Radiofrequency ablation data processing method, device, server, and computer-readable storage medium

Abstract

A method, an apparatus, a server, and a computer-readable storage medium for processing radiofrequency ablation data are provided, the method including: acquiring radiofrequency ablation data including parameter data for each ablation task performed within a preset duration, the parameter data being set when each device in a radiofrequency ablation system performed each ablation task, and characteristic data of an ablation subject associated with each ablation task; analyzing the radiofrequency ablation data to acquire and output an ablation parameter setting scheme that is respectively matched to different types of ablation subjects. The above-mentioned method, apparatus, server, and non-transitory computer-readable storage medium for processing radiofrequency ablation data can realize radiofrequency ablation data analysis based on big data, and provide a reference for setting ablation parameters for different types of ablation subjects.

Description

[Technical field]

[0001] TECHNICAL FIELD Embodiments of the present disclosure relate to the field of data processing technology, and in particular to a method, apparatus, server and computer-readable storage medium for radiofrequency ablation data processing. [Background technology]

[0002] Radiofrequency ablation (RFA) is a more common minimally invasive tumor ablation technique. The principle of radiofrequency ablation is to use an alternating high-frequency current with a frequency of less than 30 MHz (megahertz) to induce high-speed vibration of ions in tumor tissue, causing the ions to rub against each other, converting the radiofrequency energy into thermal energy and inducing coagulation necrosis of tumor cells.

[0003] Each device in the radiofrequency ablation system generates a large amount of radiofrequency ablation data during each ablation task. However, in the conventional technology, these radiofrequency ablation data are only logged. How to use these radiofrequency ablation data to provide data reference and technical support for future ablation tasks is a major problem that needs to be urgently solved in the industry. Summary of the Invention

[0004] The embodiments of the present disclosure provide a method, apparatus, server, and non-transitory computer-readable storage medium for radiofrequency ablation data processing, which can realize radiofrequency ablation data analysis based on big data and provide a reference for setting ablation parameters for different types of ablation subjects, thereby improving the utilization rate of radiofrequency ablation data.

[0005] One aspect of an embodiment of the present disclosure provides a method of radiofrequency ablation data processing applied to a computer device, the method comprising: Acquiring radiofrequency ablation data, the radiofrequency ablation data including, for each ablation task performed within a preset duration, parameter data set when each device in the radiofrequency ablation system performed each ablation task, and characteristic data of an ablation target associated with each ablation task; The radiofrequency ablation data is analyzed to obtain and output ablation parameter setting schemes respectively matching different types of ablation subjects.

[0006] An aspect of an embodiment of the present disclosure further provides an apparatus for radiofrequency ablation data processing, the apparatus comprising: an acquisition module for acquiring radiofrequency ablation data including parameter data set when each device in the radiofrequency ablation system executed each ablation task for each ablation task executed within a preset duration, and characteristic data of an ablation target associated with each ablation task; an analysis module for analyzing the radiofrequency ablation data and obtaining ablation parameter setting schemes respectively matching different types of ablation subjects; and an output module for outputting the ablation parameter setting scheme.

[0007] In one aspect of the disclosed embodiment, a server is provided, further comprising a memory and a processor, The memory stores executable program code; The processor coupled to the memory invokes the executable program code stored in the memory to perform the method of radiofrequency ablation data processing provided by the above embodiments.

[0008] An aspect of the embodiments of the present disclosure further provides a computer readable storage medium having stored thereon a non-transitory computer program, the computer program, when executed by a processor, implementing the method of radiofrequency ablation data processing provided by the above embodiments.

[0009] Each embodiment provided by the present disclosure obtains radiofrequency ablation data of each ablation task performed within a preset duration, and obtains ablation parameter setting schemes that respectively match different types of ablation targets by analyzing the obtained radiofrequency ablation data. By realizing automatic analysis of radiofrequency ablation data based on big data and recommending ablation parameter setting schemes based on the automatic analysis, the utilization rate of radiofrequency ablation data is improved, and since the analysis results are based on a large amount of data, they have high referability. [Brief description of the drawings]

[0010] In order to more clearly describe the embodiments of the present disclosure or the conventional technical solutions, the following briefly introduces the accompanying drawings used in the embodiments or conventional descriptions. Obviously, the drawings described below are only some embodiments of the present disclosure. Those skilled in the art can obtain other drawings based on these drawings without creative labor. [Figure 1] FIG. 1 is an application environment diagram of the method of radiofrequency ablation data processing provided by an embodiment of the present disclosure. [Diagram 2] FIG. 2 is an implementation flowchart of a method for radiofrequency ablation data processing provided by one embodiment of the present disclosure. [Diagram 3] FIG. 3 is an implementation flowchart of a method for radiofrequency ablation data processing provided by another embodiment of the present disclosure. [Figure 4] FIG. 4 is a schematic diagram of the structure of a radiofrequency ablation data processing device provided by one embodiment of the present disclosure. [Diagram 5] FIG. 5 is a schematic diagram of the structure of a radiofrequency ablation data processing device provided according to another embodiment of the present disclosure. [Figure 6] FIG. 6 is a schematic diagram of a hardware structure of data provided by one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] In order to make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure are described below clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, and are not all the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are included in the protection scope of the present disclosure.

[0012] Referring to FIG. 1, an application scenario diagram of the radiofrequency ablation data processing method provided by the embodiment of the present disclosure is shown. The radiofrequency ablation data processing method may be realized by the server 20 of FIG. 1. The server 20 may be a single cloud server or a distributed server cluster composed of multiple servers on the cloud. The distributed server cluster includes at least one access server group for data access, at least one distribution server group for data distribution, at least one cloud processing server group for data processing, and at least one database server group for data storage (such as the database in various embodiments of the present disclosure). Each of the above server groups may be located in one place or may be distributed in different places.

[0013] The access server group, as an external interface of the distributed server cluster, establishes a data connection with the distribution server group via a first gateway to realize data exchange, thereby realizing data collection and external transmission of data in the radiofrequency ablation data processing method provided by each embodiment of the present disclosure.

[0014] The distribution server group establishes a data connection with the cloud processing server group via a second gateway to exchange data, thereby realizing the distribution of radiofrequency ablation data and the control of data processing in the radiofrequency ablation data processing method provided by each embodiment of the present disclosure. The distribution server group determines a target server to process the data sent from the access server group based on the real-time processing amount of each cloud processing server.

[0015] The cloud processing server group establishes a data connection with the database server group via a third gateway to realize the analysis and processing of radiofrequency ablation data in the radiofrequency ablation data processing method provided by each embodiment of the present disclosure, as well as to realize the storage, query, and modification of database-based data in each embodiment of the present disclosure.

[0016] The server 20 establishes a data connection with at least one device in one or more radiofrequency ablation systems 10 and other related devices 30 via a wireless network or a wired connection, obtains radiofrequency ablation data of the ablation tasks performed by each radiofrequency ablation system 10 via the data connection, and processes the radiofrequency ablation data by the radiofrequency ablation data processing method in the following embodiment. For ease of understanding, only three radiofrequency ablation systems 10 are shown in Fig. 1, but the actual application is not limited thereto. Multiple radiofrequency ablation systems 10 can be located in different locations, such as different departments of multiple units in different regions.

[0017] It will be understood that if the server 20 establishes a data connection with only some of the devices in the radiofrequency ablation system 10, those some of the devices may function as an interface for transferring radiofrequency ablation data of other devices in the radiofrequency ablation system 10 to the server 20.

[0018] The other related devices 30 may be other radiofrequency ablation data collection and storage devices, such as medical imaging equipment, intelligent physical examination equipment, a personal computer terminal of a doctor user, a server of a medical data publishing platform, and so on.

[0019] As shown in FIG. 1, the radiofrequency ablation system includes a radiofrequency ablation control device 11, an infusion pump 12, an indifferent electrode 13, and a radiofrequency ablation catheter 14.

[0020] Before performing an ablation task, the radiofrequency ablation catheter 14, which generates and outputs radiofrequency energy, and the extension tube 121 of the injection pump 12 are first inserted into the body of a subject to be ablated (e.g., a patient with emphysema) and reach the ablation site. Then, the neutral electrode 13 is brought into contact with the skin surface of the subject to be ablated. Radiofrequency current flows through the radiofrequency ablation catheter 14, the tissue of the subject to be ablated, and the neutral electrode 13, forming a loop.

[0021] When the ablation task is triggered, the radiofrequency controller 11 controls the ablation catheter 14 to output radiofrequency energy to the ablation site in a unipolar discharge manner, and performs an ablation operation on the ablation site. At the same time, the infusion pump 12 performs a perfusion operation on the ablation target via the extension tube 121, and adjusts the impedance and temperature of the ablation site by injecting saline into the ablation site.

[0022] Referring to Fig. 2, there is shown an implementation flowchart of a method for processing radiofrequency ablation data provided by an embodiment of the present disclosure. The method may be implemented by a computer device such as the server 20 in Fig. 1. As shown in Fig. 2, the method specifically includes the following steps:

[0023] Step S201: Obtain radiofrequency ablation data of each ablation task performed within a preset duration.

[0024] Specifically, the server periodically acquires radiofrequency ablation data of each ablation task performed within a preset duration, the radiofrequency ablation data including parameter data set when each device in at least one radiofrequency ablation system executes each ablation task and characteristic data of the ablation target associated with each ablation task. The preset duration may be, for example, one month, half a year, one year, or a longer time, or may be a time interval from the start of operation of each radiofrequency ablation system to the acquisition of radiofrequency ablation data by the server, and may be specifically set according to a user's customized operation.

[0025] Here, the above parameter data may include, but are not limited to, for example, the radiofrequency power, ablation time, alarm value, fluid perfusion amount of the infusion pump, perfusion time, alarm value, etc., which are called when the radiofrequency ablation controller controls the radiofrequency ablation catheter to perform the ablation operation. The radiofrequency ablation controller and the infusion pump each perform the ablation task according to the corresponding parameter data.

[0026] The characteristic data of the ablation subject may include, for example, the name, age, sex, disease, etc. of the ablation subject, but is not limited to these.

[0027] The server can acquire this parameter data by periodically transmitting a data acquisition request to the device in the radiofrequency ablation system. Alternatively, the device in the radiofrequency ablation system can report the parameter data set each time an ablation task is performed to the server in real time or periodically. Alternatively, the device in the radiofrequency ablation system may report the parameter data set each time an ablation task is performed to a data collection server dedicated to collecting radiofrequency ablation data in real time or periodically, and the server periodically acquires the parameter data from the data collection server.

[0028] Of course, the characteristic data of the ablation target associated with each ablation task can be stored in the radiofrequency ablation system or the data collection server, which can then query the characteristic data of the ablation target associated with the ablation task from the data collection server according to the identification information of the ablation task.

[0029] The data collection server can acquire characteristic data of the ablation target associated with each ablation task in response to a user's input operation, and can also acquire a description file (e.g., an electronic prescription) of each ablation task from another terminal (such as a computer terminal in each medical room), and then extract the characteristic data of the ablation target associated with each ablation task from the description file.

[0030] Step S202: Analyze the radiofrequency ablation data, and obtain and output ablation parameter setting schemes respectively matching different types of ablation targets.

[0031] Specifically, the ablation subjects can be classified according to the feature data of each ablation subject, and then each radiofrequency ablation data is classified according to the type of ablation subject associated with the corresponding ablation task to obtain ablation parameter setting schemes matching different types of ablation subjects and outputs.

[0032] Optionally, the ablation parameter setting scheme may be all parameter data called by all devices in the radiofrequency ablation system to perform the same ablation task, or all parameter data called by one device in the radiofrequency ablation system to perform one ablation task.

[0033] Each embodiment of the present disclosure acquires radiofrequency ablation data of each ablation task performed within a preset duration, and analyzes the acquired radiofrequency ablation data to acquire ablation parameter setting schemes that respectively match different types of ablation subjects. By realizing automatic analysis of radiofrequency ablation data based on big data and recommendation of ablation parameter setting schemes based on the automatic analysis, the utilization rate of radiofrequency ablation data is improved, and since the analysis results are based on a large amount of data, they have high referability.

[0034] Referring to Figure 3, there is shown an implementation flowchart of a method for processing radiofrequency ablation data provided by an embodiment of the present disclosure. The method may be implemented by a computer device such as the server 20 in Figure 1. As shown in Figure 3, the method specifically includes the following steps:

[0035] Step S301: Obtain radiofrequency ablation data for each ablation task performed within a preset duration.

[0036] Specifically, the server periodically acquires radiofrequency ablation data of each ablation task performed within a preset duration. The radiofrequency ablation data includes parameter data set when each device in the radiofrequency ablation system executes each ablation task and characteristic data of the ablation target associated with each ablation task. The preset duration may be, for example, one month, half a year, one year, or a longer time, or may be a time interval from the start of operation of the radiofrequency ablation system to the acquisition of the radiofrequency ablation data by the server, and may be set according to a user's customized operation.

[0037] The radiofrequency ablation data includes parameter data set when each device in the radiofrequency ablation system performed each ablation task, characteristic data of the ablation target associated with each ablation task, explanatory data for each ablation task, characteristic data of the ablation site of each ablation target, and change data of each ablation site after execution of the associated ablation task.

[0038] Here, the above parameter data may include, for example, but not limited to, the radiofrequency power, ablation time, and alarm value of the radiofrequency ablation controller, and the fluid perfusion rate, perfusion time, flow rate, and alarm value of the infusion pump, etc. The radiofrequency ablation controller and the infusion pump each perform the ablation task according to the corresponding parameter data.

[0039] Optionally, the characteristic data of the ablation subject may include, but is not limited to, at least one of the gender, the disease before the ablation task is performed, and the survival time after the ablation task is performed, and the age of the ablation subject, where the disease is, for example, a disease other than the disease corresponding to the ablation task, such as hypertension, high fever, asthma, etc.

[0040] Optionally, the description data for the ablation task includes a duration of the ablation task and description data for an ablation stage corresponding to the ablation task, such as a first ablation task or an ablation stage.

[0041] Optionally, the ablation site characteristic change data includes change data in at least one of the position, size, shape, and area of ​​the ablation site before and after each associated ablation task is performed.

[0042] The server can acquire this parameter data by periodically transmitting a data acquisition request to the device in the radiofrequency ablation system. Alternatively, the device in the radiofrequency ablation system can report the parameter data set each time an ablation task is performed to the server in real time or periodically. Alternatively, the device in the radiofrequency ablation system may report the parameter data set each time an ablation task is performed to a data collection server dedicated to collecting radiofrequency ablation data in real time or periodically, and the server periodically acquires the parameter data from the data collection server.

[0043] Of course, among the above radiofrequency ablation data, other data except for the parameter data can also be stored in the radiofrequency ablation system or a dedicated data collection server. When stored in the data collection server, the server can obtain other radiofrequency ablation data associated with the ablation task by querying the data collection server according to the identification information of the ablation task or the identification information of the ablation target. Here, the identification information of the ablation task is for uniquely identifying the identity of the ablation task, such as a unique number of the ablation task in the entire network. The identification information of the ablation target is for uniquely identifying the identity of the ablation target, such as the name of the ablation target. These identification information may be assigned by the radiofrequency ablation system or other related devices when performing the corresponding task and reported to the server, or may be assigned by the server and transmitted to the radiofrequency ablation system or other related devices.

[0044] The data collection server may acquire radiofrequency ablation data by a user's input operation, or may acquire radiofrequency ablation data from another terminal (such as a computer terminal in each medical room). For example, the data collection server may acquire a description file (such as an electronic prescription) of each ablation task from the other terminal, and then extract feature data of the ablation target associated with each ablation task from the description file.

[0045] Optionally, in another embodiment of the present disclosure, the method further comprises: The method includes receiving identification information of the ablation target, the contrast time, and the contrast data of the ablation target sent by a medical imaging device, performing image recognition on the contrast data, generating feature data of the ablation site of the ablation target according to the recognition result, associating the identification information of the ablation target and the contrast time with the feature data of the ablation site, and storing the association in an ablation site information database.

[0046] Here, the medical imaging device may be, but is not limited to, an X-ray inspection machine or a CT (Computed Tomography) device. Specifically, the medical imaging device may report the imaging data obtained after each imaging task is performed or periodically during the imaging task to the server.

[0047] The characteristic data of the ablation site may include, but is not limited to, at least one of a position, a size, a shape, and an area of ​​the ablation site. Optionally, the ablation site information database may be located in a data collection server in the cloud.

[0048] The characteristic change data of the ablation site of each ablation target can also be obtained in the following manner: the ablation site information database is consulted to obtain the characteristic data of the ablation portion of each ablation target and the corresponding contrast time, and the characteristic change data of the ablation site of each ablation target is obtained according to the contrast time.

[0049] Specifically, the server may query the ablation site information database to obtain characteristic data of the ablation areas of all ablation targets and their contrast times at once, or may query characteristic data of one or more ablation targets and their contrast times according to the identification information of one or more ablation targets.

[0050] In this way, by including other related equipment such as medical imaging equipment in the automatic analysis network of radiofrequency ablation data, automatic collection of related data can be realized, labor costs can be saved, data collection time can be shortened, and the efficiency of data analysis can be further improved.

[0051] Step S302: Classify the ablation target according to the characteristic data of the ablation target in the radiofrequency ablation data, the description data of the ablation task, the characteristic change data of the ablation site, and preset classification conditions.

[0052] Specifically, the preset classification conditions are classification criteria. According to the specific content of the radio frequency operation data, the classification criteria may include, but are not limited to, for example, age group, sex, disease before performing the ablation task, ablation stage, characteristic range of the ablation site, survival time range, etc., as shown in Table 1 below. In practical application, one or more classification conditions may be preset according to the user's custom operation.

[0053] In this way, the ablation targets are classified according to the preset classification conditions to distinguish different types of ablation targets, and then the radiofrequency ablation data is analyzed according to the classification results, so that the ablation parameter setting scheme obtained by the analysis can be more target-oriented and referable. JPEG0007654798000001.jpg68170

[0054] As shown in Table 1 above, multiple types of ablation targets are preset, and each type (e.g., Type 1, Type 2, etc.) may correspond to one or more classification conditions (e.g., age group, gender, etc.). The characteristic data of the ablation target in the radiofrequency ablation data of one ablation task, the description data of the ablation task, and the characteristic change data of the ablation site are compared with the classification conditions corresponding to each preset type, respectively, to determine the classification condition of the preset type that is best satisfied by the ablation task, the preset type is determined as the type to which the ablation target belongs, and the parameter data set when the ablation task is performed is associated with the preset type.

[0055] In the above Table 1, (x, y, z) are the position coordinates of the ablation site. Optionally, a two-dimensional or three-dimensional coordinate system is established with a specific vertex of the entire tissue or organ where the ablation site is located as the origin, and the coordinates of the center of gravity of the ablation site in the two-dimensional or three-dimensional coordinate system are taken as the position of the ablation site. By using the coordinates, the positioning of the ablation site can be made more accurate, and the accuracy of the analysis results can be further improved.

[0056] Of course, the above Table 1 is just an example, and in actual applications, the associated data table may have more or less content and may be represented in other formats.

[0057] Step S303: According to the classification result and the feature change data of the ablation site, the parameter data in the radiofrequency ablation data is analyzed to obtain at least one set of target parameter data respectively matching different types of ablation targets as an ablation parameter setting scheme.

[0058] Specifically, first, the parameter data is classified according to the classification result of the ablation target to obtain at least one set of candidate parameter data corresponding to different types of ablation target, and then, among the candidate parameter data, parameter data whose characteristic change data of the ablation site and / or survival time do not satisfy the preset change criterion is eliminated to obtain at least one set of target parameter data as the ablation parameter setting scheme.

[0059] Here, the change criterion is used to evaluate the ablation effect and may be an absolute or relative value. The change criterion may correspond to at least one of the characteristic change data of the ablation site, for example, the size of the ablation site needs to be reduced to a preset value (e.g., reduced to 0.01 cm), or the area of ​​the ablation site needs to be reduced (e.g., reduced by 0.1 cm). The type of change criterion and the parameter value may be set according to the user's customized operation.

[0060] Optionally, in another embodiment of the present disclosure, the method further includes setting an average value of the characteristic change data of all ablation sites to the preset change criterion.

[0061] That is, when the characteristic change of the ablation site reaches the average change level of all the ablation sites, such as the area change reaches the average area change level, the size change reaches the average size change level, etc., the corresponding parameter data is retained. In this way, when the radiofrequency ablation system executes the ablation task, all the parameter data set in each device in the system are treated as one set of parameter data, and each set of parameter data is selected using the ablation effect, thereby further improving the referability of the analysis result.

[0062] Optionally, in another embodiment of the present disclosure, the method further includes analyzing a normal distribution feature of the feature change data of the ablation site, and setting a normal distribution feature value obtained by the analysis as the preset change standard.

[0063] Specifically, the normal distribution has a bell-shaped curve with a high probability density in the middle and small probability density on both sides. The shape is determined by the parameters μ and σ. If a continuous random variable conforms to a normal distribution with a mean value of μ and a standard deviation of σ, it can be expressed as X~N(μ,σ^2). Here, μ refers to the center position of the curve, and the maximum probability density generally appears near the mean value. By analyzing the normal distribution characteristics of the characteristic change data of the ablation site, the characteristic value with the highest probability that the ablation site shows normal changes can be obtained, and the characteristic value can be set as the change criterion of the preset, further improving the referability of the analysis results.

[0064] Step S304: Establish association relationships between different types of ablation targets and their corresponding classification conditions and at least one set of respectively matching target parameter data, and output and store the established association relationships in a database.

[0065] In a practical application example, the analysis results can be shown in Table 2 below. JPEG0007654798000002.jpg105170

[0066] Of course, the above Table 2 is just an example, and in actual applications, the associated data table may have more or less content and may be expressed in other forms. When storing each associated relationship in the present disclosure in a database, in addition to the associated relationship, the corresponding different types of ablation subjects, classification conditions, and target parameter data may also be stored in the database, or the database only stores the storage link of the above data, and the source data of the above data is stored in another database server, so as to reduce the volume of the database and improve the query speed.

[0067] Optionally, in another embodiment of the present disclosure, the method further comprises: Monitoring physiological changes of each ablation subject by an intelligent physical examination device to obtain physiological change data of each ablation subject after execution of each associated ablation task; and periodically filtering the target parameter data in the database in response to the physiological change data.

[0068] Optionally, the physiological change data may include, but are not limited to, at least one of body temperature, blood pressure, blood glucose level, heart rate, blood lipids, lung capacity, and blood oxygen saturation. The intelligent physical examination device may be a home physical examination device, or may be a physical examination device installed in various medical places. Specifically, for example, smart watches, smart bracelets and other smart wearable devices with blood pressure, heart rate, and body temperature measurement functions, smart blood glucose meters, smart blood pressure monitors, smart thermometers, etc. with data transmission functions.

[0069] Furthermore, periodically selecting the target parameter data in the database in response to the physiological change data can be specifically periodically acquiring a physiological abnormality change rate according to the physiological change data; and adjusting the storage of the target parameter data in the database in response to the physiological abnormality rate of change and the preset ratio.

[0070] Here, the physiological abnormal change rate is the ratio of the number of ablation subjects of the same type whose physiological data changes exceed a preset range after the associated ablation task is performed to the total number of ablation subjects of that type. For example, for type 1 ablation subjects, it is the ratio of the number of people whose blood pressure values ​​continue to exceed a preset value for a preset duration or more after the associated ablation task is performed to the total number of type 1 ablation subjects.

[0071] Adjusting the storage state of the target parameter data in the database specifically includes marking, in the database, parameter data among the target parameter data having a physiological abnormal change rate greater than the preset ratio as hidden, locked, or frozen, so that the parameter data cannot be queried by external devices, and marking, in the database, parameter data among the target parameter data having a physiological abnormal change rate equal to or less than the preset ratio as normal, so that the parameter data can be queried by external devices.

[0072] In this way, by using an intelligent physical examination device to monitor physiological changes in each ablation subject and dynamically adjusting the storage state of the ablation parameter setting scheme stored in the database according to the monitoring data, the final ablation parameter setting scheme becomes more target-oriented and accurate as the amount of data increases.

[0073] Step S305: When the parameter analysis command is received, the first target data is obtained.

[0074] Step S306: According to the first target data and the preset first target classification condition, a classification of the ablation target designated by the parameter analysis command is determined.

[0075] Step S307: Search the database for at least one set of parameter data matching the determined belonging classification as reference data.

[0076] Step S308: Analyze the feasibility of the parameter data to be set according to the reference data, and output the analysis result.

[0077] Specifically, a parameter analysis command can be sent from another terminal to the server according to the user's operation. When the other terminal sends the parameter analysis command to the server, the first target data or the storage link of the first target data input by the user can be sent to the server.

[0078] Here, the first target data includes characteristic data of the ablation target specified by the parameter analysis command, characteristic data of the ablation site, explanatory data of the ablation task to be performed, and parameter data to be set.

[0079] The classification of the ablation target specified by the parameter analysis command is determined in accordance with the characteristic data of the ablation target, the characteristic data of the ablation site, the description data of the ablation task to be performed, the parameter data to be set, the first target classification conditions, and the first target data and the first target classification conditions, as described above. This can be done by referring to the relevant explanations of steps S301 and S302, and will not be repeated here.

[0080] Specifically, performing a feasibility analysis on the parameter data to be set according to the reference data includes comparing the reference data with the parameter data to be set to determine whether the difference between the two is smaller than a preset difference range, and if the difference is smaller than the preset difference range, determining that the corresponding parameter data to be set is feasible, but if not, determining that the corresponding parameter data to be set is not feasible.

[0081] The analysis result includes explanatory information as to whether the parameter data to be set is feasible or not, and may also include reference data corresponding to the parameter data to be set that is not feasible.

[0082] For example, assuming that the parameter data to be set includes the irrigation rate and irrigation duration of the injection pump, and the power and ablation duration of the radiofrequency ablation catheter, if the reference data obtained by query shows that the irrigation rate and irrigation duration of the injection pump in the parameter data to be set are feasible, but the power and ablation duration of the radiofrequency ablation catheter are not feasible, the analysis result may include the reference data of the power and ablation duration of the radiofrequency ablation catheter in addition to explanatory information on whether the data is feasible.

[0083] In this way, by using the reference data in the database to analyze the setting parameter data of the ablation task to be performed, the accuracy of the parameter settings of the radiofrequency ablation system can be improved to achieve a better ablation effect.

[0084] Step S309: When the parameter query command is received, the second target data is obtained.

[0085] Step S310: According to the second target data and the preset second target classification condition, a classification of the ablation target designated by the parameter query command is determined.

[0086] Step S311: Search the database for at least one set of parameter data matching the determined belonging classification, and output the search results.

[0087] Specifically, the second target data includes characteristic data of the ablation target and the ablation site specified by the parameter query command, and description data of the ablation task to be performed.

[0088] For details on determining the classification of the ablation target specified by the parameter query command in accordance with the characteristic data of the ablation target and the ablation site specified by the above parameter query command, the description data of the ablation task to be performed, the second target classification conditions, and the second target data and the second target classification conditions, please refer to the relevant explanations of steps S301 and S302, and therefore the description will not be repeated here.

[0089] Optionally, the first target classification condition and the second target classification condition correspond to the first target data and the second target data, respectively. During the parameter analysis and parameter inquiry, the first and second target classification conditions and the type range of each parameter in the target data used may be equal to or less than the type range of the classification conditions and the radiofrequency ablation data used in steps S301 and S302. For example, in steps S301 and S302, the classification conditions may be age group, sex, and area of ​​the ablation site, and the first classification target condition may be only age group.

[0090] In this way, by utilizing the above database, a service of querying ablation parameter setting schemes based on target classification conditions and target data can be provided to users so that they can fully utilize the radiofrequency ablation data.

[0091] Each embodiment of the present disclosure obtains radiofrequency ablation data of each ablation task performed within a preset duration, and obtains ablation parameter setting schemes that respectively match different types of ablation targets by analyzing the obtained radiofrequency ablation data, thereby realizing automatic analysis of radiofrequency ablation data based on big data, and querying and analyzing the ablation parameter setting schemes based on the automatic analysis, thereby improving the utilization rate of radiofrequency ablation data, and since the analysis results are based on a large amount of data, they have high referability.

[0092] Referring to Fig. 4, a schematic diagram of the structure of a radiofrequency ablation data processing device provided by an embodiment of the present disclosure is shown. For the purpose of explanation, only parts related to the embodiment of the present disclosure are shown. The device may be a computer device or a software module installed in a computer device. As shown in Fig. 4, the device includes an acquisition module 401, an analysis module 402, and an output module 403.

[0093] The acquisition module 401 is for acquiring radiofrequency ablation data including, for each ablation task performed within a preset duration, parameter data set when each device in the radiofrequency ablation system performed each ablation task, and characteristic data of the ablation target associated with each ablation task.

[0094] The analysis module 402 is for analyzing the radiofrequency ablation data to obtain ablation parameter setting schemes respectively matching different types of ablation subjects.

[0095] The output module 403 is for outputting the ablation parameter setting scheme.

[0096] The specific process by which each of the above modules achieves its respective functions can be referred to the relevant contents in the embodiments shown in FIG. 2 and FIG. 3, so it will not be repeated here.

[0097] Each embodiment of the present disclosure acquires radiofrequency ablation data of each ablation task performed within a preset duration, and analyzes the acquired radiofrequency ablation data to acquire ablation parameter setting schemes that respectively match different types of ablation subjects. By realizing automatic analysis of radiofrequency ablation data based on big data and recommendation of ablation parameter setting schemes based on the automatic analysis, the utilization rate of radiofrequency ablation data is improved, and since the analysis results are based on a large amount of data, they have high referability.

[0098] Referring to Figure 5, a schematic diagram of the structure of a radiofrequency ablation data processing device provided by another embodiment of the present disclosure is shown. For the purpose of explanation, only parts related to the embodiment of the present disclosure are shown. The device may be a computer device or a software module installed in a computer device. As shown in Figure 5, the differences from the embodiment shown in Figure 4 are as follows:

[0099] Furthermore, the radiofrequency ablation data includes explanatory data for each of the ablation tasks and characteristic change data for the ablation site of each of the ablation subjects.

[0100] The analysis module 402 is further for classifying the ablation target according to the feature data of the ablation target, the description data of the ablation task, the feature change data of the ablation site, and preset classification conditions, for analyzing the parameter data according to the classification result and the feature change data of the ablation site, and for obtaining at least one set of target parameter data matching the different types of the ablation target as the ablation parameter setting scheme, and for associating the different types of the ablation target with their corresponding classification conditions and at least one set of target parameter data matching them.

[0101] The output module 403 is further for outputting the established association relationship for storage in a database.

[0102] Further, the analysis module 402 is also for classifying the parameter data according to the classification result, obtaining at least one set of candidate parameter data respectively corresponding to different types of the ablation subjects, and eliminating, from the candidate parameter data, parameter data whose characteristic change data of the ablation site does not meet the preset change criterion, and obtaining at least one set of target parameter data as the ablation parameter setting scheme.

[0103] Furthermore, the apparatus further comprises: The apparatus further includes a setting module 501 for setting the average value of the characteristic change data of the ablation site as the preset change criterion.

[0104] Furthermore, the setting module 501 is also for analyzing the normal distribution feature of the feature change data of the ablation site, and setting the normal distribution feature value obtained by the analysis as the change standard of the preset.

[0105] Furthermore, the apparatus further comprises: a monitoring module 502 for monitoring physiological changes of each of the ablation subjects by an intelligent physical examination device and obtaining physiological change data of each of the ablation subjects after execution of each associated ablation task; A filtering module 503 is provided for periodically filtering said target parameter data in the database in response to the physiological change data.

[0106] Furthermore, the selection module 503 is also for periodically obtaining a physiological abnormality change rate according to the physiological change data including at least one of body temperature, blood pressure, blood glucose level, heart rate, blood lipids, lung capacity and blood oxygen saturation, and for adjusting the storage state of the target parameter data in the database according to the ratio of the physiological change data to a preset.

[0107] Furthermore, the apparatus further comprises: a receiving module 504 for receiving the identification information of the ablation subject, the imaging time, and the imaging data of the ablation subject transmitted by the medical imaging device; an identification module 505 for performing image recognition on the contrast data, generating feature data of the ablation site of the ablation target according to the recognition result, associating identification information of the ablation target and the contrast time with the feature data of the ablation site, and storing the association in an ablation site information database by an output module 403; and an acquisition module 401 for querying the ablation site information database to acquire characteristic data of the ablation site of each of the ablation targets and the corresponding contrast enhancement time, and acquiring characteristic change data of the ablation site of each of the ablation targets according to the contrast enhancement time.

[0108] Furthermore, the acquisition module 401 is also for acquiring first target data when receiving a parameter analysis command, the first target data including characteristic data of the ablation target specified by the parameter analysis command, characteristic data of the ablation site, description data of the ablation task to be performed, and parameter data to be set.

[0109] The apparatus further comprises: a first determination module 506 for determining a classification of the ablation target designated by the parameter analysis command according to the first target data and a preset first target classification condition; a first search module 507 for searching the database for at least one set of parameter data matching the determined classification as reference data; and a feasibility analysis module 508 for performing a feasibility analysis on the parameter data to be set according to the reference data and outputting the analysis result by the output module 403.

[0110] Furthermore, the acquisition module 401 is also for acquiring second target data when receiving a parameter query command, the second target data including characteristic data of the ablation target and the ablation site specified by the parameter query command, and description data of the ablation task to be performed.

[0111] The apparatus further comprises: a second determination module 509 for determining a classification of the ablation target designated by the parameter query command according to the second target data and a preset second target classification condition; and a second search module 510 for searching the database for at least one set of parameter data matching the determined classification and outputting a search result.

[0112] Further, the characteristic data includes at least one of the gender, the disease before the ablation task is performed, and the survival time after the ablation task is performed, and the age of the ablation target.

[0113] The description data for the ablation task includes a duration of the ablation task and a description data for an ablation stage corresponding to the ablation task.

[0114] The characteristic change data of the ablation site includes change data of at least one of the size, shape, and area of ​​the ablation site before and after each associated ablation task is performed.

[0115] The specific process by which each of the above modules achieves its respective functions can be referred to the relevant contents in the embodiments shown in FIG. 2 and FIG. 3, so it will not be repeated here.

[0116] Each embodiment of the present disclosure acquires radiofrequency ablation data of each ablation task performed within a preset duration, and analyzes the acquired radiofrequency ablation data to acquire ablation parameter setting schemes that respectively match different types of ablation subjects. By realizing automatic analysis of radiofrequency ablation data based on big data and recommendation of ablation parameter setting schemes based on the automatic analysis, the utilization rate of radiofrequency ablation data is improved, and since the analysis results are based on a large amount of data, they have high referability.

[0117] Referring to Fig. 6, it is a schematic diagram of the hardware structure of data provided by one embodiment of the present disclosure. As shown in Fig. 6, the server 60 includes a network interface 61, a processor 62, a memory 63, a computer program 64 stored in the memory 63 and executed in the processor 62, and a system bus 65. The system bus 65 is for connecting the network interface 61, the processor 62, and the memory 63. When the processor 62 executes the computer program 64, steps in each embodiment of the radiofrequency ablation data processing method described above are performed, such as steps S201 to S203 shown in Fig. 2.

[0118] The network interface 61 is for communicating with other servers.

[0119] Illustratively, the processor 62 may be a Central Processing Unit (CPU) or other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. A general purpose processor may be a microprocessor, the processor may be any conventional processor, etc.

[0120] Exemplarily, the memory 63 may be, for example, a hard disk drive memory, a non-transient memory, a non-volatile memory (such as a flash memory or a memory with electronically programmable restrictions removed to form a solid-state drive), a volatile memory (such as a static or dynamic random access memory), etc., and is not limited in the embodiment of the present disclosure. The memory 63 may include both an internal memory unit of the server 60 and an external storage device. The memory 63 is for storing computer programs and other programs and data required by the server 60. The memory 63 is also for temporarily storing data that has been output or is to be output.

[0121] Exemplarily, the computer program 64 can be divided into one or more modules / units, which are stored in the memory 63 and executed by the processor 62 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments that can realize a specific function. The instruction segments are for explaining the execution process of the computer program 64 in the server 60. For example, the computer program 64 can be divided into an acquisition module 401, an analysis module 402 and an output module 403, and the functions of each unit are as follows:

[0122] The acquisition module 401 is for acquiring radiofrequency ablation data including, for each ablation task performed within a preset duration, parameter data set when each device in the radiofrequency ablation system performed each ablation task, and characteristic data of the ablation target associated with each ablation task.

[0123] The analysis module 402 is for analyzing the radiofrequency ablation data to obtain ablation parameter setting schemes respectively matching different types of ablation subjects.

[0124] The output module 403 is for outputting the ablation parameter setting scheme.

[0125] The specific processes by which the above functional modules achieve their respective functions can be referred to the relevant contents in the embodiments shown in FIG. 4 and FIG. 5, and will not be repeated here.

[0126] Additionally, the memory 63 also stores device drivers, which may be network and interface drivers.

[0127] Those skilled in the art can understand that Fig. 6 is merely an example of the present server 60, does not constitute a limitation on the server 60, and an actual application may include more or fewer components than those shown in the figure, or a combination of specific components, or different components. For example, the server 60 may include input / output devices (such as a keyboard, a microphone, a camera, a speaker, a display, etc.).

[0128] Furthermore, the embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which may be located in the server in the above-mentioned embodiment, storing a computer program in the non-transitory computer-readable storage medium, which, when executed by a processor, performs the method of radiofrequency ablation data processing described in the above-mentioned embodiment shown in FIG.

[0129] In the above embodiments, the description of each embodiment has its own emphasis, so that for parts that are not described or explained in detail in one embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will understand that each exemplary module / unit and algorithm step described with reference to the embodiments disclosed herein can be realized by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution. Professional engineers can use different methods for each specific application to realize the described functions, but such realization should not be considered as going beyond the scope of the present disclosure.

[0131] In the embodiments provided in the present disclosure, it should be understood that the disclosed device / terminal and method may be implemented in other ways. For example, the above device / terminal embodiments are merely exemplary. For example, the division of modules or units is merely a division of logical functions, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be indirect couplings or communication connections via some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

[0133] In addition, each functional unit according to each embodiment of the present disclosure may be integrated into one processing unit, each unit may exist physically alone, or two or more units may be integrated into one unit. The integrated units can be realized in the form of hardware or software functional units.

[0134] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also be completed by a computer program that performs all or part of the flow of the processing in the above-mentioned method in the embodiment and instructs related hardware. The computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of each of the above-mentioned method embodiments are performed. Here, the computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include any entity or device that can carry computer program code, a recording medium, a flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a communication signal, a software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of the law and patent practice in the jurisdiction. For example, in some jurisdictions, according to the law and patent practice, computer-readable media does not include electrical carrier signals and telecommunications signals.

[0135] It should be noted that the above embodiments are not limiting, but are only used to describe the technical solutions of the present disclosure. Although the present disclosure has been described in detail with reference to the above embodiments, those skilled in the art can still modify the technical solutions described in the above embodiments or perform equivalent replacements for some of the technical features, and these modifications or replacements do not cause the essence of the corresponding technical solutions to depart from the concept and scope of each embodiment of the present invention, and are within the scope of the claims of the present invention.

Claims

1. 1. A method of radiofrequency ablation data processing applied to a computer device, the method comprising: acquiring radiofrequency ablation data, for each ablation task performed within a preset duration, the data including parameter data set when each device in the radiofrequency ablation system performed each ablation task and characteristic data of an ablation target associated with each ablation task; Analyzing the radiofrequency ablation data to obtain and output ablation parameter setting schemes respectively matching different types of ablation targets; The radiofrequency ablation data further includes description data for each of the ablation tasks and characteristic change data for the ablation site of each of the ablation subjects; Analyzing the radiofrequency ablation data to obtain and output ablation parameter setting schemes respectively matching different types of ablation targets; Classifying the ablation target according to feature data of the ablation target, description data of the ablation task, feature change data of the ablation site, and preset classification conditions; Analyzing the parameter data according to the classification result and the characteristic change data of the ablation site, and obtaining at least one set of target parameter data respectively matching different types of the ablation targets as the ablation parameter setting scheme; and establishing an association relationship between different types of the ablation subjects and the classification conditions respectively corresponding to the at least one set of target parameter data respectively matching the different types of the ablation subjects, and outputting and storing the established association relationship in a database. When receiving a parameter analysis command, acquiring first target data including characteristic data of an ablation target and an ablation site designated by the parameter analysis command, description data of an ablation task to be performed, and parameter data to be set; Determining a classification of the ablation target designated by the parameter analysis command according to the first target data and a preset first target classification condition; Searching the database for at least one set of parameter data matching the determined classification as reference data; A method for processing radiofrequency ablation data, comprising: performing a feasibility analysis on the parameter data to be set according to the reference data; and outputting an analysis result.

2. Analyzing parameter data according to the classification result and the characteristic change data of the ablation site, and obtaining at least one set of target parameter data respectively matching different types of ablation targets as an ablation parameter setting scheme; Classifying the parameter data according to the classification result to obtain at least one set of candidate parameter data each corresponding to a different type of the ablation target; and eliminating, from the candidate parameter data, parameter data in which characteristic change data of the ablation site does not satisfy a preset change criterion, to obtain the at least one set of target parameter data as the ablation parameter setting scheme.

3. The method further comprises: The method of claim 2 , further comprising setting a mean value of the ablation site characteristic change data to the preset change criterion.

4. The method further comprises: The method according to claim 2 , further comprising: analyzing a normal distribution feature of the characteristic change data of the ablation site; and setting a normal distribution feature value obtained by the analysis as the preset change standard.

5. The method further comprises: Monitoring physiological changes of each of the ablation subjects by an intelligent physical examination device to obtain physiological change data of each of the ablation subjects after execution of each associated ablation task; and periodically filtering said target parameter data in said database in response to said physiological change data.

6. periodically selecting the target parameter data in the database in response to the physiological change data; Periodically acquiring a physiological abnormality change rate according to the physiological change data including at least one of body temperature, blood pressure, blood glucose level, heart rate, blood lipids, lung capacity, and blood oxygen saturation; 6. The method of claim 5, further comprising adjusting storage of said target parameter data in said database in response to said physiological abnormality rate of change and a preset ratio.

7. The method further comprises: receiving identification information of the ablation subject, an imaging time, and imaging data of the ablation subject transmitted by a medical imaging device; performing image recognition on the contrast data and generating feature data of the ablation site of the ablation target according to a recognition result; Associating the identification information of the ablation target and the contrast time with characteristic data of the ablation site, and storing the association in an ablation site information database; Acquiring characteristic change data of the ablation site of each of the ablation subjects includes Querying the ablation site information database to obtain characteristic data of the ablation site of each of the ablation targets and the corresponding contrast enhancement time; The method according to claim 1 , further comprising acquiring characteristic change data of the ablation site of each of the ablation targets according to the contrast enhancement time.

8. The method further comprises: When receiving a parameter query command, acquiring second target data including characteristic data of the ablation target and the ablation site specified by the parameter query command and description data of the ablation task to be performed; Determining a classification of the ablation target designated by the parameter query command according to the second target data and a preset second target classification condition; and searching in said database for and outputting at least one set of parameter data matching the determined classification membership.

9. The characteristic data includes at least one of the gender, the disease before the ablation task is performed, and the survival time after the ablation task is performed, and the age of the ablation target; the ablation task description data includes an execution time of the ablation task and ablation stage description data corresponding to the ablation task; The method according to any one of claims 1 to 8, characterized in that the characteristic change data of the ablation site includes change data of at least one of the size, shape, and area of ​​the ablation site before and after the associated ablation task is performed.

10. A radiofrequency ablation data processing device, comprising: an acquisition module for acquiring radiofrequency ablation data, the radiofrequency ablation data including parameter data set when each device in the radiofrequency ablation system executed each ablation task for each ablation task executed within a preset duration, and characteristic data of an ablation target associated with each ablation task; an analysis module for analyzing the radiofrequency ablation data and obtaining ablation parameter setting schemes respectively matching different types of ablation subjects; and an output module for outputting the ablation parameter setting scheme. The radiofrequency ablation data further includes description data for each of the ablation tasks and characteristic change data for the ablation site of each of the ablation subjects; Specifically, the analysis module includes: Classifying the ablation target according to the characteristic data of the ablation target, the description data of the ablation task, the characteristic change data of the ablation site, and preset classification conditions; and analyzing the parameter data according to the classification result and the characteristic change data of the ablation site, and obtaining at least one set of target parameter data respectively matching different types of the ablation targets as the ablation parameter setting scheme; Specifically, the output module includes: and to establish association relationships between different types of the ablation targets and the classification conditions respectively corresponding thereto and the at least one set of target parameter data respectively matching therewith, and output the established association relationships to store them in a database; The acquisition module further comprises: and to obtain first target data, when receiving a parameter analysis command, including characteristic data of an ablation target and an ablation site designated by the parameter analysis command, description data of an ablation task to be performed, and parameter data to be set; The apparatus further comprises: a first determination module for determining a classification of the ablation target designated by the parameter analysis command according to the first target data and a preset first target classification condition; a first search module for searching the database for at least one set of parameter data matching the determined classification as reference data; A radiofrequency ablation data processing device, comprising: a feasibility analysis module for performing a feasibility analysis on the parameter data to be set according to the reference data and outputting the analysis results.

11. A server, It has memory and a processor. The memory stores executable program code; 10. A server comprising: a processor coupled to the memory for calling the executable program code stored in the memory to execute the method for radiofrequency ablation data processing according to any one of claims 1 to 9.

12. 10. A non-transitory computer readable storage medium having a computer program stored thereon, the computer program being adapted, when executed by a processor, to perform a method for radiofrequency ablation data processing according to any one of claims 1 to 9.

Images