate is the numerical change rate of the trend calculation coefficient, that is, the ratio of the trend coefficient difference to the trend date difference. For example, when the trend coefficient difference is 1.7 and the trend date difference is 17, the corresponding trend change rate is 1.7 / 17=0.1.

[0156] It is not difficult to understand that there may be multiple adjacent trend filling dates in the filling date sequence, so there may be multiple trend date differences and corresponding trend coefficient differences, and there will be multiple trend change rates. In order to make the predicted filling coefficient closer to the actual data, the average value of multiple trend change rates can be obtained to obtain a more accurate filling coefficient.

[0157] The average change rate is the average of all trend change rates. For example, when the multiple trend change rates corresponding to the dynamic inspection item are 0.1, 0.05, and 0.15, the average change rate can be obtained as (0.1+0.05+0.15) / 3=0.1.

[0158] A344, when it is determined that the trend missing sequence is at the end of the completion date sequence, the trend completion date adjacent to the trend missing sequence in the completion date sequence is obtained as the starting prediction date, and the trend completion coefficient corresponding to the starting prediction date is retrieved as the starting prediction coefficient.

[0159] It should be noted that since the data results of fetal heart examination are constantly changing with the fetal development cycle, when filling in missing data based on existing data, the data at the middle position is usually predicted and filled in based on the data at both ends of the missing position. However, in some special implementations, since the fetus is constantly developing, new examination items will appear at different times, resulting in no reference data for prediction before the missing date. It is necessary to determine the position of the trend missing sequence in the filling date sequence, and thus determine whether the date of missing data is before or after the trend filling date corresponding to the trend filling coefficient, and then determine whether the changing trend of the calculated filling coefficient is decreasing or increasing, so as to obtain a more accurate filling coefficient for reference by personnel in diagnosis.

[0160] It can be understood that when the trend missing sequence is at the end of the supplementary date sequence, the starting prediction date is the trend supplementary date adjacent to the trend missing sequence in the supplementary date sequence. For example, when the trend missing sequence (5.12,5.30) is at the end of the supplementary date sequence (3.4,3.22,4.8,4.25,5.12,5.30), the trend supplementary date 4.25 adjacent to the trend missing sequence (5.12,5.30) can be used as the starting prediction date.

[0161] The starting prediction coefficient is the trend filling coefficient corresponding to the starting prediction date, for example, it can be -0.3.

[0162] Through the above implementation, the present invention can obtain the starting prediction date, so as to facilitate the subsequent determination of the filling coefficient corresponding to the missing date.

[0163] A345, obtain the missing date in the trend missing sequence, obtain the predicted date difference according to the difference between the missing date and the starting predicted date, and obtain the prediction coefficient difference according to the product of the predicted date difference and the average change rate.

[0164] It can be understood that the prediction date difference is the difference between the missing date and the starting prediction date. For example, when the missing dates in the trend missing sequence are 5.12 and 5.30, and the starting prediction date is 4.25, the prediction date difference between the starting prediction date 4.25 and the missing date 5.12 is 17, and the prediction date difference between the starting prediction date 4.25 and the missing date 5.30 is 35, so that the corresponding prediction coefficient difference can be obtained according to the prediction date difference.

[0165] Among them, the prediction coefficient difference is the difference between the corresponding evaluation coefficients between the missing date and the starting prediction date, that is, the product of the prediction date difference and the average change rate. For example, when the prediction date difference is 17 and the average change rate is 0.1, the prediction coefficient difference is 1.7.

[0166] Through the above implementation, the present invention can obtain the prediction coefficient difference between each missing date and the starting prediction date, so as to facilitate the subsequent determination of the filling coefficient of the corresponding missing date.

[0167] A346, based on the sum of the starting prediction coefficient and the difference between the prediction coefficients, obtain the completion coefficient of the item to be completed corresponding to the missing date.

[0168] It should be noted that when it is determined that the trend missing sequence is at the end of the filling date sequence, since the development of the fetus is gradually strengthening, the filling coefficient corresponding to the missing date at the end should be greater than the initial prediction coefficient. Therefore, when predicting the filling coefficient corresponding to the missing date based on the trend filling coefficient of the trend filling date, it is necessary to add the initial prediction coefficient and the prediction coefficient to obtain the sum.

[0169] It can be understood that when the starting prediction coefficient and the prediction coefficient difference are determined, the corresponding starting prediction coefficient and the prediction coefficient difference can be added to obtain the sum, and the sum is the supplementary coefficient for the corresponding missing date. For example, when the starting prediction coefficient is -0.3, and the difference in prediction coefficients corresponding to the missing date 5.8 and the starting prediction date 4.25 is 1.7, then the supplementary coefficient for the missing date 5.8 can be obtained as -0.3+1.7=1.4.

[0170] Through the above implementation, the present invention can determine the filling coefficient corresponding to the missing date, so that doctors can refer to the data, thereby improving the accuracy of the diagnosis result.

[0171] A347, when it is determined that the trend missing sequence is located at the front end of the supplementary date sequence, the trend supplementary date adjacent to the trend missing sequence in the supplementary date sequence is obtained as the end prediction date, and the trend supplementary coefficient corresponding to the end prediction date is retrieved as the end prediction coefficient.

[0172] It can be understood that when the trend missing sequence is at the front end of the filling date sequence, the final prediction date is the trend filling date adjacent to the trend missing sequence in the filling date sequence. For example, when the trend missing sequence (2.20) is at the front end of the filling date sequence (2.20, 3.4, 3.22, 4.8, 4.25, 5.12, 5.30), the trend filling date 3.4 adjacent to the trend missing sequence (2.20) can be used as the final prediction date.

[0173] The final prediction coefficient is the trend filling coefficient corresponding to the final prediction date, for example, it can be -0.4.

[0174] Through the above implementation, the present invention can obtain the end prediction date and the corresponding end prediction coefficient, so as to obtain the filling coefficient corresponding to the missing date at the front end of the filling date sequence.

[0175] A348, obtain the missing date in the trend missing sequence, obtain the predicted date difference according to the difference between the last predicted date and the missing date, and obtain the prediction coefficient difference according to the product of the predicted date difference and the average change rate.

[0176] It can be understood that when the trend missing sequence is at the front end, the corresponding prediction date difference is the difference between the last prediction date and the missing date. For example, when the missing date is February 20 and the last prediction date is March 4, the corresponding prediction date difference is 12 days, and then the prediction coefficient difference is the product of the prediction date difference and the average change rate. For example, when the prediction date difference is 12 and the average change rate is 0.1, the prediction coefficient difference is 1.2.

[0177] A349, based on the difference between the final prediction coefficient and the prediction coefficient difference, obtain the completion coefficient of the item to be completed corresponding to the missing date.

[0178] It should be noted that when it is determined that the trend missing sequence is at the front end of the filling date sequence, since the development of the fetus is gradually strengthening, the filling coefficient corresponding to the missing date at the front end should be smaller than the prediction coefficient at the end. Therefore, when predicting the filling coefficient corresponding to the missing date based on the trend filling coefficient of the trend filling date, it is necessary to subtract the prediction coefficient difference from the prediction coefficient at the end.

[0179] It can be understood that the difference between the final prediction coefficient and the prediction coefficient difference is calculated to obtain the completion coefficient of the item to be completed corresponding to the corresponding missing date.

[0180] A35, when it is determined that both sides of the missing date sequence are reference dates, the corresponding missing date sequence is used as the missing sequence at both ends, and the corresponding reference date is used as the completion date at both ends.

[0181] It can be understood that the missing sequences at both ends are the missing date sequences corresponding to the case where both sides of the missing date sequence are the base dates, and the complementary dates at both ends are the base dates on both sides of the missing date sequence. For example, when the missing date sequence is (March 22), and the corresponding dates 3.4 and 4.8 on both sides are the base dates, the missing date sequence (March 22) can be used as the missing sequence at both ends, and the base dates 3.4 and 4.8 can be used as the complementary dates at both ends.

[0182] Through the above implementation, the present invention can determine the two-end filling date, so that the filling coefficient of the missing date can be determined according to the reference coefficient corresponding to the two-end filling date.

[0183] A36, obtaining a reference coefficient corresponding to the two-end filling date as the two-end filling coefficient, and determining the filling coefficient of the item to be filled corresponding to the missing date in the two-end missing sequence according to the two-end filling coefficient and the two-end filling date.

[0184] It can be understood that the padding coefficient at both ends is the base coefficient corresponding to the padding date at both ends.

[0185] It is not difficult to understand that the evaluation coefficient of the missing date in the middle can be predicted based on the filling coefficients at both ends and the filling dates at both ends, so as to obtain the filling coefficients of the items to be filled corresponding to the missing dates in the missing sequences at both ends.

[0186] In some embodiments, the specific implementation of step A36 (determining the filling coefficient of the item to be filled corresponding to the missing date in the missing sequence at both ends according to the filling coefficient at both ends and the filling date at both ends) includes:

[0187] A361, based on the sum of the coefficients padded at both ends, obtain the sum of the coefficients at both ends, and based on the sum of the dates padded at both ends, obtain the sum of the dates at both ends.

[0188] It can be understood that the sum of the coefficients at both ends is the sum of the coefficients padded at both ends, and the sum of the dates at both ends is the sum of the dates padded at both ends.

[0189] For example: when the filling coefficients at both ends are -0.4 and -2 respectively, the corresponding sum of the coefficients at both ends is -0.4+(-2)=-2.4. When the corresponding filling dates at both ends are March 4th and April 8th respectively, it is calculated from January 1st that March 4th corresponds to the 63rd day and April 8th corresponds to the 97th day, so the corresponding sum of the dates at both ends is 160 days.

[0190] Through the above implementation, the present invention can obtain the coefficients and values ​​at both ends and the dates and values ​​at both ends, so as to facilitate the subsequent calculation of the filling coefficients of the corresponding missing dates.

[0191] A362, obtaining the missing dates in the missing sequence at both ends, and obtaining the date proportion according to the ratio of the missing dates to the sum of the dates at both ends.

[0192] It can be understood that the date ratio is the ratio of the missing date to the sum of the dates at both ends. For example, when the missing date is March 22 and January 1 corresponds to the 82nd day, and the sum of the dates at both ends is 160 days, the date ratio is 82 / 160.

[0193] A363, based on the date proportion and the product of the coefficient and value at both ends, obtain the filling coefficient of the item to be filled corresponding to the missing date in the missing sequence at both ends.

[0194] It can be understood that the date proportion is multiplied by the coefficients and values ​​at both ends to obtain the filling coefficients of the missing dates in the missing sequences at both ends.

[0195] For example, when the date ratio is 82 / 160 and the sum of the coefficients at both ends is -2.4, the padding coefficient can be -2.4×(82 / 160)=-123 / 120.

[0196] S3, determining the abnormal items in the ultrasound time series diagnosis tree, and splicing the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal items to obtain an abnormal sequence table corresponding to the abnormal items.

[0197] It should be noted that in order to facilitate the diagnosis of medical personnel, the dynamic inspection tables with abnormal items can be spliced, so that the inspection result data of the same dynamic inspection item can be statistically summarized, so that personnel can quickly obtain diagnostic results through the abnormal sequence table, thereby improving diagnostic efficiency.

[0198] It can be understood that the abnormal items are dynamic inspection items with abnormal inspection results, and the abnormal sequence table is a form obtained by splicing multiple dynamic inspection tables with the same abnormal items.

[0199] In some embodiments, the specific implementation of step S3 (determining the abnormal items in the ultrasound time series diagnosis tree, and splicing the dynamic check tables in the ultrasound time series diagnosis tree based on the abnormal items to obtain the abnormal sequence table corresponding to the abnormal items) includes:

[0200] S31, obtaining medical indicators of ultrasound data corresponding to each dynamic examination item in the ultrasound time series diagnosis tree, wherein the medical indicators at least include inner diameter, area and length.

[0201] It is understandable that in order to determine whether each dynamic examination item has an abnormality, the medical indicators of the corresponding ultrasound data can be retrieved for subsequent comparison with the corresponding standard normal data, thereby automatically obtaining abnormal results.

[0202] Among them, medical indicators are indicators of medical examinations, including inner diameter, area and length, such as the inner diameter of the left ventricle and the inner diameter of the right ventricle of the heart.

[0203] S32, retrieving the standard index interval of the dynamic examination item corresponding to each date information in the ultrasound time series diagnosis tree.

[0204] It should be noted that since the fetus is constantly developing with the changes in the gestational cycle, for example, the ventricles grow from non-existence to existence and then from small to large, the standard values ​​corresponding to the medical indicators when examined at different times are also different. Therefore, the standard indicator ranges for the corresponding dynamic examination items can be retrieved based on the date information.

[0205] It can be understood that the standard indicator range is the standard normal data range of the corresponding indicators of the dynamic detection items. For example, the normal right atrial diameter range corresponding to the 17th to 28th week of pregnancy is (7.1mm, 11.3mm), and the left atrial diameter range is (6.3mm, 8.9mm).

[0206] S33, when it is determined that the medical indicator is not within the standard indicator range, the corresponding dynamic examination item is regarded as an abnormal item.

[0207] It is understandable that when the corresponding medical index is not within the standard index range, it can be said that the examination result of the corresponding dynamic examination item is abnormal, so that the dynamic examination item can be regarded as an abnormal item.

[0208] Through the above implementation, the present invention can determine abnormal items, so as to facilitate the subsequent splicing of dynamic inspection tables with corresponding abnormal items to obtain an abnormal sequence table.

[0209] S34, based on the abnormal items, the ultrasonic filling areas in the dynamic inspection tables are intercepted as the areas to be spliced, and the areas to be spliced ​​are sequentially spliced ​​according to the detection time to obtain an abnormal sequence table corresponding to the abnormal items.

[0210] It can be understood that the area to be spliced ​​is the ultrasonic filling area corresponding to the abnormal item in the dynamic inspection table, so that the intercepted area to be spliced ​​can be spliced ​​in chronological order according to the detection time, so as to obtain the abnormal sequence list corresponding to the spliced ​​abnormal item.

[0211] Through the above implementation, the present invention can obtain an abnormal sequence list, so that doctors can quickly diagnose congenital heart disease according to the abnormal sequence list corresponding to the abnormal items, thereby improving the diagnosis efficiency.

[0212] It should be noted that when there are abnormal items in the dynamic inspection table corresponding to the missing date, there is no corresponding ultrasound image data in the to-be-joined area corresponding to the detection time in the spliced ​​abnormal sequence table. In order for personnel to clearly observe the image data of the corresponding abnormal items, the ultrasound data corresponding to the reference date can be used to complete the ultrasound filling area of ​​the missing date for display, so that personnel can view it. Therefore, in some embodiments, it also includes:

[0213] The medical index corresponding to the missing date is obtained based on the supplemented data, and the ultrasound data is scaled according to the medical index to obtain a supplementary image corresponding to the missing date.

[0214] It can be understood that the medical indicators of the corresponding abnormal items of the missing date can be obtained by comparing the completed data corresponding to the missing date with the evaluation coefficient corresponding to the reference time. For example, when the medical indicator of the reference date is an inner diameter of 6 mm, the corresponding evaluation coefficient is -1, and the completion coefficient of the missing date is -1.5, so the medical indicator corresponding to the missing date can be obtained as an inner diameter of 4 mm. Therefore, the medical indicator of the reference date can be compared with the medical indicator of the missing date to obtain the corresponding scaling factor, for example, the scaling factor is 2 / 3, and then the ultrasound data of the inner diameter of 6 mm on the reference date can be scaled according to the scaling factor to obtain the supplementary image of the missing date.

[0215] The supplementary image is the ultrasound data image corresponding to the missing date, and the scaling factor is the proportional coefficient for scaling the ultrasound data of the reference date.

[0216] Through the above implementation, the present invention can obtain a supplementary image corresponding to the missing date, so as to facilitate subsequent filling in the corresponding area to be spliced.

[0217] The supplementary image is retrieved and filled into the area to be spliced ​​corresponding to the corresponding missing time in the abnormal sequence table.

[0218] It can be understood that the supplementary image is filled in the area to be spliced ​​corresponding to the corresponding missing time in the abnormal sequence list, so as to complete the ultrasound data of the corresponding detection time in the abnormal sequence list, and obtain an abnormal sequence list of complete data for diagnosis and review by personnel, so as to quickly locate the corresponding cause of heart disease based on the examination results of the abnormal items and improve the diagnostic efficiency.

[0219] S4, generating a heart prediction change graph based on the trend prediction strategy and the ultrasound data in the abnormal sequence table and sending it to the detection end.

[0220] It should be noted that in order to clearly display the changes in the examination results of the corresponding dynamic examination items, a heart prediction change graph can be generated based on the ultrasound data in the abnormal sequence table, so that medical staff can intuitively view the changing trends of the examination results of the corresponding dynamic examination items through the heart prediction change graph, thereby quickly diagnosing the corresponding fetal heart condition.

[0221] It can be understood that the heart prediction change graph is a data change graph of the heart ultrasound examination results, and the detection end is a signal terminal corresponding to the heart detection and diagnosis personnel, such as a mobile phone, computer, etc.

[0222] In some embodiments, the specific implementation of step S4 (generating a cardiac prediction change map based on the trend prediction strategy and the ultrasound data in the abnormal sequence table and sending it to the detection end) includes:

[0223] S401, obtaining the medical index of the ultrasound data corresponding to each detection time in the abnormal sequence table as a construction index, and the standard index interval corresponding to each detection time.

[0224] It should be noted that in order to determine the data coordinates corresponding to each detection time in the heart prediction change diagram, the medical indicators of the corresponding ultrasound data and the corresponding standard indicator range can be obtained, so as to facilitate the subsequent determination of the corresponding data change curve.

[0225] It can be understood that the constructed index is a medical index corresponding to the ultrasound data at each detection time in the abnormal sequence table.

[0226] S402: When it is determined that the construction index is smaller than the standard index interval, the minimum value of the standard index interval is retrieved as the minimum reference value.

[0227] It can be understood that when the constructed indicator is smaller than the standard indicator interval, the minimum benchmark value is the minimum value of the standard indicator interval.

[0228] For example, when the detected construction index of the right atrium is 6.1 mm in inner diameter, which is smaller than the standard index interval (7.1 mm, 11.3 mm), the minimum value of the standard index interval, 7.1 mm, can be used as the minimum reference value.

[0229] S403, obtaining a negative anomaly difference based on the difference between the construction indicator and the minimum benchmark value, obtaining a negative anomaly proportion based on the ratio of the negative anomaly difference to the minimum benchmark value, and obtaining the detection time of the construction indicator corresponding to the negative anomaly proportion as the negative anomaly time.

[0230] It can be understood that the negative abnormal difference is the difference between the construction index and the minimum benchmark value. For example, when the construction index is an inner diameter of 6.1 mm and the minimum benchmark value is 7.1 mm, the negative abnormal difference is 6.1-7.1=-1, and the negative abnormality ratio is the ratio of the negative abnormal difference to the minimum benchmark value, that is, -1 / 7.1.

[0231] Among them, the negative anomaly time is the detection time of the indicator corresponding to the negative anomaly ratio.

[0232] Through the above implementation, the present invention can determine the negative abnormality ratio and negative abnormality time, so as to obtain the corresponding heart prediction change diagram later.

[0233] S404: When it is determined that the construction index is greater than the standard index interval, the maximum value of the standard index interval is retrieved as the maximum reference value.

[0234] It can be understood that when the constructed indicator is greater than the standard indicator interval, the maximum benchmark value is the maximum value of the standard indicator interval.

[0235] For example, when the constructed index is that the inner diameter of the right atrium is 12.43 mm, which is greater than the maximum value of the standard index interval (7.1 mm, 11.3 mm), the maximum reference value can be 11.3 mm.

[0236] S405, obtaining a positive anomaly difference based on the difference between the construction index and the maximum benchmark value, obtaining a positive anomaly proportion based on the ratio of the positive anomaly difference to the maximum benchmark value, and obtaining the detection time of the construction index corresponding to the positive anomaly proportion as the positive anomaly time.

[0237] It can be understood that the positive anomaly difference is the difference between the construction index and the maximum reference value. For example, when the construction index is 12.43 mm and the maximum reference value is 11.3 mm, the corresponding positive anomaly difference is 12.43-11.3=1.13 mm.

[0238] Among them, the positive anomaly ratio is the ratio of the positive anomaly difference to the maximum benchmark value. For example, when the positive anomaly difference is 1.13mm and the maximum benchmark value is 11.3mm, the positive anomaly ratio is 0.2, and the positive anomaly time is the detection time of the indicator constructed corresponding to the positive anomaly ratio, such as March 22.

[0239] Through the above implementation, the present invention can determine the corresponding positive abnormality ratio and the corresponding positive abnormality time, so as to generate the corresponding heart prediction change diagram later.

[0240] S406: When it is determined that the construction indicator is within the standard indicator interval, the detection time corresponding to the construction indicator is used as the standard time, and a preset standard proportion is configured for the standard time.

[0241] It can be understood that when the construction indicator is in the standard indicator range, the standard time is the detection time corresponding to the construction indicator, and the preset standard proportion is the proportion value of the standard configured according to the standard time, which can be preset manually.

[0242] S407, call up the preset base plate image and the preset change coordinate system, obtain the center point of the preset base plate image, align the origin of the preset change coordinate system with the center point, the horizontal axis of the preset change coordinate system has multiple preset time points, and the vertical axis of the preset change coordinate system has multiple preset proportions.

[0243] It can be understood that the preset base plate image is a preset blank base plate image, which is used to display the negative abnormality ratio, positive abnormality ratio and preset standard ratio corresponding to different detection times to generate a blank interface of the predicted heart change diagram, and the preset change coordinate system is a preset coordinate system, which is used to determine the corresponding coordinate points of the negative abnormality ratio, positive abnormality ratio and preset standard ratio so as to obtain the predicted heart change diagram.

[0244] Among them, the preset time point is the corresponding time coordinate point on the horizontal axis, which can be one-to-one corresponding to the detection time, and the preset proportion is the proportion coordinate point on the vertical axis, so as to determine the coordinate position of the positive abnormal proportion and the negative abnormal proportion.

[0245] S408: Determine a negative trend point in the preset change coordinate system according to the negative abnormal time and the negative abnormal proportion.

[0246] It can be understood that the negative trend point is a position point determined in a preset change coordinate system based on the negative abnormal time and the negative abnormal proportion, that is, the horizontal coordinate of the negative trend point corresponds to the negative abnormal time, and the vertical coordinate corresponds to the position point of the negative abnormal proportion.

[0247] S409: Determine a positive trend point in the preset change coordinate system based on the positive abnormality time and the positive abnormality ratio.

[0248] It can be understood that the positive trend point is a position point determined in a preset changing coordinate system based on the positive anomaly time and the positive anomaly ratio, that is, the horizontal coordinate of the positive trend point corresponds to the positive anomaly time, and the vertical coordinate corresponds to the position point of the positive anomaly ratio.

[0249] S410: Determine a standard trend point in the preset change coordinate system according to the standard time and the preset standard proportion.

[0250] It can be understood that the standard trend point is a position point determined in a preset change coordinate system based on the standard time and the preset standard proportion, that is, the horizontal coordinate of the standard trend point corresponds to the standard time, and the vertical coordinate corresponds to the position point of the preset standard proportion.

[0251] S411, sorting the detection times in the abnormal sequence table in ascending order to obtain a time connection sequence, connecting the negative trend point, the positive trend point and the standard trend point in sequence based on the order of the detection times in the time connection sequence, generating a heart prediction change diagram and sending it to the detection end.

[0252] It should be noted that in order to connect the corresponding position points according to the order of actual detection time, thereby generating the corresponding predicted heart change diagram, the detection times in the abnormal sequence table can be sorted in ascending order so that they can be connected according to the time series to obtain the predicted heart change diagram.

[0253] It can be understood that the time connection sequence is the time series in which the detection times in the abnormal sequence list are sorted in ascending order, so that the negative trend points, positive trend points and standard trend points corresponding to the detection time can be sequentially connected according to the time connection sequence to obtain the predicted heart change diagram and send it to the detection end for personnel to view intuitively.

[0254] For example, Figure 3 As shown, when the time connection sequence is (3.4, 3.22, 4.8, 4.25, 5.12), the corresponding trend points can be connected according to the time connection sequence to obtain the heart prediction change diagram of the corresponding dynamic detection item.

[0255] It is not difficult to understand that different dynamic detection items have corresponding predicted heart change graphs, so that detection and diagnosis can be performed based on the predicted heart change graphs corresponding to the dynamic detection items, so as to improve the diagnosis efficiency and improve the accuracy of the diagnosis results.

[0256] See also Figure 4, is a schematic diagram of a congenital heart disease progression assessment system based on ultrasound data provided by an embodiment of the present invention, the congenital heart disease progression assessment system based on ultrasound data comprising:

[0257] A generation module is used to obtain a dynamic check list of target detection personnel corresponding to the detection time, and retrieve the attribute information of the target detection personnel, and generate an initial timing diagnosis tree according to the attribute information and the dynamic check list.

[0258] A filling module is used to retrieve the ultrasound data intercepted by the physician, obtain the annotation information and interception time of the ultrasound data, and fill the corresponding ultrasound data into the dynamic inspection table corresponding to the inspection node in the initial timing diagnosis tree based on the annotation information and the interception time to obtain the ultrasound timing diagnosis tree.

[0259] The splicing module is used to determine the abnormal items in the ultrasound time series diagnosis tree, and splice the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal items to obtain an abnormal sequence table corresponding to the abnormal items.

[0260] The sending module is used to generate a heart prediction change diagram based on the trend prediction strategy and the ultrasound data in the abnormal sequence table and send it to the detection end.

[0261] See also Figure 5 , is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention, the electronic device 50 includes: a processor 51, a memory 52 and a computer program; wherein

[0262] The memory 52 is used to store the computer program, and the memory may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0263] The processor 51 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0264] Optionally, the memory 52 may be independent or integrated with the processor 51 .

[0265] When the memory 52 is a device independent of the processor 51, the device may further include:

[0266] The bus 53 is used to connect the memory 52 and the processor 51 .

[0267] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0268] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, abbreviated as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0269] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0270] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0271] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the progression of congenital heart disease based on ultrasound data, characterized in that: include: Obtain a dynamic checklist of target detection personnel corresponding to detection time, retrieve attribute information of the target detection personnel, and generate an initial time series diagnosis tree according to the attribute information and the dynamic checklist; Retrieving ultrasound data intercepted by the physician, obtaining annotation information and interception time of the ultrasound data, and filling the corresponding ultrasound data into a dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time, to obtain an ultrasound time series diagnosis tree; Determine an abnormal item in the ultrasound time series diagnosis tree, and perform splicing processing on each of the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal item to obtain an abnormal sequence table corresponding to the abnormal item; According to the trend prediction strategy and the ultrasound data in the abnormal sequence table, a heart prediction change map is generated and sent to the detection end.

2. The method according to claim 1, characterized in that The step of obtaining a dynamic checklist of detection times corresponding to target detection personnel, retrieving attribute information of the target detection personnel, and generating an initial time series diagnosis tree according to the attribute information and the dynamic checklist includes: Obtaining a dynamic inspection table corresponding to the inspection time of the target inspection personnel, and retrieving the attribute information of the target inspection personnel, wherein the dynamic inspection table includes an ultrasonic filling area and a coefficient evaluation area corresponding to a plurality of dynamic inspection items; Constructing a person node according to the attribute information, extracting the month information and date information in the detection time, constructing a month summary node based on the month information, and connecting the month summary node with the person node; Constructing a date subdivision node based on the date information, connecting the date subdivision node with the corresponding month summary node, and constructing a check node connected to the date subdivision node; The dynamic check table is bound to the corresponding check node according to the detection time to obtain an initial timing diagnosis tree.

3. The method according to claim 2, characterized in that The method of retrieving ultrasound data intercepted by the physician end, obtaining annotation information and interception time of the ultrasound data, and filling the corresponding ultrasound data into a dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time, to obtain the ultrasound time series diagnosis tree, includes: Retrieving ultrasound data intercepted by the physician, and obtaining annotation information and interception time of the ultrasound data; Determine the corresponding month summary node as the month update node and the date subdivision node as the date update node based on the interception time, and use the check node connected to the date update node as the node to be updated; Determine a dynamic check table corresponding to the node to be updated as a dynamic update table, and locate a dynamic check item in the dynamic update table as an update item based on the annotation information; The ultrasound data is filled into the ultrasound filling area corresponding to the update item, and the evaluation coefficient of the ultrasound data by the physician is received, and the evaluation coefficient is filled into the coefficient evaluation area corresponding to the update item to obtain an ultrasound timing diagnosis tree.

4. The method according to claim 3, characterized in that Also includes: When it is determined that the dynamic inspection item corresponding to the dynamic inspection table in the ultrasound time series diagnosis tree does not have an evaluation coefficient, the corresponding dynamic inspection item is used as an item to be completed; Obtaining date information of the item to be completed that does not have an evaluation coefficient as the missing date, and taking the remaining date information corresponding to the item to be completed as the reference date, and retrieving the evaluation coefficient of the item to be completed corresponding to the reference date as the reference coefficient; According to the missing date, the base date and the base coefficient, a completion coefficient of the item to be completed corresponding to the missing date is obtained.

5. The method according to claim 4, characterized in that The obtaining, according to the missing date, the base date and the base coefficient, a filling coefficient of the item to be filled corresponding to the missing date includes: Sort the missing dates and the reference dates in ascending order to obtain a sequence of completion dates corresponding to the items to be completed; Sequentially counting the adjacent missing dates in the complement date sequence to obtain multiple missing date sequences; When it is determined that only one side of the missing date sequence has a reference date, the corresponding missing date sequence is used as a trend missing sequence, and the reference date is used as a trend completion date; Acquire a reference coefficient corresponding to the trend completion date as a trend completion coefficient, and determine a completion coefficient of the to-be-completed item corresponding to the missing date in the trend missing sequence according to the trend completion coefficient and the trend completion date; When it is determined that both sides of the missing date sequence are reference dates, the corresponding missing date sequence is used as the missing sequence at both ends, and the corresponding reference date is used as the completion date at both ends; A reference coefficient corresponding to the two-end filling date is obtained as the two-end filling coefficient, and a filling coefficient of the item to be filled corresponding to the missing date in the two-end missing sequence is determined according to the two-end filling coefficient and the two-end filling date.

6. The method according to claim 5, characterized in that The determining, according to the trend filling coefficient and the trend filling date, the filling coefficient of the item to be filled corresponding to the missing date in the trend missing sequence includes: Obtaining two adjacent trend completion dates in the completion date sequence as trend calculation dates, and retrieving trend completion coefficients corresponding to the trend calculation dates as trend calculation coefficients; Calculate the absolute value of the trend date difference according to the trend to obtain the trend date difference, and calculate the absolute value of the trend coefficient difference based on the trend to obtain the trend coefficient difference; Based on the ratio of the trend coefficient difference and the trend date difference, the trend change rate corresponding to the two adjacent trend completion dates is obtained, and the average value of the trend change rate is obtained to obtain the average change rate; When it is determined that the trend missing sequence is at the end of the supplementary date sequence, a trend supplementary date adjacent to the trend missing sequence in the supplementary date sequence is obtained as a starting prediction date, and a trend supplementary coefficient corresponding to the starting prediction date is retrieved as a starting prediction coefficient; Obtaining the missing date in the trend missing sequence, obtaining the predicted date difference according to the difference between the missing date and the starting predicted date, and obtaining the prediction coefficient difference according to the product of the predicted date difference and the average change rate; According to the sum of the starting prediction coefficient and the difference of the prediction coefficient, the filling coefficient of the item to be filled corresponding to the missing date is obtained; When it is determined that the trend missing sequence is located at the front end of the supplementary date sequence, the trend supplementary date adjacent to the trend missing sequence in the supplementary date sequence is obtained as the end prediction date, and the trend supplementary coefficient corresponding to the end prediction date is retrieved as the end prediction coefficient; Obtain the missing date in the trend missing sequence, obtain the predicted date difference according to the difference between the last predicted date and the missing date, and obtain the prediction coefficient difference according to the product of the predicted date difference and the average change rate; According to the difference between the final prediction coefficient and the prediction coefficient difference, the filling coefficient of the item to be filled corresponding to the missing date is obtained.

7. The method according to claim 5, characterized in that The step of determining the filling coefficient of the item to be filled corresponding to the missing date in the missing sequence at both ends according to the filling coefficient at both ends and the filling date at both ends includes: According to the sum of the padded coefficients at both ends, the sum of the coefficients at both ends is obtained, and based on the sum of the padded dates at both ends, the sum of the dates at both ends is obtained; Obtain the missing dates in the missing sequence at both ends, and obtain the date proportion according to the ratio of the missing dates to the sum of the dates at both ends; Based on the date proportion and the product of the coefficient and value at both ends, the filling coefficient of the to-be-filled item corresponding to the missing date in the missing sequence at both ends is obtained.

8. The method according to claim 3, characterized in that The determining of the abnormal items in the ultrasound time series diagnosis tree, and splicing the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal items to obtain an abnormal sequence table corresponding to the abnormal items, includes: Acquire medical indicators of ultrasound data corresponding to each dynamic examination item in the ultrasound time series diagnosis tree, wherein the medical indicators at least include inner diameter, area and length; Retrieving the standard index interval of the dynamic examination item corresponding to each date information in the ultrasound time series diagnosis tree; When it is determined that the medical indicator is not within the standard indicator range, treating the corresponding dynamic examination item as an abnormal item; Based on the abnormal items, the ultrasonic filling areas in each of the dynamic inspection tables are intercepted as the areas to be spliced, and the areas to be spliced ​​are sequentially spliced ​​according to the detection time to obtain an abnormal sequence table corresponding to the abnormal items.

9. The method according to claim 8, characterized in that The step of generating a heart prediction change graph based on the trend prediction strategy and the ultrasound data in the abnormal sequence table and sending the graph to the detection end includes: Obtaining the medical index of the ultrasound data corresponding to each detection time in the abnormal sequence table as a construction index, and the standard index interval corresponding to each detection time; When it is determined that the construction index is less than the standard index interval, the minimum value of the standard index interval is retrieved as the minimum reference value; According to the difference between the construction index and the minimum reference value, a negative anomaly difference is obtained, based on the ratio of the negative anomaly difference to the minimum reference value, a negative anomaly proportion is obtained, and the detection time of the construction index corresponding to the negative anomaly proportion is obtained as the negative anomaly time; When it is determined that the construction index is greater than the standard index interval, the maximum value of the standard index interval is retrieved as the maximum reference value; According to the difference between the construction index and the maximum reference value, a positive anomaly difference is obtained, based on the ratio of the positive anomaly difference to the maximum reference value, a positive anomaly proportion is obtained, and the detection time of the construction index corresponding to the positive anomaly proportion is obtained as the positive anomaly time; When it is determined that the construction indicator is within the standard indicator range, the detection time corresponding to the construction indicator is used as the standard time, and a preset standard proportion is configured for the standard time; Retrieving a preset base plate image and a preset change coordinate system, obtaining the center point of the preset base plate image, aligning the origin of the preset change coordinate system with the center point, wherein the horizontal axis of the preset change coordinate system has a plurality of preset time points, and the vertical axis of the preset change coordinate system has a plurality of preset proportions; Determine a negative trend point in the preset change coordinate system according to the negative abnormal time and the negative abnormal proportion; Determine a positive trend point in the preset change coordinate system based on the positive abnormality time and the positive abnormality proportion; Determine a standard trend point in the preset change coordinate system according to the standard time and the preset standard proportion; The detection times in the abnormal sequence table are sorted in ascending order to obtain a time connection sequence, and the negative trend points, the positive trend points and the standard trend points are connected in sequence based on the order of the detection times in the time connection sequence to generate a heart prediction change graph and send it to the detection end.

10. A congenital heart disease progression assessment system based on ultrasound data, characterized in that: include: A generation module, used to obtain a dynamic checklist of target detection personnel corresponding to detection time, and retrieve attribute information of the target detection personnel, and generate an initial time series diagnosis tree according to the attribute information and the dynamic checklist; A filling module is used to retrieve the ultrasound data intercepted by the physician, obtain the annotation information and the interception time of the ultrasound data, and fill the corresponding ultrasound data into the dynamic inspection table corresponding to the inspection node in the initial time series diagnosis tree based on the annotation information and the interception time to obtain the ultrasound time series diagnosis tree; A splicing module, used for determining abnormal items in the ultrasound time series diagnosis tree, splicing each of the dynamic inspection tables in the ultrasound time series diagnosis tree based on the abnormal items, and obtaining an abnormal sequence table corresponding to the abnormal items; The sending module is used to generate a heart prediction change diagram based on the trend prediction strategy and the ultrasound data in the abnormal sequence table and send it to the detection end.

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