Ultrasound and contrast data based treatment plan assistance generation system for arterial lesions

Abstract

The application discloses an arteriopathy treatment scheme auxiliary generation system based on ultrasonic and contrast data, belongs to the technical field of data processing, and comprises a data model storage unit for storing various relationship models, an acquisition unit for acquiring medical record data, a reference case database generation unit for screening similar cases and storing the medical record data of the similar cases as a reference case database, a data anomaly screening unit for screening output abnormal data and missing data, a reference scheme generation unit for confirming an output reference treatment scheme, and an interaction unit for realizing data and information interaction between the system and a user. The system ensures the accuracy and integrity of detection data through correlation analysis between various detection data, automatically generates a reference treatment scheme based on a case database, locks a risk point in surgery, provides strong assistance and support for accurate treatment of doctors, and improves the treatment effect of arteriopathy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and relates to an arterial lesion treatment scheme auxiliary generation system based on ultrasonic and contrast data. BACKGROUND

[0002] The lower limbs are the common sites of arteriosclerosis obliterans, and the root cause of lower limb arteriosclerosis obliterans is vascular plaque (atherosclerosis): a plaque like porridge formed after the deposition of lipids, cholesterol, calcium and other substances under the intima of the artery, which can cause the arterial wall to thicken and harden, lose elasticity, and the lumen to narrow or even occlude, ultimately affecting the normal flow of blood.

[0003] The formation of vascular plaques is related to many disease influencing factors, mainly including high blood lipids, high blood pressure, diabetes, smoking, obesity, lack of exercise, genetic factors, age growth, chronic inflammatory reaction, etc. These factors can damage the vascular endothelium, promote lipid deposition or induce inflammation, and ultimately lead to plaque formation.

[0004] Generally, when a patient presents with suspected atherosclerosis symptoms such as cold lower limbs, intermittent claudication, rest pain or gangrene, the doctor will first perform an arterial ultrasound examination to obtain data such as vascular wall structure, plaque properties, blood flow velocity and direction, and preliminarily judge the degree of arterial stenosis based on the analysis results of the above data. When severe stenosis or occlusion is detected by ultrasound, interventional surgery needs to be considered. In order to improve the effect of surgical treatment and reduce the risk of surgery, angiography is usually performed before surgery to further determine the shape of the vascular lumen, the position and degree of stenosis, collateral circulation and other data, and in complex cases, intravascular ultrasound (IVUS) examination is also required to provide more accurate images from the inside of the blood vessel (IVUS measurement indicators of atherosclerotic plaques include: plaque CSA (cross-sectional area), maximum plaque thickness, minimum plaque thickness, plaque eccentricity, plaque burden and lumen area stenosis rate; calcified lesions are semi-quantitatively analyzed according to the quadrant occupied by calcification). In existing applications, after the doctor obtains the above detection data, he needs to formulate a corresponding surgical plan based on his own experience and judgment, such as determining the puncture point, stent length and type (stent implantation) according to the stenosis position, length and degree, and determining the thrombolysis method according to the stability of the plaque.

[0005] In practical applications, since atherosclerosis treatment involves a large amount of detection data, and the treatment of a certain lesion position in the blood vessel during the operation is likely to cause a chain reaction at the associated position (for example, unstable plaque fragments are detached during the operation and flow along the blood vessel blood, causing distal embolism, etc.), it is necessary to ensure that the detection data is accurate and the operation scheme is comprehensive. Obviously, if the doctor relies entirely on the accurate analysis of the above detection data and formulates a comprehensive and clear treatment plan, it will not only significantly increase the doctor's work intensity, but also increase the risk of operation. SUMMARY

[0006] In order to reduce the work intensity of the doctor and improve the treatment effect of atherosclerosis, the present application aims to provide an atherosclerosis treatment scheme auxiliary generation system based on ultrasound and angiography data, which analyzes the correlation between various detection data to ensure the accuracy and integrity of the detection data, and automatically generates a reference treatment scheme based on a large case database, locks the risk points in the operation, and provides strong assistance and support for the accurate treatment of doctors, and improves the treatment effect of atherosclerosis. The specific scheme is as follows:

[0007] An atherosclerosis treatment scheme auxiliary generation system based on ultrasound and angiography data, comprising:

[0008] A data model storage unit configured to store a first relationship model reflecting the correlation between various data in the arterial ultrasound data and the arterial angiography data, a second relationship model reflecting the correlation between the various arterial ultrasound data and the arterial angiography data and various disease influencing factors, and a third relationship model reflecting the correlation between the reference treatment scheme and the arterial ultrasound data and / or the arterial angiography data;

[0009] A data acquisition unit configured to acquire the medical record data of the current patient;

[0010] A reference case database generation unit configured to connect with the large case database data, acquire and filter similar cases and their medical record data from the large case database according to the medical record data of the current patient, and store them as a reference case database;

[0011] A data anomaly screening unit configured to receive the medical record data of the current patient and output abnormal data and missing data according to the first relationship model, the second relationship model and the reference case database;

[0012] A reference scheme generation unit configured to output a reference treatment scheme according to the medical record data of the current patient and the third relationship model;

[0013] An interactive unit, including data information input and output components, configured to realize data and information interaction between the system and the user;

[0014] The data acquisition unit is connected with the data anomaly screening unit, and responds to the abnormal data or missing data to automatically acquire or review relevant medical record data, or outputs a data acquisition request through the interactive unit and monitors medical record data input by the user in response;

[0015] The medical record data includes one or more combinations of various disease influencing factors, arterial ultrasound data, and arteriography data;

[0016] The data anomaly screening unit includes:

[0017] The first screening subunit is configured to screen and output first abnormal data according to the arterial ultrasound data and / or the arteriography data in combination with the first relationship model, and mark it as a first type of anomaly;

[0018] The second screening subunit is configured to screen and output second abnormal data according to the disease influencing factors in combination with the second relationship model, and mark it as a second type of anomaly;

[0019] The third screening subunit is configured to compare and determine third abnormal data and missing data in the current patient medical record data according to the reference case database, and mark it as a third type of anomaly;

[0020] The anomaly prompt subunit is configured to remind the user of the data anomaly through the interactive unit in a set display mode according to the type of the data anomaly.

[0021] Through the technical solution, when the user inputs the medical record data of the current patient, the system can find and match similar cases and their medical record data from the medical record database to form a reference case database, thereby providing a reliable basis for subsequent data accuracy and screening and customization of the scheme. By auditing the ultrasound data / angiography data of the current patient itself and the correlation between the two, the accuracy of the ultrasound and angiography data is preliminarily determined, and then the accuracy of the data is determined again according to the correlation between the ultrasound / angiography data and each disease influencing factor in the medical record data of the current patient, and finally the reference case database is introduced for the final data accuracy screening (including missing data screening), thereby ensuring the accuracy, integrity and reliability of the relevant medical record data and providing data guarantee for the generation of the reference treatment scheme. In the technical solution, the acquisition of each item of medical record data is verified for correlation, which can ensure the integrity of the required data while avoiding unnecessary data collection. For example, if the arterial ultrasound examination shows that the patient's blood vessels only have mild stenosis, only drug treatment is needed, and further arterial angiography is not needed, thereby avoiding excessive medical treatment and examination. By marking the three types of data as abnormal and displaying them in different ways, the user can be effectively reminded to pay attention to abnormal data, and the user can focus more attention on the preparation of the treatment scheme rather than data checking and other work. In combination with the provided reference treatment scheme, the user can subsequently customize a more accurate treatment scheme.

[0022] Optionally, the reference case database generation unit comprises:

[0023] a medical record data analysis subunit configured to analyze one or more of the disease influencing factors, the arterial ultrasound data and the arterial angiography data from the medical record data of the current patient;

[0024] a similarity determination algorithm subunit configured to determine the similarity of the arterial ultrasound data and / or the arterial angiography data of the current patient to the relevant medical record data in the case database according to a similarity determination algorithm matched or generated according to at least one specific disease influencing factor in the medical record data of the current patient;

[0025] a first matching subunit configured to match the same or similar cases and their medical record data from the case database according to the analyzed arterial ultrasound data and / or arterial angiography data, to form a first intermediate database;

[0026] a second matching subunit configured to match the same or similar cases and their medical record data from the medical record database according to the analyzed disease influencing factors, to form a second intermediate database;

[0027] The database fusion subunit is configured to obtain data of the first intermediate database and the second intermediate database, and take intersection cases and their medical record data to form the reference case database.

[0028] The similarity determination algorithm subunit includes:

[0029] The weight configuration relationship table is configured to store a corresponding relationship between one or more specific disease influencing factors and similarity determination weight coefficients of each item of data in the arterial ultrasound data and the arterial angiography data.

[0030] The similarity score relationship table is configured to store similarity scores corresponding to each item of data difference range in the arterial ultrasound data and the arterial angiography data.

[0031] The similarity determination module is configured to obtain arterial ultrasound data and / or arterial angiography data and each item of data value contained in the current patient medical record data, compare the data with relevant data contained in each case in the case database, obtain similarity score values of each item of data, and then calculate the similarity according to the similarity determination weight coefficients corresponding to each item of data.

[0032] Each case and its medical record data in the first intermediate database, the second intermediate database, and the reference case database are stored in order according to the similarity.

[0033] If the number of cases in the reference case database is lower than a set value, the same number or a set proportion of cases and their medical record data are extracted from the first intermediate database and the second intermediate database for data expansion according to the case order until the number of cases in the reference case database reaches the set value.

[0034] The above technical solution can determine the generation condition of the reference case database according to one or more disease influencing factors of the current patient, so that the data in the generated reference case database has more reference value. Meanwhile, the number of reference cases in the reference case database is maintained at a set value according to the similarity, which is helpful for generating a personalized data model and improving the accuracy of the reference treatment scheme.

[0035] Optionally, the reference case database generation unit further includes:

[0036] The database correction subunit is configured to obtain relevant medical record data after review or user feedback input medical record data and temporarily store the data as correction data, and find similarity determination weight coefficients corresponding to the correction data from the weight configuration relationship table.

[0037] The difference between the correction data and the corresponding original data is combined with the similarity determination weight coefficients to calculate the difference between the two similarity scores before and after the correction.

[0038] If the difference of the similarity scores is lower than a set value, the reference case database is not updated, otherwise, similar cases and their medical record data are re-screened from the case database according to the correction data to form a new reference case database.

[0039] By the technical solution, the cases in the reference case database for reference have reference value, which is beneficial to improving the accuracy of the later reference treatment scheme.

[0040] Optionally, the third screening subunit comprises:

[0041] a data missing checking module configured to compare and find missing medical record data in the current patient's medical record data according to the cases and their medical record data in the reference case database, and output the missing data;

[0042] a first personalized relationship model generating module configured to generate a first personalized relationship model based on multi-factor analysis or neural network training according to the cases and their medical record data in the reference case database, the first personalized relationship model being used to reflect the correlation between each item of the arterial ultrasound data and the arterial angiography data, and the correlation between each item of the arterial ultrasound data and the arterial angiography data and each disease influencing factor;

[0043] a data anomaly output module configured to match and output the third abnormal data according to the current patient's medical record data and the first personalized relationship model.

[0044] By the technical solution, the first personalized relationship model trained and generated based on the reference case database is more suitable for the judgment of the current patient's medical record data anomaly, and the third abnormal data generated is more accurate than the abnormal data generated by using the first relationship model and the second relationship model, which is convenient for the user to make a reasonable and comprehensive judgment on the data accuracy; at the same time, the data missing checking module can check and output the data category that has a low similarity weight coefficient but is related to the generation of the reference treatment scheme, thereby ensuring the integrity of the current patient's medical record data.

[0045] Optionally, the data acquisition unit comprises:

[0046] a data retrieval subunit configured to retrieve and aggregate the current patient's medical record data in a set format from a data storage area storing each item of the current patient's medical record data;

[0047] a data review subunit configured to receive and respond to the abnormal data output by the data anomaly screening unit, retrieve the corresponding original data from the current patient's medical record data and verify, output a user confirmation pop-up window if the data is consistent, and correct the current abnormal data and output a user confirmation pop-up window if the data is inconsistent.

[0048] The data request subunit is configured to receive the missing data output by the data anomaly screening subunit, output a data acquisition request through the interaction subunit, and monitor the acquisition of the medical record data of the user feedback input.

[0049] The data storage subunit is configured to store the acquired various types of data.

[0050] Through the above technical solution, the data acquisition unit can not only retrieve various examination data such as arterial ultrasound data from the related examination equipment, but also actively review the related medical record data according to the output result of the data anomaly screening unit, which helps to improve the accuracy and integrity of the data.

[0051] Optionally, the data acquisition unit further comprises:

[0052] The anomaly degree determination subunit is configured to compare the difference between the three types of abnormal data and the first type of abnormal data and the second type of abnormal data, and if the difference range exceeds the set value, output a data supplementary sampling request to the interaction subunit.

[0053] The supplementary sampling data includes intravascular ultrasound data.

[0054] When the difference between the abnormal data determined based on the first personalized relationship model and the abnormal data determined based on the existing relationship model is too large, it can be understood that an important data included in the current patient medical record data is weakened in the first or second relationship model, resulting in the omission of data anomalies during data correlation determination. Through the above technical solution, the accuracy and integrity of the data can be further improved through the supplement of the data category.

[0055] Optionally, the reference treatment scheme includes a treatment scheme category and a scheme detail parameter.

[0056] The treatment scheme category includes mechanical thrombolysis, thrombus aspiration, balloon angioplasty, stent implantation, combined thrombectomy, and combined surgery.

[0057] The scheme detail parameter includes drug variety and dosage, stent length and expansion method, stent placement position, instrument and operation parameter, physiological index monitoring item and monitoring scheme, and treatment risk point.

[0058] Optionally, the reference scheme generation unit comprises:

[0059] The second personalized relationship model generation module is configured to analyze and generate a second personalized relationship model reflecting the correlation between the reference treatment scheme and the arterial ultrasound data and / or the arterial angiography data according to the cases and their medical record data included in the reference case database.

[0060] The first reference scheme generation subunit is configured to receive the medical record data of the current patient and the third relationship model, and confirm the output of the first reference scheme.

[0061] The second reference scheme generation subunit is configured to receive the medical record data of the current patient and the second personalized relationship model, and confirm the output of the second reference scheme.

[0062] The reference scheme confirmation subunit is configured to receive and compare the first reference scheme and the second reference scheme.

[0063] If the treatment scheme categories contained in the two are the same and the scheme detail parameter deviation is within the set range, the second reference scheme is selected as the reference treatment scheme.

[0064] If the treatment scheme categories contained in the two are different or the scheme detail parameter deviation exceeds the set range, the first reference scheme, the second reference scheme, and the prompt information are output to the interaction unit.

[0065] Through the above technical solutions, different reference treatment schemes can be generated according to the general relationship model and the personalized relationship model. When the generated schemes differ too much, the interaction unit outputs prompt information to remind the user to make a judgment and choice, ensuring the accuracy of the final scheme.

[0066] Optionally, the data model storage unit also stores a fourth relationship model reflecting the corresponding relationship between each treatment scheme and its corresponding risk point.

[0067] The arterial lesion treatment scheme auxiliary generation system further includes:

[0068] The risk locking unit is configured to be in data connection with the reference scheme generation unit, obtain the reference treatment scheme, and generate a risk point corresponding to the current reference treatment scheme based on the fourth relationship model and output to the interaction unit.

[0069] Through the above technical solutions, when the system generates a reference treatment scheme, the corresponding treatment risk point can be output at the same time, which facilitates the user to comprehensively evaluate the reference treatment scheme and improves the treatment effect.

[0070] Optionally, based on the arterial lesion treatment scheme auxiliary generation system, the step of generating a reference treatment scheme includes:

[0071] Obtaining the medical record data of the current patient;

[0072] According to the above medical record data, similar cases and their medical record data are screened from the case database and stored as a reference case database.

[0073] According to the analysis of the reference case database, a first personalized relationship model is generated to reflect the correlation between the arterial ultrasound data and the arterial angiography data, and to reflect the correlation between the arterial ultrasound data and the arterial angiography data and the influencing factors of the diseases;

[0074] Abnormal data screening is performed on the medical record data of the current patient:

[0075] It is determined whether the correlation between the arterial ultrasound data and / or the arterial angiography data conforms to the first relationship model, and if not, the first abnormal data is output;

[0076] It is determined whether the correlation between the arterial ultrasound data and / or the arterial angiography data and the influencing factors of the diseases conforms to the second relationship model, and if not, the second abnormal data is output;

[0077] It is determined whether the medical record data of the current patient is missing compared to the medical record data of similar cases in the reference case database, and if so, the missing data is output;

[0078] It is determined whether the correlation between the arterial ultrasound data and / or the arterial angiography data and the correlation between the arterial ultrasound data and / or the arterial angiography data and the influencing factors of the diseases conforms to the first personalized relationship model, and if not, the third abnormal data is output;

[0079] According to the abnormal data, the original data is reviewed and / or new medical record data is obtained;

[0080] According to the reviewed and / or supplemented medical record data, it is determined whether the reference case database needs to be corrected and a new reference case database is generated;

[0081] If the reference case database needs to be corrected, the abnormal data in the medical record data of the current patient is screened again;

[0082] Based on the cases and their medical record data in the corrected reference case database, a second personalized relationship model is generated to reflect the correlation between the reference treatment scheme and the arterial ultrasound data and / or the arterial angiography data;

[0083] According to the screened medical record data, the first reference scheme and the second reference scheme are generated respectively based on the third relationship model and the second personalized relationship model;

[0084] The first reference scheme and the second reference scheme are compared, and a reference treatment scheme is generated or a prompt information is output to the interaction unit for the user to determine the final treatment scheme according to the difference degree.

[0085] The application scheme at least includes one of the following beneficial effects:

[0086] (1) After the patient's medical record data is input into the system, the system can search and match similar cases and their medical record data from the current patient in the large medical record database to form a reference case database, thereby providing reliable basis for subsequent data accuracy and screening and scheme customization;

[0087] (2) Multiple screening of medical record data to improve the accuracy of the generated reference treatment scheme: by auditing the ultrasound data / angiography data itself and the correlation between the two, the accuracy of the ultrasound and angiography data is initially determined, and then combined with each disease influencing factor in the current patient's medical record data and according to the correlation between the ultrasound / angiography data and each disease influencing factor, the accuracy of the data is determined again, and finally the personalized relationship model generated by the reference case database is used for the final data accuracy screening (including missing data screening), thereby ensuring the accuracy, completeness and reliability of the relevant medical record data, providing data guarantee for the generation of the reference treatment scheme;

[0088] (3) Through three types of data anomaly marking and different ways of display, the user can be effectively reminded to pay attention to abnormal data, and the user can focus more attention on the preparation of the treatment scheme rather than data checking and other work, combined with the provided reference treatment scheme, it is convenient for the user to customize a more accurate treatment scheme subsequently. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 is the overall schematic diagram of the system of the present application;

[0090] Figure 2 is the structural schematic diagram of the function module of the system of the present application.

[0091] Reference signs: 100, data model storage unit; 200, data acquisition unit; 210, data call subunit; 220, data review subunit; 230, data request subunit; 240, abnormality degree determination subunit; 250, data storage subunit; 300, reference case database generation unit; 310, medical record data analysis subunit; 320, similarity determination algorithm subunit; 3201, weight configuration relationship table; 3202, similarity score relationship table; 3203, similarity determination module; 330, first matching subunit; 340, second matching subunit; 350, database fusion subunit; 360, database correction subunit; 400, data anomaly screening unit; 410, first screening subunit; 420, second screening subunit; 430, third screening subunit; 4301, data missing check module; 4302, first personalized relationship model generation module; 4303, data anomaly output module; 440, abnormality prompt subunit; 500, reference scheme generation unit; 510, second personalized relationship model generation module; 520, first reference scheme generation subunit; 530, second reference scheme generation subunit; 540, reference scheme confirmation subunit; 600, interaction unit; 700, risk locking unit. DETAILED DESCRIPTION

[0092] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0093] In the description of the present specification, the description of the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the described embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily mean the same embodiment or example. Also, the described specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0094] An arterial lesion treatment scheme assistance generation system based on ultrasound and contrast data, as shown in Figure 1 includes a data model storage unit 100, a data acquisition unit 200, a reference case database generation unit 300, a data anomaly screening unit 400, a reference scheme generation unit 500, and an interaction unit 600.

[0095] The data model storage unit 100 includes a local storage and a cloud storage. The local storage includes a disk or a solid state disk of a computer or a personal computer. The cloud storage is configured as a database connected to a cloud server. The data model storage unit 100 is configured to store a first relationship model reflecting a correlation between various data in the arterial ultrasound data and the arterial angiography data, a second relationship model reflecting a correlation between the various arterial ultrasound data and the arterial angiography data and various disease influencing factors, and a third relationship model reflecting a correlation between a reference treatment scheme and the arterial ultrasound data and / or the arterial angiography data.

[0096] The first relationship model includes a correlation between various data in the arterial ultrasound data and a correlation between the arterial ultrasound data and the arterial angiography data. For example, a correlation between a blood flow velocity and a degree of stenosis of a blood vessel at a position distal to a vascular lesion and a blood flow velocity and a degree of stenosis at the position of the vascular lesion. Since the blood flow is constant, the product of the blood flow velocity and the degree of stenosis at the two positions should be a fixed value. Similarly, there is a clear correspondence between the angiography data of the position of the vascular lesion and the ultrasound data. Through the first relationship model, abnormal data can be screened out by using the correlation between various data in the arterial ultrasound data or the angiography data. Thus, it can be clearly known that the data abnormality caused in the data acquisition process, for example, the ultrasound image is blurred due to the fact that the ultrasound probe is not in close contact with the skin surface during the vascular ultrasound examination, and thus the degree of stenosis is determined to be deviated.

[0097] The disease influencing factors in the second relationship model include, but are not limited to, hyperlipidemia, hypertension, diabetes, smoking, obesity, lack of exercise, genetic factors, age, and chronic inflammatory response. Generally, the disease influencing factors determine the position and degree of atherosclerosis to a great extent. When the disease risk is warned, the second relationship model can be used to estimate the risk of lesions in parts that are not easy to examine.

[0098] In the third relationship model, the reference treatment scheme includes a scheme classification and a scheme detail parameter. The scheme classification includes, but is not limited to, mechanical thrombolysis, thrombus aspiration, balloon angioplasty, stent implantation, combined thrombectomy (a compressed mesh stent is sent to the distal end of a blood clot, and then released to embed the blood clot, and after a while, the stent and the wrapped blood clot are pulled out of the body), and complex surgery (mainly for chronic lower limb ischemia with multi-segment and diffuse complex lesions). The scheme detail parameter includes not only the type and dosage of the drug, the length and expansion method of the stent, the placement position of the stent, the instrument and operation parameters, the suction negative pressure, the balloon expansion size, and the treatment risk points, but also the physiological index monitoring items and monitoring schemes during the surgery, such as monitoring the blood pressure of a specific part of the body.

[0099] The data acquisition unit 200 is configured to acquire the medical record data of the current patient, which includes outpatient medical records, test sheets, medical imaging examination data, pathological data, and nursing records, etc. According to the classification of data, the medical record data includes one or more combinations of various disease influencing factors, arterial ultrasound data, and arteriography data.

[0100] In detail, in the embodiment of the present application, the data acquisition unit 200 includes a data retrieval subunit 210, a data review subunit 220, a data request subunit 230, and a data storage subunit 250.

[0101] The data storage subunit 250 is configured to store various types of acquired data.

[0102] The data retrieval subunit 210 is configured to retrieve and store the medical record data of the current patient in a set format from the data storage area where the medical record data of the current patient is stored, such as retrieving various medical record information entered by doctors from a structured data entry module. The structured data entry refers to a standardized input process of data according to a pre-defined standardized format, model or terminology to ensure data consistency, analyzability and efficient retrieval. The data storage area also includes various types of examination equipment, such as data storage of high-precision ultrasound examination equipment, angiography imaging devices, and patient personal medical record data stored in a cloud server. After the data retrieval subunit 210 retrieves the data information, it performs preliminary format screening and classification, and stores it according to the set format.

[0103] The data review subunit 220 is configured to receive and respond to abnormal data output by the data anomaly screening unit 400, retrieve the corresponding original data from the medical record data of the current patient and verify it. If the data is consistent, a user confirmation pop-up window is output for confirmation by the user (usually a doctor). If the data is inconsistent, the current abnormal data is corrected and a user confirmation pop-up window is output. The purpose of the data review subunit 220 is to reduce data deviation during data extraction, ensure data consistency and accuracy, such as errors in data update of equipment data storage, resulting in incorrect data obtained by the data retrieval subunit 210. The data review subunit 220 can troubleshoot the above problems.

[0104] The data request subunit 230 is configured to receive the missing data output by the data anomaly screening unit 400, output a data acquisition request through the interaction unit 600, and monitor the acquisition of user feedback input medical record data. In the embodiments of the present application, the interaction unit 600 includes data information input and output components, including but not limited to a mouse, a keyboard, a display, a joystick, a touch panel, a voice player, a microphone, and is configured to realize data and information interaction between the system and the user. When detecting data anomalies, the data request subunit 230 prompts the user about the related data anomalies through the display or the voice player, outputs a dialogue pop-up window to request the user to confirm the abnormal data, and if the data needs to be revised, outputs a corresponding pop-up window and monitors the acquisition of new medical record data input by the user to replace the original data.

[0105] As can be known from the above disclosed embodiments, the data acquisition unit 200 in the scheme of the present application can not only retrieve various examination data such as arterial ultrasound data, blood pressure detection data, and blood glucose detection data from related examination equipment, but also actively review related medical record data according to the output results of the data anomaly screening unit 400, realize preliminary screening of data, and help improve the accuracy and integrity of data.

[0106] The reference case database generation unit 300 is configured to be connected with the case database, acquire and screen similar cases and their medical record data from the case database according to the medical record data of the current patient, and store them as a reference case database.

[0107] In detail, in combination with Figure 2 As shown in the reference case database generation unit 300 in the embodiments of the present application, the reference case database generation unit 300 includes a medical record data analysis subunit 310, a similarity determination algorithm subunit 320, a first matching subunit 330, a second matching subunit 340, and a database fusion subunit 350.

[0108] The medical record data analysis subunit 310 is configured to analyze one or more of various disease influencing factors, arterial ultrasound data, and arterial angiography data from the medical record data of the current patient. In actual applications, the data has been stored in a set manner after being acquired by the data acquisition unit 200, which facilitates data analysis by the case data analysis subunit, such as determining whether a certain case data belongs to a disease influencing factor and making more specific classification.

[0109] Different from the prior art which directly takes one or more medical record data as a reference for similarity determination, in the embodiment of the present application, the similarity determination algorithm subunit 320 is configured to match or generate a similarity determination algorithm for determining the similarity between the current patient's arterial ultrasound data and / or arteriography data and the relevant medical record data in the case database according to at least one specific disease influencing factor in the current patient's medical record data, that is, different patients correspond to different similarity determination rules in order to find more suitable cases and their medical record data for reference. In detail, in the embodiment of the present application, the similarity determination algorithm subunit 320 specifically includes: a weight configuration relationship table 3201, a similarity score relationship table 3202, and a similarity determination module 3203.

[0110] The weight configuration relationship table 3201 is used to store the corresponding relationship between one or more specific disease influencing factors and the similarity determination weight coefficients of each item of data in the arterial ultrasound data and the arteriography data. In specific practice, the accuracy of the arterial ultrasound data is often disturbed by relevant disease influencing factors, for example, the obesity status of the patient can affect the blood vessel wall thickness data in the arterial ultrasound data, and the direct result is that the reliability of the above data is affected. If the original data is still used for comparison and the above influencing factors are ignored during the data similarity comparison process, it is obvious that the similarity determination will not be accurate. The effect ultimately achieved by configuring the similarity determination weight coefficients is to provide correction for the similarity determination result based on the reliability of the data, so that the reference case and its medical record data obtained later are more referable.

[0111] The similarity score relationship table 3202 is used to store the similarity scores corresponding to the difference value ranges of each item of data in the arterial ultrasound data and the arteriography data. For example, for the same moderate stenosis of the blood vessel (the image examination shows that the blood vessel diameter reduction ratio is 50%-70%), the similarity score can be further assigned according to the specific difference degree, such as 10 points for a difference within 5%, 6 points for a difference of 5%-10%, etc.

[0112] The similarity determination module 3203 is configured to obtain the arterial ultrasound data and / or arteriography data and the data values contained therein in the current patient's medical record data, compare them with the relevant data contained in each case in the case database, obtain the similarity score values of each item of data, and then calculate the similarity by weighted summation according to the similarity determination weight coefficients corresponding to each item of data. The above similarity is actually a specific numerical value.

[0113] Based on the aforementioned similarity determination algorithm, the first matching subunit 330 is configured to match cases and their medical records that are identical or similar to the data obtained from the parsed arterial ultrasound data and / or arterial angiography data from the large case database, forming a first intermediate database. The second matching subunit 340 is configured to match cases and their medical records that are identical or similar to the data obtained from the parsed disease influencing factors from the large case database, forming a second intermediate database.

[0114] The cases in the first and second intermediate databases are mostly the same. The database fusion subunit 350 is configured to acquire data from the first and second intermediate databases, and take the intersection of the cases and their medical records to form the reference case database. In practice, the cases and their medical records in the first, second, and reference case databases are sorted and stored according to their similarity to facilitate data querying and retrieval. In practical applications, the case information numbers can be used for sorting.

[0115] If the number of cases in the reference case database is lower than the set value, then the same number or a set proportion of cases and their medical records are extracted from the first intermediate database and the second intermediate database respectively to expand the data until the number of cases in the reference case database reaches the set value, which facilitates the generation of personalized relationship models later.

[0116] The data anomaly screening unit 400 is configured to receive the current patient's medical record data and, based on the first relational model, the second relational model, and the reference case database, screen and output abnormal and missing data.

[0117] Detailed, such as Figure 2 As shown, the data anomaly screening unit 400 includes: a first screening subunit 410, a second screening subunit 420, a third screening subunit 430, and an anomaly alert subunit 440.

[0118] The first screening subunit 410 is configured to screen out first abnormal data according to the arterial ultrasound data and / or the arterial angiography data in combination with the first relationship model, and mark the first abnormal data as a first type of abnormality. The first type of abnormality reflects an abnormality contained in the data itself, for example, an incorrect correlation between the stenosis degree and the blood flow velocity at different positions of the blood vessel in the arterial ultrasound data. The second screening subunit 420 is configured to screen out second abnormal data according to the disease influencing factors in combination with the second relationship model, and mark the second abnormal data as a second type of abnormality, for example, under the premise that multiple disease influencing factors are located in a certain interval in the second relationship model, a certain data in the arterial ultrasound data should be in a certain set interval but appears to be abnormal. The third screening subunit 430 is configured to compare and determine third abnormal data and missing data in the medical record data of the current patient in combination with the reference case database, and mark the third abnormal data and the missing data as a third type of abnormality, for example, the medical record data of similar cases in the reference case database all contain arterial angiography data, but the medical record data of the current patient does not contain the data, which is marked as data missing, and also includes missing of examination data such as blood glucose and blood lipid. The abnormality prompting subunit 440 is configured to prompt the user about the data abnormality in a set display manner through the interaction unit 600 according to the type of the data abnormality, for example, by changing the color of the displayed data item to distinguish the types of various abnormality, so as to facilitate the user to distinguish.

[0119] It should be noted that the first relationship model, the second relationship model and the like stored in the foregoing data model storage unit 100 are general relationship models generated by training the data in the case database, and the data verification through the general relationship models is only to exclude the data with obvious errors in the medical record data of the current patient, and is not to correct the data according to the correlation of the general relationship models. However, different patients have specificity in specific practice, and obviously the foregoing general relationship models cannot accurately screen out the related data abnormality, for example, for multiple different types of hardening lesions in multiple segments of the blood vessel, the corresponding relationship in the general relationship model will become unstable.

[0120] Therefore, in the embodiments of the present application, the third screening subunit 430 specifically includes a data missing checking module 4301, a first personalized relationship model generating module 4302 and a data abnormality output module 4303.

[0121] The data missing data detection module 4301 is configured to compare and search for missing medical record data in the current patient's medical record data based on the cases and their medical record data contained in the reference case database, and output the missing data. The first personalized relationship model generation module 4302 is configured to generate, based on the cases and their medical record data contained in the reference case database, a first personalized relationship model reflecting the correlation between various data in arterial ultrasound data and arterial angiography data, and reflecting the correlation between various arterial ultrasound data and arterial angiography data and the influencing factors of various diseases, using multifactor analysis or neural network training. In practice, multifactor analysis is usually used to generate the above-mentioned first personalized relationship model. The data anomaly output module 4303 is configured to match and output third abnormal data based on the current patient's medical record data and the first personalized relationship model. Since the source data for the training analysis of the first personalized relationship model comes from the reference case database, it is more effective in discovering anomalies in the current patient's medical record data.

[0122] Based on the above scheme, the data acquisition unit 200 also includes an anomaly determination subunit 240, configured to compare the differences between the three types of abnormal data and the first and second types of abnormal data. If the difference exceeds a set value, a data supplementation sampling request is output to the interaction unit 600. The supplemented sampling data includes, but is not limited to, intravascular ultrasound data. The significance of the above scheme is that when the difference between the abnormal data determined based on the first personalized relationship model and the abnormal data determined by the existing relationship model is too large, it can be understood that an important piece of data contained in the current patient's medical record data is weakened in the first or second relationship model, resulting in the omission of data anomalies when performing data correlation determination. Based on the above scheme, by supplementing the data categories, the accuracy and completeness of the data can be further improved.

[0123] The data acquisition unit 200 is connected to the data anomaly screening unit 400. In response to abnormal or missing data, it automatically acquires or reviews relevant medical record data, or outputs a data acquisition request through the interaction unit 600 and monitors the medical record data input by the user feedback.

[0124] In the embodiments of the present application, the reference case database generation unit 300 further comprises a database correction subunit 360. The database correction subunit 360 is configured to obtain the relevant case data after review or the case data input by the user and temporarily store the case data as correction data, search for the similarity determination weight coefficient corresponding to the correction data from the weight configuration relationship table 3201, calculate the difference between the two similarity scores before and after the correction data and the corresponding original data in combination with the similarity determination weight coefficient, and if the difference between the two similarity scores is lower than a set value, the reference case database is not updated, otherwise, the similar cases and their case data are reselected from the case database according to the correction data to form a new reference case database, so that the cases for reference in the reference case database have reference value and the accuracy of the later reference treatment scheme is improved.

[0125] The reference scheme generation unit 500 is configured to determine the output reference treatment scheme according to the case data of the current patient and the third relationship model. In detail, the reference scheme generation unit 500 comprises a first reference scheme generation subunit 520 configured to receive the case data of the current patient and the third relationship model and determine the output first reference scheme.

[0126] To further improve the accuracy of the reference treatment scheme, in the embodiments of the present application, the reference scheme generation unit 500 further comprises a second personalized relationship model generation module 510, a second reference scheme generation subunit 530, and a reference scheme determination subunit 540.

[0127] The second personalized relationship model generation module 510 is configured to generate a second personalized relationship model reflecting the correlation between the reference treatment scheme and the arterial ultrasound data and / or the arterial angiography data according to the cases and their case data in the reference case database through a multi-factor analysis method or neural network training analysis, i.e., the treatment scheme category and scheme detail parameters corresponding to various arterial ultrasound data and / or arterial angiography data. The second reference scheme generation subunit 530 is configured to receive the case data of the current patient and the second personalized relationship model and determine the output second reference scheme. The reference scheme determination subunit 540 is configured to receive and compare the first reference scheme and the second reference scheme: if the treatment scheme categories contained in the two schemes are the same and the scheme detail parameters deviate within a set range, the second reference scheme is selected as the reference treatment scheme. If the treatment scheme categories contained in the two schemes are different or the scheme detail parameters deviate beyond the set range, the first reference scheme, the second reference scheme, and a prompt information are output to the interaction unit 600, and the user determines which treatment scheme to adopt.

[0128] Further optimization, in the data model storage unit 100 also stores a fourth relationship model for reflecting the corresponding relationship between each treatment scheme and its corresponding risk point, for example, the blood vessels at a certain position are prone to cause damage to the blood vessels during stent surgery or are prone to cause acute thrombosis after stent placement. Correspondingly, the arterial lesion treatment scheme assisted generation system also includes a risk locking unit 700, which is configured to be in data connection with the reference scheme generation unit 500, obtains the reference treatment scheme and generates the risk point corresponding to the current reference treatment scheme based on the fourth relationship model and outputs to the interaction unit 600. When the system generates the reference treatment scheme, the corresponding treatment risk point can be output at the same time, so as to facilitate the user to comprehensively evaluate the reference treatment scheme and improve the treatment effect.

[0129] Based on the arterial lesion treatment scheme assisted generation system described above, the step of generating the reference treatment scheme comprises:

[0130] S1, obtaining the medical record data of the current patient;

[0131] S2, screening similar cases and their medical record data from the case database according to the above medical record data, and storing them as a reference case database;

[0132] S3, analyzing and generating a first personalized relationship model for reflecting the correlation between each item of arterial ultrasound data and arterial angiography data, and the correlation between each item of arterial ultrasound data and arterial angiography data and each disease influencing factor according to the above reference case database;

[0133] S4, performing abnormal data screening on the medical record data of the current patient:

[0134] S41, determining whether the correlation between each item of arterial ultrasound data and / or arterial angiography data conforms to the first relationship model, and if not, outputting the first abnormal data;

[0135] S42, determining whether the correlation between each item of arterial ultrasound data and / or arterial angiography data and each disease influencing factor conforms to the second relationship model, and if not, outputting the second abnormal data;

[0136] S43, determining whether the medical record data of the current patient has data missing compared with the medical record data contained in the similar cases in the reference case database, and if so, outputting the missing data;

[0137] S44, determining whether the correlation between each item of arterial ultrasound data and / or arterial angiography data, and the correlation between each item of arterial ultrasound data and / or arterial angiography data and each disease influencing factor conforms to the first personalized relationship model, and if not, outputting the third abnormal data;

[0138] S5, according to the abnormal data, review the original data and / or obtain new medical record data;

[0139] S6, according to the review and / or supplementary medical record data, determine whether the reference case database needs to be corrected and generate a new reference case database;

[0140] S7, if the reference case database needs to be corrected, repeat steps S4-S6 to re-screen the abnormal data in the current patient medical record data until the reference case database is determined;

[0141] S8, based on the corrected reference case database and the medical record data of each case, analyze and generate a second personalized relationship model reflecting the correlation between the reference treatment scheme and the arterial ultrasound data and / or the arterial angiography data;

[0142] S9, according to the screened medical record data, combine the third relationship model and the second personalized relationship model to generate a first reference scheme and a second reference scheme, respectively;

[0143] S10, compare the first reference scheme and the second reference scheme and determine the generation of the reference treatment scheme or output the prompt information to the interaction unit 600 according to the difference to determine the final treatment scheme by the user.

[0144] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A system for assisting in the generation of treatment plans for arterial lesions based on ultrasound and angiography data, characterized in that, include: The data model storage unit (100) is configured to store a first relational model reflecting the relationship between various data in arterial ultrasound data and arterial angiography data, a second relational model reflecting the relationship between various arterial ultrasound data and arterial angiography data and various disease influencing factors, and a third relational model reflecting the relationship between reference treatment plan and arterial ultrasound data and / or arterial angiography data. The data acquisition unit (200) is configured to acquire the medical record data of the current patient; The reference case database generation unit (300) is configured to connect to the case database, obtain and filter cases and their medical records that are similar to the current patient from the case database based on the current patient's medical record data, and store them as the reference case database; The data anomaly screening unit (400) is configured to receive the current patient's medical record data and, based on the first relational model, the second relational model, and the reference case database, screen and output abnormal and missing data. The reference treatment plan generation unit (500) is configured to confirm and output a reference treatment plan based on the current patient's medical record data and the third relationship model. The interaction unit (600) includes data information input and output components, configured to enable data and information interaction between the system and the user; The data acquisition unit (200) is connected to the data anomaly screening unit (400) and automatically acquires or reviews relevant medical record data in response to the abnormal or missing data, or outputs a data acquisition request through the interaction unit (600) and monitors and acquires the medical record data input by the user feedback. The medical record data includes one or more of the following: various influencing factors of the disease, arterial ultrasound data, and arterial angiography data. The data anomaly screening unit (400) includes: The first screening subunit (410) is configured to filter and output first abnormal data based on arterial ultrasound data and / or arterial angiography data in combination with the first relationship model, and mark it as a type of abnormality; The second screening subunit (420) is configured to screen and output second abnormal data based on the influencing factors of each disease and the second relationship model, and mark them as second-class abnormalities; The third screening subunit (430) is configured to compare and determine the third abnormal data and missing data in the current patient medical record data according to the reference case database, and mark them as three types of abnormalities; The exception notification subunit (440) is configured to notify the user of data exceptions through the interaction unit (600) in a set display manner according to the exception type of the data; The reference case database generation unit (300) includes: The medical record data parsing subunit (310) is configured to parse one or more of the following from the current patient's medical record data: various disease influencing factors, arterial ultrasound data, and arterial angiography data. The similarity determination algorithm subunit (320) is configured to match or generate a similarity determination algorithm based on at least one specific symptom influencing factor in the current patient's medical record data to determine the similarity between the current patient's arterial ultrasound data and / or arterial angiography data and related medical record data in the case database; The first matching subunit (330) is configured to match the same or similar cases and their medical records from the case database based on the arterial ultrasound data and / or arterial angiography data obtained from the analysis, and form a first intermediate database; The second matching subunit (340) is configured to match cases and their medical records that are the same as or similar to the above data from the medical record database based on the disease influencing factors obtained from the analysis, and form a second intermediate database. The database fusion subunit (350) is configured to acquire data from the first intermediate database and the second intermediate database, and to take the intersection of cases and their medical records to form the reference case database; The similarity determination algorithm subunit (320) includes: The weight configuration table (3201) is used to store the correspondence between one or more specific disease influencing factors and the weight coefficients for similarity judgment of various data in arterial ultrasound data and arterial angiography data; The similarity score relationship table (3202) is used to store the similarity scores corresponding to the range of differences between various data in arterial ultrasound data and arterial angiography data; The similarity determination module (3203) is configured to obtain arterial ultrasound data and / or arterial angiography data and the values ​​of various data contained in the current patient's medical record data, compare them with the relevant data contained in each case in the case database, obtain the similarity score of each data, and then calculate the similarity by weighting according to the similarity determination weight coefficient corresponding to each data. The cases and their medical records in the first intermediate database, the second intermediate database, and the reference case database are sorted and stored according to their similarity. If the number of cases in the reference case database is lower than the set value, then the same number or a set proportion of cases and their medical records are extracted from the first intermediate database and the second intermediate database respectively to expand the data until the number of cases in the reference case database reaches the set value.

2. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 1, characterized in that, The reference case database generation unit (300) further includes: The database correction subunit (360) is configured to obtain the relevant medical record data after review or the medical record data input by the user feedback and temporarily store it as correction data, and search for the similarity judgment weight coefficient corresponding to the correction data from the weight configuration relationship table (3201). The difference between the corrected data and its corresponding original data is calculated by combining the similarity judgment weight coefficient; If the difference in the similarity scores is lower than the set value, the reference case database will not be updated; otherwise, similar cases and their medical records will be searched again from the large case database based on the corrected data to form a new reference case database.

3. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 1, characterized in that, The third screening subunit (430) includes: The data missing check module (4301) is configured to compare and search for missing medical record data in the current patient's medical record data based on the cases and their medical record data contained in the reference case database, and output the missing data; The first personalized relation model generation module (4302) is configured to generate, based on multi-factor analysis or neural network training, a first personalized relation model that reflects the relationship between various data in arterial ultrasound data and arterial angiography data, and reflects the relationship between various arterial ultrasound data and arterial angiography data and various disease influencing factors, based on the cases and medical records contained in the reference case database. The data anomaly output module (4303) is configured to match and output the third abnormal data based on the current patient's medical record data and the first personalized relationship model.

4. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 1, characterized in that, The data acquisition unit (200) includes: The data retrieval subunit (210) is configured to retrieve and summarize the current patient's medical record data in a set format from the data storage area containing the current patient's various medical record data. The data verification subunit (220) is configured to receive and respond to the abnormal data output by the data anomaly screening unit (400), retrieve the corresponding original data from the current patient medical record data and verify it. If the data is consistent, a user confirmation pop-up is output. If the data is inconsistent, the current abnormal data is corrected and a user confirmation pop-up is output. The data request subunit (230) is configured to receive and respond to missing data output by the data anomaly screening unit (400), output data acquisition requests through the interaction unit (600), and monitor and acquire medical record data input by user feedback. The data storage subunit (250) is configured to store various types of acquired data.

5. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 4, characterized in that, The data acquisition unit (200) further includes: The anomaly determination subunit (240) is configured to compare the difference between the three types of abnormal data and the first and second types of abnormal data. If the difference exceeds the set value, a data supplementation sampling request is output to the interaction unit (600). The supplementary sampling data included intravascular ultrasound data.

6. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 1, characterized in that, The reference treatment plan includes the treatment plan category and detailed parameters of the plan; The treatment options include: mechanical thrombolysis, thrombectomy, balloon angioplasty, stent implantation, combined thrombectomy, and hybrid surgery. The detailed parameters of the protocol include: drug type and dosage, stent length and deployment method, stent placement location, instrument and operation parameters, physiological indicator monitoring items and monitoring protocol, and treatment risk points.

7. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 6, characterized in that, The reference scheme generation unit (500) includes: The second personalized relationship model generation module (510) is configured to analyze and generate a second personalized relationship model based on the cases and medical records contained in the reference case database to reflect the relationship between the reference treatment plan and arterial ultrasound data and / or arterial angiography data. The first reference scheme generation subunit (520) is configured to receive the current patient's medical record data and the third relation model, and confirm the output of the first reference scheme; The second reference scheme generation subunit (530) is configured to receive the current patient's medical record data and the second personalized relationship model, and confirm the output of the second reference scheme; Reference scheme confirmation subunit (540) is configured to receive and compare the first reference scheme with the second reference scheme: If the two reference treatment plans are of the same category and the deviation of the detailed parameters of the plans is within the set range, then the second reference plan shall be selected as the reference treatment plan. If the two treatment options are of different categories or the deviation of the detailed parameters of the options exceeds the set range, the first reference option, the second reference option, and the prompt information are output to the interaction unit (600).

8. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 7, characterized in that, The data model storage unit (100) also stores a fourth relationship model that reflects the correspondence between each treatment plan and its corresponding risk point; The arterial lesion treatment plan generation system also includes: The risk locking unit (700) is configured to be data connected to the reference treatment plan generation unit (500), obtain the reference treatment plan and generate risk points corresponding to the current reference treatment plan based on the fourth relationship model, and output them to the interaction unit (600).

9. The arterial lesion treatment plan generation system based on ultrasound and angiography data according to claim 7, characterized in that, The steps for generating a reference treatment plan based on the arterial lesion treatment plan generation system include: Retrieve the current patient's medical record data; Based on the above medical record data, cases and their medical records similar to the current patient are selected from the case database and stored as a reference case database; Based on the analysis of the aforementioned reference case database, a first personalized relationship model was generated to reflect the correlation between various data in arterial ultrasound data and arterial angiography data, as well as the correlation between various arterial ultrasound data and arterial angiography data and the influencing factors of various diseases. Perform anomaly screening on current patient medical record data: Determine whether the correlation between various data in arterial ultrasound data and / or arterial angiography data conforms to the first relationship model; if not, output the first abnormal data. Determine whether the correlation between various arterial ultrasound data and / or arterial angiography data and the influencing factors of each disease conforms to the second relationship model. If it does not conform, output the second abnormal data. Determine whether the current patient's medical record data is missing compared to the medical record data of similar cases in the reference case database; if so, output the missing data. Determine whether the correlation between various data in arterial ultrasound data and / or arterial angiography data, and the correlation between various arterial ultrasound data and / or arterial angiography data and various disease influencing factors, are consistent with the first personalized relationship model. If they are not consistent, output the third abnormal data. Based on the above abnormal data, review the original data and / or obtain new medical record data; Determine whether the above reference case database needs to be revised and a new reference case database needs to be generated based on the reviewed and / or supplemented medical record data; If the reference case database needs to be corrected, then the abnormal data in the current patient's medical record data will be screened again; Based on the case and medical record data of each case in the revised reference case database, a second personalized relationship model is generated to reflect the correlation between the reference treatment plan and arterial ultrasound data and / or arterial angiography data. Based on the screened medical record data, the first reference plan and the second reference plan are generated by combining the third relationship model and the second personalized relationship model, respectively. The system compares the first reference plan with the second reference plan and determines a reference treatment plan based on the degree of difference, or outputs a prompt message to the interaction unit (600) for the user to determine the final treatment plan.

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